Borrowing it
Nothing to install: this file belongs to AICMO/Autonomous-Agent-X-Bluesky. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AICMO/Autonomous-Agent-X-Bluesky/main/.claude/skills/discovery/SKILL.mdgit clone --depth 1 https://github.com/AICMO/Autonomous-Agent-X-BlueskyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aicmo/autonomous-agent-x-bluesky/discovery)<a href="https://agentmods.dev/skills/aicmo/autonomous-agent-x-bluesky/discovery"><img src="https://agentmods.dev/badge/skills/aicmo/autonomous-agent-x-bluesky/discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aicmo/autonomous-agent-x-bluesky/discovery"><img src="https://agentmods.dev/badge/skills/aicmo/autonomous-agent-x-bluesky/discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 80 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.01695 |
| Opus 5 | $0.00014 | $0.00847 |
| Sonnet 5 | $0.00006 | $0.00339 |
| Haiku 4.5 | $0.00003 | $0.00169 |
Grade A, and why
discovery scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 Engagement Opportunities While Reading
X outbound replies fail 100% of the time via X API (403) — confirmed Week 9 audit: 62/62 failed. Do NOT spend turns looking for X posts to reply to outbound.
For X: Reply-to-own only (100% success rate). Look for tweet IDs from recent workflow runs:
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
For Bluesky (especially during X outages): Outbound replies ARE allowed. While reading top voices and doing research, note any Bluesky posts on pillar topics that are <6h old and worth replying to. Capture the AT URI for the reply file. See "Reply Targets: Platform-Specific Rules" below.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 164 lines · 28 tokens per session scan A 9c8cdbe06fa1
discovery is a skill published in the GitHub repository AICMO/Autonomous-Agent-X-Bluesky (11 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 1,695 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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