discovery

name	discovery
description	Gather context about repo owner, products, and domain. Build domain expertise by reading top voices. Use proactively before creating content.
user-invocable	false

Discovery Skill

Find voices, read content, build expertise

Owner & Product Discovery

gh api users/{owner}  # Returns: name, bio, blog, twitter_username, company, location

Additional: Check ME.md, GitHub profile README, pinned repos, blog/website.

Products: gh api users/{owner}/repos?sort=updated → read READMEs, check live demos.

Staleness: Owner profile >30 days = refresh. Products = weekly.

---

Domain Trends Research

Web search for current data:

• "X Twitter growth strategies {current_year}"
• "AI developer Twitter accounts successful"
• "{niche} best practices {current_year}"

Refresh each session (trends change constantly).

---

Build Domain Expertise

1. Top Voices List (~20 voices)

Find via web search ("best {niche} blogs", "top {niche} Twitter accounts"), follow-the-follows, curated lists.

Store in agent/memory/research/top-voices.md:

## @handle / Name
- Platform: X / Blog / Newsletter
- Focus: [niche/angle]
- Why follow: [value]

Refresh monthly.

2. Reading Routine

Each session, pick 2-3 voices. Read with intent — look for: key arguments, data points, emerging trends, contrarian takes, gaps.

Cadence: Top 5 voices every session (skim). Voices 6-20 weekly rotation.

3. Capture Reply-to-Own Opportunities While Reading

Outbound replies to others may fail via X API (403 restriction). Reply-to-own is the reliable engagement tactic.

What works: Reply-to-own (high success rate). While reading, look for tweet IDs from recent workflow runs to reply to:

gh run list --workflow=process-outputs.yml --limit 1 --json databaseId,createdAt
gh run view <run_id> --log 2>/dev/null | grep 'INFO Response:' | head -5

Prioritize reply-to-own files. Test outbound replies cautiously.

4. Turn Reading Into Content

Reading output	Content use
Key takeaway	Authority post (your angle)
Disagreement	Contrarian take
Data point	Credibility boost
Trend spotted	First-mover post
Content gap	Fill it — own the topic

Rules: Never plagiarize. Credit sources. Aim for 1 reading-inspired post per 3-5 articles. Goal = informed originality, not summary.

5. Graduate Research Into Skills

Research in agent/memory/research/ helps THIS session. Research in .claude/skills/ helps ALL future sessions.

When to graduate: Substantial research (15+ sources), validated and actionable, broadly applicable.

Follow "Skill Development (High Bar)" from CLAUDE.md. NOT every session — reserve for validated, substantial findings.

---

Owner's Open Source Scan

Periodically scan the owner's public repos for promotable content. This feeds the publishing skill's OS promotion allocation.

When: Once per session (during research phase), or when looking for content ideas.

How:

1. Read ME.md → find the owner's GitHub profile URL under "Open Source (Promotable)"
2. WebFetch the profile page → discover public repos, orgs, and pinned projects
3. WebFetch each discovered org page → list their repos too
4. For repos that look promotable, fetch their README to find live output links and descriptions

What makes something promotable right now:

A. Live outcomes (highest priority) — Real content/services produced by agents. These are proof, not promises.

• Read ME.md for known live outcome URLs, then WebFetch each to check for recent articles, digests, posts
• A specific recent article is 10x more promotable than a generic "we have a blog" link
• Frame as: "This was written by an AI agent today" + link to the actual piece
• Also check repo READMEs for new live output URLs — new pipelines may launch anytime

B. Repos with live outputs — Code that powers running services. Link both the code AND the output.

C. Star milestones — Crossing 5, 10, 25, 50, 100 stars.

D. Trending topic overlap — Repo solves something people are discussing this week. Strongest combo: trend + live proof.

E. New repos — Launch announcements get one-time boost.

What to capture: For each candidate: what it does (1 line), proof it works (live output links + recent specific content), hook angle (why someone cares today). Store in agent/memory/research/os-promo-candidates.md.

Cross-reference with trends: Trending topic aligns with an owner repo or outcome? Post about the trend, link the repo/outcome as "we built this, and here's it running."

Reference: GitHub accounts and known live outcomes in ME.md under "Open Source (Promotable)".

---

Reply Targets: Reply-to-Own is Most Reliable

Outbound replies to others often fail at X API (403) — particularly to accounts that haven't engaged with you. Reply-to-own is the primary engagement tactic.

Reply-to-own targets:

1. Check agent/state/current.md for recent tweet IDs
2. Or run: gh run list --workflow=process-outputs.yml --limit 1 --json databaseId → gh run view <id> --log | grep 'INFO Response:'

Only create reply files when:

• You have the numeric tweet ID of YOUR OWN recent tweet
• The topic has enough depth to add value in a reply
• Queue is < 15 on all platforms
• The original tweet was posted within 30 min (for 150x multiplier) — check run completion time first

---

Storage Structure

• agent/memory/research/top-voices.md — curated voice list
• agent/memory/research/reading-notes/ — per-article notes (optional)
• agent/memory/research/expertise/ — synthesized domain knowledge