Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add strikersam/autonomous-ai-agency --skill replay-learningsgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/replay-learnings)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/replay-learnings"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/replay-learnings/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/strikersam/autonomous-ai-agency/replay-learnings"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/replay-learnings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00034 | $0.00713 |
| Opus 5 | $0.00017 | $0.00357 |
| Sonnet 5 | $0.00007 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade B, and why
replay-learnings scanned grade B with 1 finding 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 12d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
cat .claude/state/learnings.md How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: replay-learnings
When to Use
Run this skill:
- At the start of a new session before touching any code
- Before working on a module you've touched before
- After
cooldown-resumeloads the checkpoint (learnings provide extra context) - When the user asks "what did we learn about X?"
Instructions
Step 1 — Read the learnings file
cat .claude/state/learnings.md
If the file doesn't exist, skip to Step 3 (no learnings yet).
Step 2 — Filter relevant learnings
From all entries, extract those relevant to the current task. Match by:
- Module name — does the learning mention a file you're about to touch?
- Operation type — does the learning apply to commits, auth, routing, tests?
- Keyword overlap — does the learning's situation match the current context?
Present the relevant learnings as a short list:
Relevant learnings for this task:
- [2026-03-15] Never use git add -A — stage specific files only.
- [2026-03-22] Always read router/CLAUDE.md before touching model_router.py.
- [2026-04-01] risky-module-review is required for admin_auth.py even for 1-line changes.
Step 3 — Check recent checkpoint history
tail -20 .claude/state/checkpoint.jsonl 2>/dev/null || echo "No checkpoint history."
Look for patterns:
- Steps that were retried (same step_id appearing twice)
- Steps that failed
- Any "partial" status entries
Step 4 — Surface blockers from previous session
cat .claude/state/NEXT_ACTION.md 2>/dev/null || echo "No next action file."
Check for documented blockers that are still unresolved.
Step 5 — Apply relevant rules
Before starting work, briefly confirm which rules apply:
Applying rules from learnings:
✓ Will stage specific files (not git add -A)
✓ Will read router/CLAUDE.md before touching routing code
✓ Will invoke risky-module-review for auth module
Learnings File Doesn't Exist?
This is expected on a fresh clone. The file is created by learn-rule and wrap-up.
Start working, and the learnings will accumulate over time.
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.
- 12d ago First seen · 103 lines · 34 tokens per session scan B 03025d4c8a3f
replay-learnings is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 713 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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