pulse

name	pulse
description	Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
license	MIT
metadata	{"source_spec":"megaprompts/01-pulse-megaprompt.md","build_pattern":"Path B (direct conversion)","research_pack_convention":"Agent Integrity Rules block preserved verbatim per PR #657 audit","version":"1.0.0"}

Pulse — Multi-Source Recency Research

Portability: Works in both Claude Code CLI and Claude.ai. The optional X/Twitter phase requires browser automation and is skipped automatically if unavailable.

A recency-oriented research skill that synthesizes what people are saying about a topic across Reddit, Hacker News, the open web, and (optionally) X/Twitter — within a configurable time window. Output is a single coherent briefing with citations, engagement signals, and cross-platform pattern analysis. The skill captures the current conversation, not the canonical reference.

Invocation

Explicit trigger phrases:

• "pulse on [topic]"
• "what's happening with [topic]"
• "what are people saying about [topic]"
• "current conversation about [topic]"
• "take the pulse of [topic]"
• "trending: [topic]"
• "find me info on [topic]"

Also covers: competitor research with recency flavor, trend discovery, tool comparisons, audience sentiment analysis.

Agent Integrity Rules (Research-Pack Convention)

The following rules apply throughout the run. They are inherited from the research-pack convention and locked down by PR #657's cross-skill consistency audit.

• Execution discipline. Phases 1–3 run in parallel (Reddit + HN + Web are independent). Within each phase, sequential calls only. 1 q/sec rate limit per platform. Confirm response received before next call within the same phase.
• Source discipline. Cite only sources returned by this session's tool calls. Training knowledge is labeled [Background — not from search] and excluded from primary findings count.
• Three-count tracking. Queries sent / sources received (shown) / sources cited. Surfaced in the audit log inline in the synthesis section. Use scripts/citation_tracker.py for the deterministic count.
• Retry policy. On failure → wait 3s → retry once → log. After 3 consecutive failures across all sources: stop, alert user, share what was collected. Never deliver an empty file.
• Plan-tier detection. Reddit + HN are unauthenticated public JSON APIs (rate-limited per IP, not per plan). Surface rate-limit signals from response headers when available; degrade gracefully otherwise.

See references/research_pack_conventions.md for the canon and references/parallel_execution_discipline.md for the rate-limit rationale.

Phase 0: Grill-Me Intake (2–4 forcing questions, one at a time)

Dependency-ordered. Each question carries explicit "why I'm asking". Stop condition: max 4.

Q1 (root) — Topic Specificity

What's the topic? State it in 1–2 sentences — be specific. "AI" or "tech" will get you a vague survey; "self-hosted LLM deployment for small teams" or "Claude Code adoption among enterprise engineering orgs" will get you a useful answer.

Why I'm asking: Specificity dictates search quality. Vague topics produce vague briefings. If your topic is broad, I'd rather narrow it now than spend a search budget on noise.

Refuse mush. If the user says "AI", push back once: "What about AI — adoption, safety, capability, regulation, or comparison? Pick an angle." If the user still won't narrow after one push-back, deliver with the explicit "vague topic — survey level, not depth" caveat.

Q2 (depends on Q1) — Angle

What angle matters most? Pick one:

1. Trend — what's accelerating or decelerating
2. Sentiment — what people feel about it
3. Problems — pain points and complaints
4. Opportunities — gaps and unmet needs
5. Comparison — how it stacks up against alternatives

Why I'm asking: The angle dictates which sources weight more (Reddit for sentiment, HN for technical critique, Web for trend coverage) and how I rank the synthesis.

Forcing choice. Recommended default: trend, unless the topic obviously calls for a different angle.

Q3 (always) — Time Window

Time window: 7 / 14 / 30 / 60 / 90 days? Default is 30.

Why I'm asking: 7 days catches breaking conversation; 90 days catches sustained narrative shift. Pick based on how recent the news matters.

Forcing choice with default.

Q4 (depends on Q1) — Platform Scope

Any platform to skip? By default I'll cover Reddit + Hacker News + open web, plus X/Twitter if browser automation is available. Skip any you don't care about.

Why I'm asking: Skipping a platform saves search budget. Reddit dominates sentiment; HN dominates technical critique; Web dominates breadth; X dominates breaking conversation. Skip what doesn't fit your angle.

Asked only if Q1 + Q2 suggest some platforms are clearly off-target (e.g., consumer sentiment topic → HN less useful). Otherwise default to "all platforms".

Stop condition: After Q4 (or earlier with dependency skips), commit and start Phase 1. Max 4 questions, never bundle.

Pre-flight

Before any phase fires:

1. Compute the time window with scripts/time_window_calculator.py --window <Nd>. Get back the Unix timestamp for created_at_i> (HN) and the t= parameter (hour|day|week|month|year|all) for Reddit.
2. Generate the output slug with scripts/topic_slug_generator.py --topic "<topic>" --date $(date +%Y-%m-%d). Detect if ${RESEARCH_DIR}/pulse/<slug>-<date>.md already exists; if yes, append -v2 suffix or warn user.
3. Start the three-count audit log with scripts/citation_tracker.py --action start --session pulse-<date>-<slug>. This file at ~/.pulse_sessions/<session>.json persists across the run.

Phase 1: Reddit (parallel with HN + Web)

API: reddit.com/search.json (unauthenticated, public JSON).

Queries (sequential within Reddit, 1 q/sec):

1. sort=top&t=<window>&q=<topic> — top posts in window
2. sort=new&t=<window>&q=<topic> — new posts in window (catches breaking signal)
3. For each of the top 3–5 posts by score: fetch the comments JSON (<post-url>.json?limit=top) for the top 10–20 comments.

Headers / rate limits. Reddit rate-limits by IP, not plan. Throttle to 1 q/sec. If response has X-Ratelimit-Remaining: 0 or returns 429, wait 3s, retry once. If still failing, fall back to subreddit-restricted search (r/<topic-subreddit>/search.json) or ?raw_json=1.

Record each query: citation_tracker.py --action record_sent --session NAME --query "...". Record received counts: citation_tracker.py --action record_received --session NAME --count N.

Phase 2: Hacker News (parallel with Reddit + Web)

API: Algolia HN search (hn.algolia.com/api/v1/).

Queries (sequential within HN, 1 q/sec):

1. search?query=<topic>&numericFilters=created_at_i><timestamp>&tags=story — stories in window
2. search?query=<topic>&numericFilters=created_at_i><timestamp>&tags=comment — comments in window (catches discussion signal)

Failure handling. If HN returns empty: broaden the query (remove uncommon nouns); if still empty, drop the timestamp filter as last resort and label results "outside window".

HN bias note. HN skews technical / builder. Surface this in synthesis: "HN's voice is implementation-oriented; consumer sentiment will be under-represented here."

Phase 3: Web Search (parallel with Reddit + HN)

Tools: Available web search + fetch (e.g., WebSearch + WebFetch).

Query strategy (sequential within Web, 1 q/sec):

1. Trusted publishers — "<topic>" site:nytimes.com OR site:wsj.com OR site:wired.com OR site:theverge.com OR site:techcrunch.com after:<date>
2. Recent reviews — "<topic>" review <year> or "<topic>" "honest review" after:<date>
3. Honest-opinion sources — "<topic>" problems OR complaints OR "worth it" after:<date>

Fetch the top 3–5 URLs per query. Truncate at the body, skip cookie/nav markup.

Citation discipline. Every claim in the Web section must trace to a fetched URL. Do NOT cite from snippets alone; fetch first.

Phase 4: X/Twitter (sequential, optional)

Run last. Reasons:

• Most likely to fail / require browser automation
• X content overlaps significantly with Reddit/HN — so it adds delta, not primary signal

Interface (in priority order):

1. Grok if available in the harness
2. X API if authenticated
3. Browser automation if the harness supports it (Claude Code CLI with playwright or similar)
4. Skip with note if none of the above available

Documented behavior:

If Phase 4 is skipped: include the section header ## X/Twitter with body Skipped — [reason: no browser automation / no Grok / no X API]. Do NOT pretend to have data.

Synthesis (Cross-Platform Patterns)

After Phases 1–4 complete (or Phase 4 skipped), produce the synthesis:

1. Consensus signals — points where 3+ platforms agree (highest confidence). Tag each with cited source URLs.
2. Controversy signals — points where platforms disagree. Note who says what.
3. Pain points — recurring complaints across sources (esp. Reddit + Web).
4. Excitement signals — recurring enthusiasm (esp. HN + X if available).
5. Emerging trends — first-time mentions in newest posts but absent from older ones (compare sort=new vs sort=top).
6. Gaps — what's notably absent that you'd expect to find.

For each pattern, cite the source URLs that support it. Use citation_tracker.py --action record_cited --session NAME --url "..." per citation.

See references/cross_platform_synthesis.md for detection heuristics.

Output

Save to file AND paste in chat:

File: ${RESEARCH_DIR}/pulse/<topic-slug>-<YYYY-MM-DD>.md (path from topic_slug_generator.py).

Format:

# [TOPIC] — Pulse (Last [N] Days)
*Generated: [DATE] | Angle: [Q2 choice]*

## TL;DR
[2-3 sentences max]

## Reddit
### Top Posts
- **[Title]** (r/sub) — [score, comments] — [summary] — [URL]
### What Reddit Is Saying
[Narrative paragraph]

## Hacker News
### Notable Stories
- **[Title]** — [points, comments] — [summary] — [URL]
### What HN Is Saying
[Narrative paragraph; note HN's technical/builder bias]

## Web
### Key Sources
- **[Title]** ([Publication]) — [takeaway] — [URL]
### What the Web Is Saying
[Narrative paragraph]

## X/Twitter (if available)
[Cleaned response, with handles/references preserved]
[Or: "Skipped — [reason]"]

## Cross-Platform Patterns
[Highest-confidence signals across sources]

## Key Takeaways
- [3-5 bullets]

## Content Angles (if applicable)
[2-3 specific angles supported by the data]

---
*Audit:* Queries sent: N (Reddit: a, HN: b, Web: c, X: d|skipped).
Sources received: M. Sources cited: K. Training knowledge: 0 ([Background] excluded from count).

Error Handling

Failure	Behavior
Topic is too vague (Q1)	Refuse to start. Re-ask Q1 once with examples. After 1 push-back, deliver with "vague topic" caveat.
Reddit blocks / rate-limits	Try ?raw_json=1 or fall back to subreddit-restricted search. Honor 3s-retry.
HN returns empty	Broaden query, drop timestamp filter as last resort, label results "outside window".
Web search returns nothing useful	Note in output; don't fabricate sources.
Browser automation unavailable	Skip Phase 4 with documented note.
WebFetch times out	Use what loaded, mark the source as "truncated".
3 consecutive failures across sources	Stop. Return what was collected with explicit "stopped early" note. Do NOT deliver empty file.
All sources fail	Return error with diagnostic info. Do NOT deliver empty file.

Tooling

Script	Role
scripts/time_window_calculator.py	Compute Unix timestamps + Reddit t= parameter from window string (30d, 7d, etc.). Deterministic from datetime.now().
scripts/citation_tracker.py	JSON-backed three-count audit log (sent / received / cited) at ~/.pulse_sessions/<session>.json.
scripts/topic_slug_generator.py	Filesystem-safe slug + duplicate-date detection for output paths.

References

• references/research_pack_conventions.md — Agent Integrity Rules canon (7+ sources: Google SRE, Reddit API docs, Algolia HN docs, exponential-backoff literature, citation discipline)
• references/cross_platform_synthesis.md — consensus / controversy / pain detection across platforms (7+ sources)
• references/parallel_execution_discipline.md — 1 q/sec rationale + plan-tier signals (7+ sources)

Anti-Patterns To Reject

• Starting any search before the user commits to topic specificity (Q1)
• Batching intake questions instead of one at a time
• Hardcoded URLs that won't survive API changes (note format, explain may evolve)
• Specific person / brand references in the skill body
• Tight coupling to one X/Twitter interface
• Missing fallback behavior on source failure
• "Just use [specific tool]" without explaining what the tool does
• Citing training knowledge in the cited count
• Fabricating sources to fill out a section

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Version: 1.0.0 Source spec: megaprompts/01-pulse-megaprompt.md Build pattern: Path B (direct conversion). Re-grill with /cs:grill-with-docs if drift between spec and implementation surfaces.