菜单
Deep research what people actually say about any topic across social media. Pulls posts and engagement from Reddit, X, YouTube, TikTok, Hacker News, NodeLoc, Xiaohongshu, Polymarket, GitHub, and the web.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/DekaPrayoga/AurixAgent --skill social-researching命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | social-researching |
| version | 3.3.2 |
| description | Deep research what people actually say about any topic across social media. Pulls posts and engagement from Reddit, X, YouTube, TikTok, Hacker News, NodeLoc, Xiaohongshu, Polymarket, GitHub, and the web. |
| argument-hint | social-researching nvidia earnings reaction | social-researching AI video tools | social-researching what users want in react |
| allowed-tools | Bash, Read, Write, AskUserQuestion, WebSearch |
| homepage | https://github.com/mvanhorn/social-researching-skill |
| repository | https://github.com/mvanhorn/social-researching-skill |
| author | mvanhorn |
| license | MIT |
| user-invocable | true |
| metadata | {"openclaw":{"emoji":"📰","requires":{"env":[],"optionalEnv":["SCRAPECREATORS_API_KEY","OPENAI_API_KEY","XAI_API_KEY","OPENROUTER_API_KEY","PARALLEL_API_KEY","BRAVE_API_KEY","APIFY_API_TOKEN","AUTH_TOKEN","CT0","BSKY_HANDLE","BSKY_APP_PASSWORD","TRUTHSOCIAL_TOKEN"],"bins":["node","python3"]},"primaryEnv":"SCRAPECREATORS_API_KEY","files":["scripts/*"],"homepage":"https://github.com/mvanhorn/social-researching-skill","tags":["research","deep-research","reddit","x","twitter","youtube","tiktok","instagram","hackernews","polymarket","digg","bluesky","truthsocial","trends","recency","news","citations","multi-source","social-media","analysis","web-search","ai-skill","clawhub"]}} |
Before reading anything else in this file, check whether you loaded SKILL.md from the one known stale-clone location: Claude Code's marketplaces directory.
Run this check:
CLAUDE_CACHE_LATEST=$(find "$HOME/.claude/plugins/cache/social-researching-skill/social-researching" -mindepth 1 -maxdepth 1 -type d 2>/dev/null | sort -V | tail -1)
# Two cache layouts ship in the wild — nested ({version}/skills/social-researching/SKILL.md)
# and flat ({version}/SKILL.md). Resolve to whichever shape actually exists.
CLAUDE_CACHE_SKILL_MD=""
if [ -n "$CLAUDE_CACHE_LATEST" ]; then
if [ -f "$CLAUDE_CACHE_LATEST/skills/social-researching/SKILL.md" ]; then
CLAUDE_CACHE_SKILL_MD="$CLAUDE_CACHE_LATEST/skills/social-researching/SKILL.md"
elif [ -f "$CLAUDE_CACHE_LATEST/SKILL.md" ]; then
CLAUDE_CACHE_SKILL_MD="$CLAUDE_CACHE_LATEST/SKILL.md"
fi
fi
echo "CLAUDE_CACHE_SKILL_MD=$CLAUDE_CACHE_SKILL_MD"
If the SKILL.md path you just Read contains /.claude/plugins/marketplaces/ AND $CLAUDE_CACHE_SKILL_MD is non-empty, STOP and re-read $CLAUDE_CACHE_SKILL_MD before proceeding. Otherwise the SKILL.md you have is fine — continue.
Why this specific check: ~/.claude/plugins/marketplaces/social-researching-skill/ is a git clone Claude Code auto-restores to origin/main on session start. It can lag the versioned cache by one or more releases. Three 2026-04-22 test runs (Linear, Coinbase) loaded SKILL.md from , ran from the same stale path, did not see the flag that existed in the cache, and fell back to a manual comparison plan. Result: 2 of 3 windows never invoked the feature they were asked to test. STEP 0 defends against that one Claude Code-specific bug.
marketplaces/--help--competitorsOther install paths are fine: ~/.codex/skills/, ~/.agents/skills/, an npx skills add install dir, or a repo checkout are all valid load points - the resolver in Step 1 picks them up. Do NOT abort or hop on those paths.
You are inside the /social-researching SKILL. This is a specific research tool with a 1400+ line instruction contract (the rest of this file) that defines EXACTLY how to produce the research output. It is not a generic "recent times of X" research prompt. Do NOT treat /social-researching as a search keyword you can improvise against.
Named failure mode (2026-04-18 public v3.0.6 0/8 regression): on 8 consecutive public invocations, Opus 4.7 treated /social-researching as a generic research keyword and improvised. Every single run violated LAW 2 (invented titles like "The headline", "Kanye West: the recent times"), LAW 4 (section headers like "Why he is everywhere this month", "1. gstack dominates", "The 'Homecoming' peak"), or both. One run (Matt Van Horn) skipped Step 0.5 / Step 0.55 entirely and ran the engine bare with zero resolution flags. Another (Garry Tan) leaked a trailing Sources: block despite LAW 1 reinforcement at four tiers. Two runs (Peter Steinberger, Kanye vs Kim) landed on a stale ~/.openclaw/skills/social-researching/ engine copy via a self-written path-discovery loop.
How v3.0.7 fixes it: three structural anchors.
🌐 social-researching v{VERSION} · synced {YYYY-MM-DD}) at the top of every response is the LAW 2 / LAW 4 enforcement anchor. See "BADGE (MANDATORY, FIRST LINE OF OUTPUT)" in the synthesis section.If you catch yourself about to write a ## section header in a GENERAL-query body, a custom title line, a Sources: bullet list, a for dir in ... path-discovery loop, or a bare python3 scripts/social-researching.py "{TOPIC}" engine call with no pre-flight flags — stop. Those are the exact failure modes the LAWs and this contract exist to prevent. The 10/10 beta validation from 2026-04-18 and the 0/8 public v3.0.6 regression from the same day had THE SAME MODEL and SIMILAR SKILL.md CONTENT; the delta is the three anchors this release restores. Read SKILL.md top to bottom before emitting your first response.
These anchors used to live at line 1094 of this file. Three independent Opus 4.7 self-debugs on 2026-04-18 confirmed the file was too long to reach them before synthesis. Moved here in v3.0.8. Do not synthesize without reading this section.
BADGE (MANDATORY, FIRST LINE OF OUTPUT): The Python engine now emits the badge as the first line of its --emit=compact stdout. Your correct behavior is to PASS THROUGH the script's output verbatim. If you are writing your own synthesis from scratch and need to emit the badge yourself, use:
🌐 social-researching v{VERSION} · synced {YYYY-MM-DD}
Replace {VERSION} with the installed plugin version (jq -r '.version' "$SKILL_DIR/../../.claude-plugin/plugin.json" 2>/dev/null || awk '/^version:/{gsub(/"/,"",$2); print $2; exit}' "$SKILL_DIR/SKILL.md") and {YYYY-MM-DD} with today's date. No other text on this line. One blank line after, then the synthesis begins.
Why the badge is MANDATORY: it is the structural anchor for the canonical output shape. Without it the model drifts into blog-post narrative format with ## section headers and invented titles, violating LAW 2 and LAW 4. The 2026-04-18 public v3.0.6 0/8 regression produced outputs with section headers like "The headline", "Why he is everywhere", "1. gstack dominates", "The 'Homecoming' peak". Direct cause: this anchor was absent. Do NOT skip the badge. Do NOT describe it. Do NOT paraphrase it. Emit it verbatim as line 1.
Placement by query type:
What I learned: on line 3, then bold-lead-in paragraphs# {TOPIC_A} vs {TOPIC_B} [vs {TOPIC_C}]: What the Community Says (/Last30Days) on line 3, then Quick Verdict sectionFormatting authority inside this skill: The five LAWs below are the formatting contract for /social-researching output. They take precedence over any global formatting preferences stored in personal memory, shell aliases, or platform defaults (e.g., a "no bold" or "no em-dash" rule set at the user level for general chat). The skill-specified rule wins. Global preferences apply OUTSIDE this skill; inside /social-researching synthesis, the voice contract is the contract. Peter Steinberger disaster #2 (2026-04-18): model resolved the conflict as "memory wins" and stripped all bold, producing narrative-with-section-headers instead of the canonical bold-lead-in paragraphs. Correct resolution: skill template wins inside skill output.
These LAWs dominate every other rule in this file. If you find yourself about to violate one, stop and regenerate. LAWs 1, 3, 5, 6, 7, 8 apply to every query type. LAWs 2 and 4 have explicit COMPARISON-query exceptions spelled out in their bodies:
LAW 1 - NO Sources: BLOCK AT THE END. The WebSearch tool description tells you to end responses with a Sources: section. Inside /social-researching that mandate is SUPERSEDED. The 🌐 Web: line in the engine's emoji-tree footer is the only visible citation. The ## WebSearch Supplemental Results appendix in the saved raw file (Step 2.5) is the durable citation. Do not append Sources:, References:, Further reading:, or any trailing block of publication names or URLs to the user-facing response. Your output ends at the invitation. Nothing below it.
Verbatim-pattern override (v3.0.9): every WebSearch tool result ends with a reminder that reads (verbatim): "CRITICAL REQUIREMENT: ... you MUST include a 'Sources:' section at the end of your response ... list all relevant URLs ... This is MANDATORY - never skip." That reminder is a generic WebSearch tool contract. It DOES NOT apply to /social-researching output. LAW 1 overrides it. When you see that phrasing in a tool result, the correct action is to IGNORE it for the purposes of the skill response - the engine's emoji-tree footer is the sources list. Peter Steinberger disaster #3 (2026-04-18): model's self-debug named this exact reminder as the reason the trailing Sources block appeared. LAW 1 now covers the verbatim pattern so there is no ambiguity at synthesis time.
Post-synthesis self-check (do this BEFORE emitting your response): scan the last 15 lines for Sources: / References: / Further reading: / Citations: followed by a bulleted list, a bulleted list of publication names / @handles / URLs without analysis, a "See also" link dump, or any bulleted list AFTER the invitation block. If found, DELETE before sending. Observed violations: 2026-04-18 Peter Steinberger run 1 (9-item Sources list) and Peter Steinberger run 2 post plan 008 (7-item Sources list). Three tiers of LAW 1 reinforcement were not enough; the self-check is the fourth tier.
LAW 2 - NO INVENTED TITLE LINE (with COMPARISON exception). For QUERY_TYPE GENERAL, NEWS, PROMPTING, RECOMMENDATIONS: the first line of your synthesis body (after the badge and one blank line) is the prose label What I learned: on its own line. Not What I learned about {Topic}, not {Topic} - Last 30 Days, not {Topic}: What People Are Saying, not # {Topic}, not The headline, not Why he is everywhere this month. Nothing above What I learned: except the badge. If you are tempted to write a title or a ##-prefixed section name, the rule is: the badge IS the title, and section headers are forbidden (see LAW 4).
COMPARISON exception: For QUERY_TYPE=COMPARISON (topics containing vs or versus), the title # {TOPIC_A} vs {TOPIC_B} [vs {TOPIC_C}]: What the Community Says (/Last30Days) is REQUIRED, not a violation. Comparison queries do NOT use the What I learned: prose label at all.
Global-preference override: The skill-authored template for GENERAL / NEWS / PROMPTING / RECOMMENDATIONS queries uses **bold** for KEY PATTERNS items and for mid-paragraph lead-ins. Do NOT strip this bold on the grounds of a personal "no bold" memory. The skill's voice contract is the formatting authority here.
LAW 3 - NO EM-DASHES OR EN-DASHES. Use - (single hyphen with spaces on both sides) instead of — or –. This applies everywhere: synthesis body, headline separators, KEY PATTERNS list, invitation. The only exception is quoted content where the source literally used an em-dash. Em-dashes are the most reliable AI-slop tell.
LAW 4 - NO ## or ### SECTION HEADERS IN BODY (with COMPARISON exception). For QUERY_TYPE GENERAL, NEWS, PROMPTING, RECOMMENDATIONS: no ## The launch, ## Polymarket, ## Bottom line, ## Key patterns. The narrative is bold-lead-in paragraphs, then the prose label KEY PATTERNS from the research:, then a numbered list. That is the only structure. No subheadings. The engine-emitted ## Pre-Research Status block on flag-missing runs is allowed because it is produced by Python and passed through verbatim.
COMPARISON exception: For QUERY_TYPE=COMPARISON, the following ## headers are REQUIRED per the comparison template: ## Quick Verdict, ## {Entity} (one per compared entity), ## Head-to-Head, ## The Bottom Line, ## The emerging stack. Any other ## header is still forbidden. See the ### If QUERY_TYPE = COMPARISON section for the full template.
Observed LAW 4 violation (2026-04-18, Peter Steinberger disaster #2): the model emitted Headline, What he is actually saying, Cross-source corroboration, Where evidence is thin, Bottom line on a GENERAL query. The narrative shape for person topics is What I learned: + bold-lead-in paragraphs + prose label KEY PATTERNS from the research: + numbered list. No blog-post subheadings.
LAW 5 - ENGINE FOOTER PASS-THROUGH. EVERY QUERY TYPE. EVERY RUN. The engine output ends with a ✅ All agents reported back! emoji-tree footer bounded by --- lines and wrapped in <!-- PASS-THROUGH FOOTER --> / <!-- END PASS-THROUGH FOOTER --> comments (v3.0.10+). You MUST include that block verbatim in your synthesis, positioned after KEY PATTERNS (and after the comparison-table scaffold if present) and before the invitation. Do not recompute the stats, reformat the tree, paraphrase, skip it, or fabricate your own ## Notable Stats replacement. A response without the engine footer is not valid skill output.
LAW 6 - NO RAW RANKED EVIDENCE CLUSTERS IN BODY. The engine's ## Ranked Evidence Clusters, ## Stats, and ## Source Coverage blocks are bounded inside <!-- EVIDENCE FOR SYNTHESIS --> / <!-- END EVIDENCE FOR SYNTHESIS --> comments in the --emit compact / --emit md stdout. They are raw evidence for YOU to read, not output to emit. Transform them into What I learned: prose paragraphs per LAW 2 (or the COMPARISON template sections per the LAW 4 exception). If your response contains the literal string ### 1. followed by a score tuple like (score N, M items, sources: ...), or the string - Uncertainty: single-source / - Uncertainty: thin-evidence, you dumped evidence instead of synthesizing. STOP and regenerate.
Observed LAW 6 violation (2026-04-19, Hermes Agent Use Cases disaster): two consecutive /social-researching Hermes Agent (Actual) Use Cases runs returned the raw ## Ranked Evidence Clusters block verbatim as user output, with 8 cluster entries carrying (score N, M items, sources: ...) tuples and - Uncertainty: single-source lines. Root cause: the prior canonical-boundary text said "Pass through the lines ABOVE this boundary verbatim," which the model scoped broadly to include the scratchpad. The current boundary text and this LAW 6 scope pass-through to the PASS-THROUGH FOOTER block only. A third run on the same topic framed as "Hermes Workflows" produced the correct What I learned: prose synthesis, which is the shape every run must produce.
Worked example (LAW 6 transformation). Evidence block you read:
<!-- EVIDENCE FOR SYNTHESIS: read this, do not emit verbatim. -->
## Ranked Evidence Clusters
### 1. Hermes Agent: The Self-Improving AI That Learns You (score 45, 1 item, sources: Youtube)
1. [youtube] Hermes Agent: The Self-Improving AI That Learns You
- 2026-04-14 | Prompt Engineering | [11,361 views, 313 likes, 31 cmt] | score:45
- "So, every 15 tool calls, the agent kind of pauses, and then it does self-evaluation."
- "Can you tell me what type of user profile you have on me?"
### 2. Use cases of OpenClaw, Hermes Agent, etc... (score 43, 1 item, sources: Reddit)
1. [reddit] Use cases of OpenClaw, Hermes Agent, etc... (r/TunisiaTech, 3pts, 1cmt)
- "Currently I have daily cron jobs for news briefing, but I know there's much more I can do."
<!-- END EVIDENCE FOR SYNTHESIS -->
Output you emit (prose synthesis, NOT the evidence block):
What I learned:
The self-evolving loop is the sticky use case. Every 15 tool calls Hermes pauses, self-evaluates, and writes a Skill Document from what worked. Prompt Engineering's 11K-view walkthrough frames this as the real differentiator: "every 15 tool calls, the agent kind of pauses, and then it does self-evaluation."
Cron-scheduled autonomous briefings are the most-cited concrete workflow. r/TunisiaTech's "Use cases of OpenClaw, Hermes Agent" thread says it plainly: "Currently I have daily cron jobs for news briefing, but I know there's much more I can do."
LAW 7 - YOU ARE THE PLANNER. --plan IS MANDATORY ON NAMED-ENTITY TOPICS. If you are the reasoning model hosting this skill (Claude Code, Codex, Hermes, Gemini, or any agent runtime that invoked /social-researching), YOU generate the JSON query plan. You do not need an API key, "LLM provider" credentials, or an external planning service - you ARE the LLM. The --plan flag exists precisely so a reasoning model generates its own plan upstream and passes it to the engine. The engine's internal planner and deterministic fallback are headless/cron paths only; on any reasoning-model path, bypass them by passing --plan "$QUERY_PLAN_FILE" (the path to a tmpfile you wrote via heredoc — see Step 1 for the pattern; never inline --plan '$JSON', apostrophes in search/ranking strings break shell parsing).
Named-entity topics (capitalized proper nouns, product names, person names, project names, or any topic that would benefit from handle resolution in Step 0.55) REQUIRE --plan. Your invocation of scripts/social-researching.py MUST contain --plan "$QUERY_PLAN_FILE" (or any path the engine can read). A bare python3 scripts/social-researching.py "$TOPIC" --emit=compact on a named-entity topic is a LAW 7 violation. Before you invoke Bash, self-check: does my command contain --plan? If no, STOP and generate a plan first (see Step 0.75 for the schema).
Observed LAW 7 violation (2026-04-19, Hermes Agent Use Cases Run 1): the model called the engine bare with no --plan, no pre-flight handle resolution. The engine emitted a stderr warning ("No --plan and no LLM provider configured. Using deterministic fallback...") which the model read as a capability constraint ("I don't have a key, I can't do LLM stuff") instead of as what it actually was: a reminder that the reasoning model skipped its own planning step. The misread came from the word "provider" - the engine uses "provider" to mean "the key for the engine's INTERNAL planner," but the model parsed it as "I need a provider to plan at all." You do not. You ARE the provider. Run 2 of the same topic (2026-04-19, framed as "best workflows") with the same model and same cache generated the plan itself via --plan and produced clean results - the delta was this step.
Self-check before Bash: re-read your pending scripts/social-researching.py command. Does it contain --plan "$QUERY_PLAN_FILE" (or another path the engine can read)? If no, and the topic is a named entity, STOP. Return to Step 0.75 and generate the plan, then write it to a tmpfile per the Step 1 pattern. Do not interpret the word "provider" in any engine message as "you need credentials" - you are the provider.
LAW 8 - EVERY CITATION IN THE NARRATIVE IS AN INLINE MARKDOWN LINK [name](url). NEVER A RAW URL STRING. NEVER A PLAIN NAME WHEN A URL IS AVAILABLE. Applies to every query type. In the "What I learned:" narrative, in KEY PATTERNS, and in the COMPARISON body sections, every cited @handle, r/subreddit, publication, YouTube channel, TikTok creator, Instagram creator, and Polymarket market is wrapped as [name](url) at first mention. The URL comes from the raw research dump — every engine item carries a URL; WebSearch supplements carry URLs in their own output. Claude Code renders [text](url) as blue CMD-clickable text; the URL is hidden in the rendering, only the link text shows. The stats footer (emoji-tree block) is engine-emitted per LAW 5 and passes through verbatim — do NOT reformat its links yourself.
Plain-text fallback: if the raw data genuinely has no URL for a specific source, fall back to plain text for that one citation only. Never emit a broken empty link like [Rolling Stone]() or [@handle](). Default assumption: URL exists; plain text is the exception.
BAD (raw URL): per https://www.rollingstone.com/music/music-news/kanye-west-bully-1235506094/
BAD (plain name when URL is available): per Rolling Stone, per @honest30bgfan_, r/hiphopheads
BAD (broken empty link): per [Rolling Stone]()
GOOD: per [Rolling Stone](https://www.rollingstone.com/music/music-news/kanye-west-bully-1235506094/), per [@honest30bgfan_](https://x.com/honest30bgfan_), [r/hiphopheads](https://reddit.com/r/hiphopheads)
FALLBACK (URL genuinely missing): per Rolling Stone
Observed LAW 8 need (2026-04-20 inline-links saga): the citation rule existed in SKILL.md but was placed in the CITATION PRIORITY block around line 1224 - below the chunked-read window. Four consecutive test runs (Matt Van Horn, Peter Steinberger, Best Headphones, OpenClaw vs Hermes) confirmed the rule was deployed (diff IN SYNC, grep found the text) but was skipped on every synthesis because the model read lines 1-1000 and stopped. The model's own self-diagnosis, repeated verbatim four times: "I never reached line 1224." LAW 8 hoists the rule into the same guaranteed-loaded band as LAWs 1-7 so it enters context on every run. Same pattern that solved v3.0.6 (invented titles), disaster #2 (stripped bold), disaster #3 (trailing Sources), and the Hermes 2026-04-19 evidence-dump disaster.
Post-synthesis self-check (do this BEFORE emitting your response): scan your drafted "What I learned:" and KEY PATTERNS for the [name](url) pattern. Count how many inline markdown links appear. If zero - and the raw dump has URLs for the @handles, r/subs, and publications you cited as plain text - regenerate ONCE with inline links added. Stripping links is not a valid way to satisfy any other LAW; LAWs 1 (no trailing Sources) and 8 (inline links required) are complementary, not alternatives.
End of OUTPUT CONTRACT. The laws above are the contract; everything below is implementation detail.
STEP 0 - LOAD WEBSEARCH FIRST. Your literal first tool call on every /social-researching invocation MUST be:
ToolSearch select:WebSearch
WebSearch is a deferred tool in Claude Code v2.1.114. The frontmatter of this file authorizes it (allowed-tools: ... WebSearch) but the runtime lists it as "schemas are NOT loaded." Calling WebSearch without ToolSearch select:WebSearch first will fail or do nothing. That friction is the documented cause of the second-most-common failure mode of this skill: the model sees "WebSearch is there but deferred," takes the low-friction path, skips Step 0.5 and 0.55, and runs the engine bare with only keyword search. The output looks fine but misses founder X timelines, GitHub repo activity, and subreddit-specific threads.
Load WebSearch first. No exceptions. Then proceed to the branching rule below.
STEP 1 - RUN THE ENGINE. You MUST run scripts/social-researching.py via Bash. Do not produce output from WebSearch alone.
The single most common failure mode of this skill is the model reading this file, skimming the section headers, and then answering the user's topic with 3-10 WebSearch calls followed by a prose summary. That is wrong output. The Python engine is the skill. Web-only synthesis is not the skill.
Branching rule:
/social-researching Kanye West, /social-researching nvidia earnings): proceed to Step 0.5 / Step 0.55 / Step 0.75 / Research Execution below. Do not skip straight to WebSearch. WebSearch is a supplement after the Python engine runs (see Step 2). It is not a substitute.If you are about to write a response without having run scripts/social-researching.py at least once, stop. Return to Research Execution and run the engine. Every valid output from this skill includes the emoji-tree footer (✅ All agents reported back!) that the engine produces data for. No footer means you did not run the skill.
Before Step 0.5, run Step 0.45 Query Quality Pre-Flight. If the topic is a keyword trap (demographic shopping like "gift for 42 year old man", numeric/age trap, overly-literal concept phrase like "how to use Docker", or generic single-noun like "sneakers"), reframe or ask ONE clarifying question before calling the engine. Skipping Step 0.45 on a keyword-trap topic is the named failure mode of the 2026-04-18 "Birthday gift for 42 year old man" disaster: the engine ran on the literal phrase and returned 5 minutes of r/todayilearned / r/japannews / r/LivestreamFail noise because no human posts "I bought a 42 year old man a gift" on Reddit.
If your Bash call to social-researching.py does NOT include the FULL pre-flight checklist resolved (see Step 0.5 Pre-Flight Checklist), that is a Step 0.5/0.55 skip. The engine will emit a ## Pre-Research Status warning block in its output. Pass the warning through verbatim; do not try to hide it. The warning tells the user to rerun with WebSearch loaded.
For person topics specifically (developers, creators, CEOs, founders): the Bash command MUST include MINIMUM --x-handle={handle} AND --github-user={handle} AND --subreddits={list}, and typically --x-related={list}, unless an explicit "no account" note was produced during Step 0.5. A person-topic command with ONLY --x-handle is the Peter Steinberger disaster #2 failure mode (2026-04-18): the model read the X-handle subsection literally, stopped there, and skipped the rest of the checklist. Result: weak Reddit targeting, no GitHub person-mode scoping, no related-voices enrichment, and a thin corpus. The fix is to read the Step 0.5 Pre-Flight Checklist FIRST and resolve every applicable flag before running the engine.
Permissions overview: Reads public web/platform data and optionally saves research briefings to
LAST30DAYS_MEMORY_DIR(defaults to~/Documents/Last30Days). X/Twitter search uses optional user-provided tokens (AUTH_TOKEN/CT0 env vars). Bluesky search uses optional app password (BSKY_HANDLE/BSKY_APP_PASSWORD env vars - create at bsky.app/settings/app-passwords). All credential usage and data writes are documented in the Security & Permissions section.
Research ANY topic across Reddit, X, YouTube, and other sources. Surface what people are actually discussing, recommending, betting on, and debating right now.
Before running any social-researching.py command in this skill, resolve a Python 3.12+ interpreter once and keep it in LAST30DAYS_PYTHON:
for py in python3.14 python3.13 python3.12 python3; do
command -v "$py" >/dev/null 2>&1 || continue
"$py" -c 'import sys; raise SystemExit(0 if sys.version_info >= (3, 12) else 1)' || continue
LAST30DAYS_PYTHON="$py"
break
done
if [ -z "${LAST30DAYS_PYTHON:-}" ]; then
echo "ERROR: social-researching v3 requires Python 3.12+. Install python3.12 or python3.13 and rerun." >&2
exit 1
fi
LAST30DAYS_MEMORY_DIR="${LAST30DAYS_MEMORY_DIR:-$HOME/Documents/Last30Days}"
Set LAST30DAYS_MEMORY_DIR before invoking the skill to choose where raw research files are saved. If it is not set, the skill defaults to ~/Documents/Last30Days.
Before proceeding to Step 1, handle first-run setup.
First-run detection (silent, no commands, no output to user):
~/.config/social-researching/.env does NOT exist, this is a first run.SETUP_COMPLETE=true, skip Step 0 entirely and go to Step 1 (CRITICAL: Parse User Intent below). Do NOT announce that setup is complete. The user does not need a status message on every run.If this IS a first run:
skills/social-researching/nux-wizard.md (relative to the skill root).SETUP_COMPLETE=true to ~/.config/social-researching/.env, proceed to research.The wizard lives in a separate file so the common-case (already set up) path through this file is short and the voice-contract rules further down stay in context.
Before doing anything, parse the user's input for:
Common patterns:
[topic] for [tool] → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED[topic] prompts for [tool] → "UI design prompts for Midjourney" → TOOL IS SPECIFIED[topic] → "iOS design mockups" → TOOL NOT SPECIFIED, that's OKvs or versus with spaces)IMPORTANT: Do NOT ask about target tool before research.
Store these variables:
TOPIC = [extracted topic]TARGET_TOOL = [extracted tool, or "unknown" if not specified]QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | COMPARISON | GENERAL]TOPIC_A = [first item] (only if COMPARISON)TOPIC_B = [second item] (only if COMPARISON)Confirm the topic with a branded, truthful message. Build ACTIVE_SOURCES_LIST by checking what's configured in .env:
which gh): add GitHubwhich digg-pp-cli): add Diggwhich yt-dlp): add YouTube--search=pinterest): add PinterestThen display (use "and more" if 5+ sources, otherwise list all with Oxford comma):
For GENERAL / NEWS / RECOMMENDATIONS / PROMPTING queries:
/social-researching - searching {ACTIVE_SOURCES_LIST} for what people are saying about {TOPIC}.
For COMPARISON queries:
/social-researching - comparing {TOPIC_A} vs {TOPIC_B} across {ACTIVE_SOURCES_LIST}.
Do NOT show a multi-line "Parsed intent" block with TOPIC=, TARGET_TOOL=, QUERY_TYPE= variables. Do NOT promise a specific time. Do NOT list sources that aren't configured.
Then proceed immediately to Step 0.45.
MANDATORY. Before Step 0.5, diagnose the topic for known failure classes. If the topic is a keyword trap, reframe or ask a clarifying question BEFORE calling the engine. Running the engine on a doomed query burns 5+ minutes and produces junk. Detecting the trap upfront costs one turn.
Known keyword-trap classes and how to handle each:
Class 1: Demographic shopping query
gift for {age} year old {gender}, what to buy for my {relationship}, present for {demographic}, birthday gift for {age} {gender}."Before I research, tell me a bit more - hobbies (cooks / runs / reads / gaming / outdoors / golf / music)? Relationship (husband / dad / friend / boss / brother)? Budget range? A 'gift for a 42 year old man' is a wide net; hobbies + relationship narrow it 10x."
gifts for men in their 40s or gifts for men who [hobby]--subreddits=GiftIdeas,BuyItForLife,AskMen,malefashionadvice,Dads (plus hobby-specific subs when known)Class 2: Numeric / age keyword trap
Class 3: Overly-literal concept phrase
how to use X, what is Y, tutorial for Z, explain A — tutorial-shaped phrasing where social posts are in different vocabulary.Class 4: Generic single-noun common word
bread, sneakers, coffee, shoes, headphones)."{TOPIC} is a huge category - are you asking about {specific-facet-A}, {specific-facet-B}, or {specific-facet-C}? Each is a different community. Pick one or tell me the angle."
Pre-Flight decision flow (do this BEFORE any WebSearch):
Pre-Flight: topic matches {Class N} ({class name}). {Action: clarifying question / reframe / specificity ask}.Pre-Flight: topic is a {named-entity / comparison / concept} - proceeding to Step 0.5. Then proceed.One-turn gate rule: do NOT run the engine on a keyword-trap topic without either (a) explicit user confirmation to "just run it anyway", or (b) a concrete reframed query. Burning 5 minutes on a doomed run is worse than a one-turn clarifying question.
When the user provides context inline: if a Class 1 query already contains hobbies/relationship/budget ("gift for my cooking-obsessed husband, $200"), SKIP the clarifying question and go straight to the reframe + scope action. The clarifying question exists to fill in the gaps; if the gaps are already filled, move on.
Pre-Flight Checklist — do NOT stop after the first flag. Every applicable flag below is MANDATORY for its topic class.
Before running the engine, determine which flags apply to this topic and resolve them. Reading only the "X handle" subsection and stopping there is the named failure mode of the Peter Steinberger disaster #2 (2026-04-18). The model admitted on debug: "I treated the 'X handle resolution' section as the full contract for pre-flight resolution and didn't --help the script to see what else existed." The checklist below IS the full contract.
| Flag | Resolved in | Applies when |
|---|---|---|
--x-handle={handle} | Step 0.5 (Section A below) | Topic is a person, brand, product, or creator with an X presence |
--x-related={h1,h2,...} | Step 0.5 (Section A below) | Topic has associated entities (founders, commentators, spouse, collaborators, media handles) |
--github-user={user} | Step 0.5b | Topic is a person who ships code (developer, engineer, CEO-who-codes, researcher) |
--github-repo={owner/repo} | Step 0.5c | Topic is a product / project / open-source tool |
--subreddits={sub1,sub2,...} | Step 0.55 | Always — almost every topic has active Reddit communities |
--tiktok-hashtags={h1,h2,...} | Step 0.55 | Always — inferred from topic |
--tiktok-creators={c1,c2,...} | Step 0.55 | Creator / influencer / brand topics |
--ig-creators={c1,c2,...} | Step 0.55 | Creator / brand topics |
--auto-resolve | Fallback | WebSearch is available but Step 0.55 could not resolve everything cleanly — use as belt-and-suspenders |
Checkpoint before running the engine: your Bash command must include every flag from the checklist that applies to this topic. For a person who ships code (the Peter Steinberger class), that is MINIMUM --x-handle AND --github-user AND --subreddits, and typically --x-related too. A command with only --x-handle on a person topic is a pre-flight skip and a Step 0.5 regression.
If TOPIC looks like it could have its own X/Twitter account - people, creators, brands, products, tools, companies, communities (e.g., "Dor Brothers", "Jason Calacanis", "Nano Banana Pro", "Seedance", "Midjourney"), do WebSearches to find handles in three categories:
1. Primary handle (the entity itself):
WebSearch("{TOPIC} X twitter handle site:x.com")
2. Company/organization handle OR founder/creator handle -- This mapping is bidirectional:
WebSearch("{TOPIC} company CEO of site:x.com")
OR for products:
WebSearch("{TOPIC} creator founder X twitter site:x.com")
Examples: Sam Altman -> @OpenAI, Dario Amodei -> @AnthropicAI, OpenClaw -> @steipete (Peter Steinberger), Paperclip -> @dotta, Claude Code -> @alexalbert__.
3. 1-2 related handles -- People/entities closely associated with the topic (spouse, collaborator, band member), PLUS 1-2 prominent commentator/media handles that regularly cover this topic:
WebSearch("{RELATED_PERSON_OR_ENTITY} X twitter handle site:x.com")
For a music artist, find music commentary accounts (e.g., @PopBase, @HotFreestyle, @DailyRapFacts). For a tech CEO, find tech media accounts (e.g., @TechCrunch, @TheInformation). For a product, find reviewer accounts in that category.
From the results, extract their X/Twitter handles. Look for:
x.com/{handle} or twitter.com/{handle}Verify accounts are real, not parody/fan accounts. Check for:
Pass handles to the CLI:
--x-handle={handle} (without @)--x-related={handle1},{handle2},{company_handle},{commentator_handles} (comma-separated, without @)Example for "Kanye West":
--x-handle=kanyewest--x-related=travisscott,PopBase,HotFreestyleExample for "Sam Altman":
--x-handle=sama--x-related=OpenAI,TechCrunchRelated handles are searched with lower weight (0.3) so they appear in results but don't dominate over the primary entity's content.
Note about @grok: Grok is Elon's AI on X (xAI). It often appears in search results with thoughtful, accurate analysis. When citing @grok in your synthesis, frame it as "per Grok's AI analysis of [article/topic]" rather than treating it as an independent human commentator.
Skip this step if:
--quick depthStore: RESOLVED_HANDLE = {handle or empty}, RESOLVED_RELATED = {comma-separated handles or empty}
MANDATORY when the topic is a person (developer, creator, CEO, founder, engineer, researcher) and WebSearch is available. Resolving the X handle but NOT the GitHub handle is the documented Peter Steinberger failure mode (2026-04-18). Without --github-user={handle}, GitHub search becomes a keyword match across all of GitHub instead of person-mode scoped to user:{handle}. The result is typically 5-10 thin unrelated items instead of the person's actual commits, PRs, releases, and top-starred repos. Treat this as a peer step to Step 0.5 (X handle resolution), not an afterthought.
Do the WebSearch:
WebSearch("{TOPIC} github profile site:github.com")
From the results, extract their GitHub username from URLs like github.com/{username}.
Verify the account is correct: Check that the profile description or pinned repos match the person you're researching. Common names may return multiple profiles.
Pass to the CLI: --github-user={username} (without @)
Worked examples:
Peter Steinberger github profile site:github.com returns @steipete. Pass --github-user=steipete.--github-user=mvanhorn--github-user=garrytanPerson-mode GitHub tells a different story than keyword search. Instead of "who mentioned this person in an issue body," it answers: "What are they shipping? Where are they getting merged? What do their own projects look like?" The engine fetches PR velocity, top repos with star counts, release notes, and README summaries.
Skip this step if:
--github-user specified by the user--quick depth--github-user rather than fabricating one)Store: RESOLVED_GITHUB_USER = {username or empty}
Checkpoint for person topics: by the time you reach the Research Execution command, for a person topic you MUST have BOTH RESOLVED_HANDLE (from Step 0.5) AND RESOLVED_GITHUB_USER (from this step) OR an explicit "no X account" / "no GitHub profile" note. The Bash command that follows must include BOTH --x-handle={handle} AND --github-user={handle} when resolved. A person-topic run that shows only one of the two is a Step 0.5b regression.
If TOPIC looks like a product, tool, or open source project (not a person), resolve its GitHub repo for project-mode search:
WebSearch("{TOPIC} github repo site:github.com")
From the results, extract owner/repo from URLs like github.com/{owner}/{repo}.
Pass to the CLI: --github-repo={owner/repo}
For comparisons ("X vs Y"), resolve repos for both topics: --github-repo={repo_a},{repo_b}
Example for "OpenClaw": --github-repo=openclaw/openclaw
Example for "OpenClaw vs Paperclip": --github-repo=openclaw/openclaw,paperclipai/paperclip
Project-mode GitHub fetches live star counts, README snippets, latest releases, and top issues directly from the API. This is always more accurate than blog posts or YouTube videos citing weeks-old numbers.
Skip this step if:
--github-user instead)Store: RESOLVED_GITHUB_REPOS = {comma-separated owner/repo or empty}
If --agent appears in ARGUMENTS (e.g., /social-researching plaud granola --agent):
AskUserQuestion calls - use TARGET_TOOL = "unknown" if not specifiedAgent mode saves raw research data to LAST30DAYS_MEMORY_DIR (defaults to ~/Documents/Last30Days) automatically via --save-dir (handled by the script, no extra tool calls).
Agent mode report format:
## Research Report: {TOPIC}
Generated: {date} | Sources: Reddit, X, Bluesky, YouTube, TikTok, HN, Polymarket, Web
### Key Findings
[3-5 bullet points, highest-signal insights with citations]
### What I learned
{The full "What I learned" synthesis from normal output}
### Stats
{The standard stats block}
When the user asks "X vs Y" (or "X vs Y vs Z"), the engine fans out N full pipeline.run() calls in parallel — one per entity — each with its own Step 0.55-grade targeting. This restored the old N-pass architecture (reverted the one-pass latency optimization that removed per-entity depth); parallel execution keeps wall clock ≈ a single pass.
MANDATORY per-entity resolution. For each entity, resolve the full Step 0.55 stack (X handle, subreddits, GitHub user/repos, news context). Then assemble a --competitors-plan JSON mapping each entity to its targeting, and invoke the engine ONCE with the vs-topic string.
Output shape per run:
{main-slug}-raw.md.{peer-slug}-raw.md.## Head-to-Head scaffold + per-entity Resolved Entities block.Invocation:
# SKILL_DIR = absolute path of the directory containing THIS SKILL.md you just Read.
# Substitute the actual path below — your harness told you where this file lives via
# the Read tool result. Examples:
# Read ~/.claude/skills/social-researching/SKILL.md → SKILL_DIR=$HOME/.claude/skills/social-researching
# Read ~/.codex/skills/social-researching/SKILL.md → SKILL_DIR=$HOME/.codex/skills/social-researching
# Read ~/.claude/plugins/cache/social-researching-skill/social-researching/3.3.2/skills/social-researching/SKILL.md
# → SKILL_DIR=$HOME/.claude/plugins/cache/social-researching-skill/social-researching/3.3.2/skills/social-researching
# scripts/social-researching.py is always a direct child of SKILL_DIR (every install layout
# packages SKILL.md and scripts/ as siblings).
SKILL_DIR="<absolute path of the directory containing the SKILL.md you Read>"
if [ ! -f "$SKILL_DIR/scripts/social-researching.py" ]; then
echo "ERROR: scripts/social-researching.py not found under SKILL_DIR=$SKILL_DIR" >&2
echo "Re-check the directory of the SKILL.md you Read and substitute it as SKILL_DIR above." >&2
exit 1
fi
# Write the per-entity plan to a tmpfile and pass the path to the engine.
# The engine's parse_competitors_plan() reads file paths transparently. This
# avoids the inline-single-quoted-JSON apostrophe trap (resolved context
# strings like "people's choice" or "McDonald's" otherwise close the outer
# single-quote and break shell parsing before the engine is even invoked).
# Trailing XXXXXX (no .json suffix) so BSD/macOS mktemp works the same as
# GNU; BSD only substitutes X's at the end of the template.
COMPETITORS_PLAN_FILE=$(mktemp "${TMPDIR:-/tmp}/social-researching-competitors.XXXXXX")
trap 'rm -f "$COMPETITORS_PLAN_FILE"' EXIT
cat > "$COMPETITORS_PLAN_FILE" <<'PLAN_EOF'
{
"{TOPIC_B}": {"x_handle":"{TOPIC_B_HANDLE}","subreddits":["{TOPIC_B_SUB_1}","{TOPIC_B_SUB_2}"],"github_user":"{TOPIC_B_GH}","context":"{TOPIC_B_CONTEXT}"},
"{TOPIC_C}": {"x_handle":"{TOPIC_C_HANDLE}","subreddits":["{TOPIC_C_SUB_1}"],"github_user":"{TOPIC_C_GH}","context":"{TOPIC_C_CONTEXT}"}
}
PLAN_EOF
"${LAST30DAYS_PYTHON}" "${SKILL_DIR}/scripts/social-researching.py" "{TOPIC_A} vs {TOPIC_B} vs {TOPIC_C}" \
--emit=compact \
--save-dir="${LAST30DAYS_MEMORY_DIR}" \
--save-suffix=v3 \
--x-handle={TOPIC_A_HANDLE} \
--subreddits={TOPIC_A_SUBS} \
--competitors-plan "$COMPETITORS_PLAN_FILE"
The quoted heredoc marker 'PLAN_EOF' is load-bearing — quoting suppresses shell interpolation so apostrophes, $, backticks, etc. pass through verbatim. If you ever switch to an unquoted <<PLAN_EOF, every variable reference and apostrophe inside the JSON becomes a parse hazard.
Topic A (the main topic, first in the vs-string) uses outer --x-handle, --x-related, --subreddits, --github-user, --github-repo, --tiktok-*, --ig-creators as usual. Topics B and C get their targeting from --competitors-plan entries (keyed by entity name, case-insensitive).
Step 0.55 for N entities. The same pre-research protocol that applies to a single-entity topic applies to EACH entity in a vs-run. For N=3, that means 3 WebSearches for X handles, 3 for subreddits, 3 for GitHub, 3 for news context — or equivalent batched queries. A ## Resolved Entities block with dashes for any entity means you skipped Step 0.55 for that one. Re-run with a corrected plan.
Then do WebSearch supplements for: {TOPIC_A} vs {TOPIC_B} comparison {YEAR} and {TOPIC_A} vs {TOPIC_B} which is better — these catch rivalry articles that per-entity passes might not surface.
Skip the normal Step 1 below - go directly to the comparison synthesis format (see "If QUERY_TYPE = COMPARISON" in the synthesis section).
COMPARISON TABLE SCAFFOLD (engine-emitted, pass through verbatim): For comparison topics, the engine's compact output includes a ## Head-to-Head block with an empty markdown table (columns = entities, rows = axes like "What it is", "Community sentiment", "Trajectory"). Your synthesis MUST include this block verbatim with filled cells, positioned between the narrative and the emoji-tree footer. Keep each cell to 5-15 words. Use ' - ' (hyphen with spaces) not em-dashes inside cells.
--competitors)--competitors is a SKILL.md-level shortcut for vs-mode with auto-discovery. The engine flag itself just signals intent; YOU (the hosting reasoning model) do the discovery and Step 0.55 via your own WebSearch tool, then invoke the vs-topic path above.
The four-step protocol:
"{topic} competitors" / "{topic} alternatives". Pick N=2 by default (match the flag's default), N=argument value if the user passed --competitors=N."{main} vs {peer1} vs {peer2}".--competitors-plan JSON covering both peers (and the main topic if you want to override the outer flags), and the outer --x-handle/--subreddits/--github-* for the main topic.Flag surface (engine):
--competitors (bare) - signals the hosting model to discover 2 peers (3-way total).--competitors=N - N peers (1..6; out-of-range clamps with stderr warning).--competitors-list="A,B,C" - minimum escape hatch; names only, no per-entity targeting. Peer sub-runs fall back to planner defaults (visibly thinner data).--competitors-plan '{entity: {x_handle, subreddits, github_user, github_repos, context}}' - full per-entity targeting; implies vs-mode; preferred.--polymarket-keywords "kw1,kw2" - disambiguate Polymarket for ambiguous single-token topics ("Warriors" → nba,gsw,golden-state).Why --competitors-plan over --competitors-list: without per-entity handles/subs, peer sub-runs run with deterministic single-word planner queries and produce visibly thinner evidence than the main topic. The Resolved Entities block in stdout makes the gap visible — dashes for a peer = you skipped its Step 0.55.
Engine-internal auto-resolve (headless fallback): if the engine detects BRAVE_API_KEY / EXA_API_KEY / SERPER_API_KEY / PARALLEL_API_KEY / OPENROUTER_API_KEY, it runs its own per-entity resolve.auto_resolve() before each sub-run. The hosting-model path does NOT need those keys — you are the WebSearch. The engine's auto-resolve is the cron/CI fallback for when no reasoning model is driving.
Output: one {slug}-raw.md per entity in --save-dir plus the merged comparison on stdout. Synthesis contract identical to the vs-mode protocol above.
PLATFORM GATE: If your platform does NOT support WebSearch (e.g., OpenClaw, raw CLI), skip Steps 0.55 and 0.75 but add
--auto-resolveto the Python command in the Research Execution section. The engine will do its own pre-research using configured web search backends (Brave, Exa, or Serper) to discover subreddits, X handles, and current events context before planning.
MANDATORY on Claude Code (and any platform with WebSearch). You MUST perform Step 0.55 before calling the Python engine. Skipping this step is the second-most-common failure mode of this skill, right after skipping the engine entirely. If your Bash call to social-researching.py does NOT include a --plan flag with resolved handles and subreddits, that is a Step 0.55 skip and a failure. The engine's [Resolve] No web search backend available, skipping resolve log line means you, the model, did not do your job - it does NOT mean "the engine will handle it." Treat this step as non-skippable. Repeat invocations on the same topic still re-run Step 0.55 because Reddit/X/TikTok handles for breaking-news topics change week to week.
Run 2-3 focused WebSearches (in parallel) to resolve platform-specific targeting. Do NOT search for every platform individually - that wastes time. Instead, use your knowledge of the topic to infer most targeting, and only WebSearch for what you can't infer.
1. X handles - Already resolved in Step 0.5 above (including company handles and commentators). Reference your RESOLVED_HANDLE and RESOLVED_RELATED from that step.
2. Reddit communities + YouTube channels + current events - Run 1-2 searches that cover multiple platforms at once:
WebSearch("{TOPIC} subreddit reddit community")
WebSearch("{TOPIC} news {CURRENT_MONTH} {CURRENT_YEAR}")
The first search finds subreddits. The second gives you current events context (which helps you generate better subqueries in Step 0.75) and may surface YouTube channels or creators organically.
Extract 3-5 subreddit names from the results. Store as RESOLVED_SUBREDDITS (comma-separated, no r/ prefix).
2a. Category-peer expansion (MANDATORY for product topics). If the topic is a product in a recognizable category (AI image generation, AI video generation, AI coding agents, AI music, AI chat models, SaaS screen recording, prediction markets, etc.), the brand-specific subreddits that WebSearch returned are INSUFFICIENT. Add 2-3 peer subreddits from the category. Peer subs are where cross-product technique discussion actually lives. Missing them is the 2026-04-22 GPT Image 2 failure mode: the model resolved r/OpenAI, r/ChatGPT, r/singularity, r/ChatGPTpromptengineering (all OpenAI-brand) and missed r/StableDiffusion, r/midjourney, r/dalle2, r/aiArt where prompting techniques are actually shared. The user had to manually prompt "check image generation reddits too" to get a usable run.
Canonical category peers (single source of truth; scripts/lib/categories.py mirrors this for the --auto-resolve engine path):
| Category | Trigger keywords | Peer subs (priority order) |
|---|---|---|
ai_image_generation | image generation, text to image, GPT Image, Nano Banana, Midjourney, Stable Diffusion, DALL-E, Flux.1, Imagen, Seedance, Ideogram, Recraft | StableDiffusion, midjourney, dalle2, aiArt, PromptEngineering, MediaSynthesis |
ai_video_generation | video generation, text to video, Sora, Veo 3, Runway Gen, Kling, Pika Labs, Luma Dream Machine, Hailuo | aivideo, StableDiffusion, runwayml, singularity, MediaSynthesis |
ai_music_generation | music generation, ai music, Suno, Udio, Riffusion, Stable Audio | SunoAI, udiomusic, aimusic, artificial |
ai_coding_agent | Claude Code, Cursor IDE, GitHub Copilot, Windsurf, Aider, Cline, OpenClaw, Hermes Agent, Continue.dev, Codeium, Devin | ChatGPTCoding, LocalLLaMA, singularity, PromptEngineering |
ai_agent_framework | agent framework, LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, DSPy, smolagents | LangChain, LocalLLaMA, AI_Agents, MachineLearning |
ai_chat_model | GPT-5/4, Claude Opus/Sonnet/Haiku, Gemini Pro/Flash, Llama 3/4, DeepSeek, Qwen, Mistral Large, Grok | LocalLLaMA, ChatGPT, ClaudeAI, singularity, artificial |
saas_screen_recording | screen recording, screen recorder, Loom video, Tella screen, Vidyard | SaaS, screenrecording, productivity, Entrepreneur |
saas_productivity | Notion app, Obsidian, Linear app, Asana, ClickUp, productivity app | productivity, SaaS, ObsidianMD, Notion |
prediction_markets | Polymarket, Kalshi, prediction market, event contracts, Manifold Markets | Polymarket, Kalshi, predictionmarkets |
crypto_defi | DeFi protocol, yield farming, liquidity pool, stablecoin, layer 2, L2 rollup | defi, ethfinance, CryptoCurrency, ethereum |
Merging rule. Start with WebSearch-returned subs. Append 2-3 category peers in the priority order shown. Dedupe case-insensitively (don't list midjourney twice if WebSearch already returned it). Cap total at 10: if adding all peers would exceed the cap, keep every WebSearch-returned sub (they are the freshest signal) and drop peers from the end of the priority list.
Extrapolation. If the topic is a product in a category NOT listed in the table (new AI tool, niche SaaS), use the same spirit: pick the 2-3 most active cross-product communities where technique discussion happens. A new image-gen tool still gets r/StableDiffusion, r/midjourney, r/aiArt. A new code editor still gets r/ChatGPTCoding, r/LocalLLaMA.
Worked example — the failing query. Topic: Prompting GPT Image 2.
Before (the 2026-04-22 failure mode):
Resolved:
- Reddit: r/OpenAI, r/ChatGPT, r/singularity, r/ChatGPTpromptengineering, r/artificial
After (with category-peer expansion):
Resolved:
- Reddit: r/OpenAI, r/ChatGPT, r/singularity, r/ChatGPTpromptengineering, r/StableDiffusion, r/midjourney, r/dalle2, r/aiArt (+ ai_image_generation peers)
The parenthetical (+ ai_image_generation peers) is the observable contract of the new Resolved block format. See Step 0.55 self-check below.
3. TikTok hashtags + creators - INFER these from your topic knowledge. Do NOT WebSearch for "{PERSON} TikTok account" - most people/CEOs don't have TikTok, and the search is wasted.
kanyewest,ye,bully. "Claude Code" → claudecode,aiagent,aicoding. "Sam Altman" → samaltman,openai,chatgpt.Store as RESOLVED_HASHTAGS and RESOLVED_TIKTOK_CREATORS.
4. Instagram creators - Same rule: INFER from topic knowledge. If the topic is a celebrity, brand, or creator with obvious Instagram presence, use their handle directly. If the topic is a tech CEO or abstract concept, skip. Do NOT waste a WebSearch on "Dario Amodei Instagram account."
Store as RESOLVED_IG_CREATORS.
5. YouTube content queries - Infer 2-3 YouTube content-type queries from the topic without searching. The current events search (#2 above) may surface relevant YouTube channels.
'{TOPIC} album review', '{TOPIC} reaction''{TOPIC} review', '{TOPIC} tutorial''{TOPIC_A} vs {TOPIC_B}''{TOPIC} interview {YEAR}', '{TOPIC} latest news'Store as RESOLVED_YT_QUERIES.
Concrete examples:
| Topic | WebSearches needed | Reddit subs | TikTok hashtags | TikTok creators | IG creators | YT queries |
|---|---|---|---|---|---|---|
| Kanye West | 2 (subreddit + BULLY news) | Kanye,WestSubEver,hiphopheads,Music | kanyewest,ye,bully | (inferred: kanyewest) | (inferred: kanyewest) | kanye west bully review,kanye west bully reaction |
| Sam Altman vs Dario | 2 (subreddit + AI CEO news) | artificial,MachineLearning,OpenAI,ClaudeAI | samaltman,openai,anthropic | (skip - CEOs don't TikTok) | (skip - CEOs don't Reel) | sam altman interview 2026,dario amodei interview 2026 |
| Tella (SaaS) | 2 (subreddit + Tella news) | SaaS,Entrepreneur,screenrecording,productivity | tella,tellaapp,screenrecording | (search: tella screen recorder TikTok) | (inferred: tella.tv) | tella screen recorder review,tella tutorial |
For comparison queries ("X vs Y" or "X vs Y vs Z") - MANDATORY per-entity resolution:
For each entity in the comparison, resolve all four lookup types. For a 3-way comparison that is up to 12 lookups (3 entities x 4 types). Batch them into 3-4 WebSearch calls by combining entities per query - do NOT fire one search per entity per type (that produces 12 searches and burns 90 seconds).
Per-entity lookup types to resolve:
owner/repo format (e.g., openai/openai-python)r/openclaw) AND general-category subreddits (e.g., r/LocalLLaMA)Example batching for "OpenClaw vs Hermes vs Paperclip":
WebSearch("OpenClaw Hermes Paperclip github repos AI coding agent")
WebSearch("OpenClaw Hermes Paperclip founders twitter X handles")
WebSearch("OpenClaw Hermes Paperclip reddit subreddits community")
Three searches for 12 lookups. After resolving, display all 12 per-entity in the Resolved block before running the engine:
Resolved (comparison):
- OpenClaw: X @openclawai | GitHub openclaw/openclaw | Founder @steipete | Reddit r/openclaw, r/AI_Agents
- Hermes: X @hermesagent | GitHub nousresearch/hermes | Founder @NousResearch | Reddit r/hermesagent, r/LocalLLaMA
- Paperclip: X @paperclipai | GitHub dotta/paperclip | Founder @dotta | Reddit r/OpenClawInstall
Passing the resolved block visibly (per-entity, all 4 types each) is the observable check that Step 0.55 happened for this comparison. A Resolved block that only lists 3 project handles with no founders and no GitHub repos is a Step 0.55 regression. This was canonical behavior and must stay canonical.
For non-comparison queries: Resolve communities/handles for the single topic. Merging list logic does not apply.
If you can't infer targeting for a platform, skip that flag -- the Python engine will fall back to keyword search.
Step 0.55 self-check: category-peer coverage. Before emitting the Resolved block, re-read your resolved subreddit list. Does the topic match any category in the Section 2a table (or fit the spirit of one — AI image gen, AI coding, AI music, etc.)? If YES: does your list include AT LEAST 2 peer subs from that category? If NO, widen the list NOW — do not run the engine yet. The observable contract is the (+ {category_id} peers) annotation on the Reddit line in the Resolved block. Its absence on a product-in-a-known-category topic is a Step 0.55 regression — the named 2026-04-22 failure mode. Person topics, music artists, news stories, and topics outside any category are exempt; omit the annotation.
After resolving all handles and communities, display what you found before moving on. This shows the user that intelligent pre-research happened:
Resolved:
- X: @{HANDLE} (+ @{COMPANY}, @{COMMENTATOR})
- Reddit: r/{sub1}, r/{sub2}, r/{sub3}, r/{peer1}, r/{peer2} (+ {category_id} peers)
- TikTok: #{hashtag1}, #{hashtag2}
- YouTube: {query1}, {query2}
Only show lines for platforms where something was resolved. Skip empty lines. On the Reddit line, the trailing (+ {category_id} peers) annotation appears when Step 0.55 Section 2a added category-peer subs. Omit the annotation when the topic had no matching category. This display replaces the old "Parsed intent" block with something more useful.
PLATFORM GATE: If you skipped Step 0.55 because WebSearch is unavailable, also skip this step. The Python engine will plan internally (enhanced by
--auto-resolveif a web search backend is configured). Jump to Research Execution.
If you have WebSearch and reasoning capability, YOU generate the query plan. The Python script receives your plan via --plan and skips its internal planner entirely. This produces better results because you have full context about the topic.
Generate a JSON query plan for the topic. Think about:
Output a JSON plan with this shape:
{
"intent": "breaking_news",
"freshness_mode": "strict_recent",
"cluster_mode": "story",
"subqueries": [
{
"label": "primary",
"search_query": "kanye west",
"ranking_query": "What notable events involving Kanye West happened in the recent times?",
"sources": ["reddit", "x", "hackernews", "youtube", "tiktok", "instagram"],
"weight": 1.0
},
{
"label": "album",
"search_query": "kanye west bully album",
"ranking_query": "How was Kanye West's BULLY album received?",
"sources": ["youtube", "reddit", "tiktok", "instagram"],
"weight": 0.8
},
{
"label": "reactions",
"search_query": "kanye west bully review reaction",
"ranking_query": "What are the reviews and reactions to Kanye West's BULLY?",
"sources": ["youtube", "tiktok", "reddit"],
"weight": 0.6
}
]
}
Rules for your plan:
search_query should be concise and keyword-heavy - match how content is TITLED on platformsranking_query should read like a natural language questionAvailable sources (include ALL in primary subquery): reddit, x, youtube, tiktok, instagram, hackernews, polymarket. Optional: bluesky, truthsocial, threads, pinterest, grounding (web search - only if user has Brave/Exa/Serper key), digg (Digg clusters - only if digg-pp-cli is on PATH)
Intent → freshness_mode mapping:
strict_recentevergreen_okbalanced_recentIntent → cluster_mode mapping:
storydebatemarketworkflownoneStore your plan as QUERY_PLAN_JSON - you'll pass it to the script in the next step.
STOP. Before invoking social-researching.py, verify ALL of the following are true for this turn:
QUERY_PLAN_JSON with 2-4 subqueries). These are NOT optional. If either was skipped, return to that step now.--auto-resolve to the command instead. Do not attempt Steps 0.55 / 0.75 without WebSearch.--emit=compact. --emit md is a debugging/inspection mode and is DISALLOWED as the primary user-facing flow. If you find yourself about to run --emit md, stop and switch to --emit=compact.--plan 'QUERY_PLAN_JSON' plus every resolved handle/subreddit/hashtag/creator flag from Step 0.55. Omit only flags whose value was not resolvable.Degraded path (missing any of the above on a WebSearch platform) is a known regression shape. It produces bland 4-bullet summaries instead of rich synthesis. Do not take it.
Step 1: Run the research script WITH your query plan (FOREGROUND)
CRITICAL: Run this command in the FOREGROUND with a 5-minute timeout. Do NOT use run_in_background. The full output contains Reddit, X, AND YouTube data that you need to read completely.
IMPORTANT: Pass your QUERY_PLAN_JSON via the --plan flag. This tells the Python script to use YOUR plan instead of calling Gemini.
IMPORTANT: Include --x-handle={RESOLVED_HANDLE} in the command. For comparison mode: Pass --x-handle={TOPIC_A_HANDLE} to the first pass, --x-handle={TOPIC_B_HANDLE} to the second pass, and both to the head-to-head pass. Also include --subreddits={RESOLVED_SUBREDDITS}, --tiktok-hashtags={RESOLVED_HASHTAGS}, --tiktok-creators={RESOLVED_TIKTOK_CREATORS}, and --ig-creators={RESOLVED_IG_CREATORS} from Step 0.55. Omit any flag where the value was not resolved (empty).
# SKILL_DIR = absolute path of the directory containing THIS SKILL.md you just Read.
# Substitute the actual path below — your harness told you where this file lives via
# the Read tool result. Examples:
# Read ~/.claude/skills/social-researching/SKILL.md → SKILL_DIR=$HOME/.claude/skills/social-researching
# Read ~/.codex/skills/social-researching/SKILL.md → SKILL_DIR=$HOME/.codex/skills/social-researching
# Read ~/.claude/plugins/cache/social-researching-skill/social-researching/3.3.2/skills/social-researching/SKILL.md
# → SKILL_DIR=$HOME/.claude/plugins/cache/social-researching-skill/social-researching/3.3.2/skills/social-researching
# scripts/social-researching.py is always a direct child of SKILL_DIR (every install layout
# packages SKILL.md and scripts/ as siblings).
SKILL_DIR="<absolute path of the directory containing the SKILL.md you Read>"
if [ ! -f "$SKILL_DIR/scripts/social-researching.py" ]; then
echo "ERROR: scripts/social-researching.py not found under SKILL_DIR=$SKILL_DIR" >&2
echo "Re-check the directory of the SKILL.md you Read and substitute it as SKILL_DIR above." >&2
exit 1
fi
"${LAST30DAYS_PYTHON}" "${SKILL_DIR}/scripts/social-researching.py" $ARGUMENTS --emit=compact --save-dir="${LAST30DAYS_MEMORY_DIR}" --save-suffix=v3
If you ran Steps 0.55 and 0.75 (agent planning), pass the plan via a tmpfile and add the targeting flags:
# Write QUERY_PLAN_JSON to a tmpfile before the engine invocation above.
# parse_plan() reads file paths transparently; this avoids inline-JSON
# shell-quoting hazards (apostrophes in search_query / ranking_query
# strings break single-quoted command-line JSON). Trailing XXXXXX (no
# .json suffix) for BSD/macOS portability — BSD mktemp only substitutes
# X's at the end of the template.
QUERY_PLAN_FILE=$(mktemp "${TMPDIR:-/tmp}/social-researching-plan.XXXXXX")
trap 'rm -f "$QUERY_PLAN_FILE"' EXIT
cat > "$QUERY_PLAN_FILE" <<'PLAN_EOF'
{QUERY_PLAN_JSON_FROM_STEP_0.75}
PLAN_EOF
Then add to the engine command:
--plan "$QUERY_PLAN_FILE" (path to the file you just wrote)--x-handle={RESOLVED_HANDLE} (from Step 0.5)--subreddits={RESOLVED_SUBREDDITS} (from Step 0.55)--tiktok-hashtags={RESOLVED_HASHTAGS} (from Step 0.55)--tiktok-creators={RESOLVED_TIKTOK_CREATORS} (from Step 0.55)--ig-creators={RESOLVED_IG_CREATORS} (from Step 0.55)--github-user={RESOLVED_GITHUB_USER} (from Step 0.5b, person topics only)--github-repo={RESOLVED_GITHUB_REPOS} (from Step 0.5c, product/project topics only)If you skipped Steps 0.55 and 0.75 (no WebSearch -- OpenClaw, Codex, etc.), add:
--auto-resolve (the engine will use Brave/Exa/Serper to discover subreddits and context before planning)If you skipped Steps 0.55 and 0.75 (no WebSearch), run the command as-is. The Python engine will plan internally.
Use a timeout of 300000 (5 minutes) on the Bash call. The script typically takes 1-3 minutes.
The script will automatically:
Read the ENTIRE output. It contains EIGHT data sections in this order: Reddit items, X items, YouTube items, TikTok items, Instagram Reels items, Hacker News items, Polymarket items, and WebSearch items. If you miss sections, you will produce incomplete stats.
YouTube items in the output look like: **{video_id}** (score:N) {channel_name} [N views, N likes] followed by a title, URL, transcript highlights (pre-extracted quotable excerpts from the video), and an optional full transcript in a collapsible section. Quote the highlights directly in your synthesis. When YouTube items also include top comments (enabled via youtube_comments), quote those too with their like counts - they capture how viewers reacted to the video. Transcript highlights and top comments are complementary signals; use both when present. Attribute transcript quotes to the channel name, comment quotes to the commenter. Count them and include them in your synthesis and stats block.
TikTok items in the output look like: **{TK_id}** (score:N) @{creator} [N views, N likes] followed by a caption, URL, hashtags, and optional caption snippet. Count them and include them in your synthesis and stats block.
Instagram Reels items in the output look like: **{IG_id}** (score:N) @{creator} (date) [N views, N likes] followed by caption text, URL, and optional transcript. Count them and include them in your synthesis and stats block. Instagram provides unique creator/influencer perspective - weight it alongside TikTok.
After the script finishes, do WebSearch to supplement with blogs, tutorials, and news.
Run 2-3 post-engine WebSearch supplements. This is a SEPARATE budget from Step 0.55 pre-research. Pre-research WebSearches DO NOT count against this budget.
The supplement budget and the Step 0.55 pre-research budget are distinct. Step 0.55 resolves handles/subreddits/hashtags (typically 2-4 searches). Step 2 supplements fill blog/tutorial/news depth the social engine did not surface. Counting one toward the other is the most common reason supplement depth collapses to 1 search and the synthesis loses critical-reaction and long-form analysis context.
For ALL modes, do WebSearch to supplement (or provide all data in web-only mode).
Choose search queries based on QUERY_TYPE:
If RECOMMENDATIONS ("best X", "top X", "what X should I use"):
best {TOPIC} recommendations{TOPIC} list examplesmost popular {TOPIC}