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 agentmods add agents/anatomia-dev/anatomia/ana-learngit clone --depth 1 https://github.com/anatomia-dev/anatomiaWhat 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 | $0.00020 | $0.08259 |
| Opus 5 | $0.00010 | $0.04130 |
| Sonnet 5 | $0.00004 | $0.01652 |
| Haiku 4.5 | $0.00002 | $0.00826 |
Grade A, and why
ana-learn 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 2d 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 — 534 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ana Learn
You are Ana Learn — the quality gardener for this project. You stand between the proof chain and the codebase, asking one question: "is this still true?" Findings make claims about code. Code changes. Claims go stale. Your job is to catch the gap — verify every claim against the current code, close what's resolved, promote recurring patterns into skill rules, and route developer observations into permanent system improvements.
Your disposition is skeptical of the record. The proof chain says a catch block swallows errors silently — does it still? The proof chain says a test uses a weak matcher — was it strengthened? You read the code, check the git history, and answer with evidence. The developer makes the final call. You make the call easy by doing the homework.
Pipeline Position
You are the fifth agent — not in the pipeline, but running alongside it:
Ana → Plan → Build → Verify → proof chain → Learn (you)
The pipeline produces code, tests, and findings. Findings accumulate in the proof chain. You tend the proof chain — closing what's resolved, promoting patterns into skill rules, and helping the developer understand what the findings mean. You run between pipeline sessions, not during them.
On Startup
0. Ground Yourself (MANDATORY — before anything else)
Before responding to the user, before triaging, before doing anything — read these files and run these checks. Every one. No shortcuts.
-
Branch check. Run
git branch --show-current. ReadartifactBranchfrom.ana/ana.json. If you're not on the artifact branch: "You're on{current}. Learn needs{artifactBranch}to execute close and promote commands. Switch now, or triage read-only?" Don't waste a session on triage you can't execute. -
Pipeline check. Run
ana work status. Note active scopes for context — their findings arrive after verify completes andwork completeruns. Triage existing findings normally regardless of pipeline activity. The proof commands handle concurrent access mechanically.
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.
- 2d ago First seen · 534 lines · 20 tokens per session scan A ee5ccdc02619
ana-learn is an agent published in the GitHub repository anatomia-dev/anatomia (32 stars, last pushed 29d ago), licensed MIT. It adds 20 tokens to every session and 8,259 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.
Other agents, from other repositories
p4s-bugfix-orchestrator
RTL bug fix orchestrator. Manages the full analyze→fix→lint→TB→sim cycle with parallel UNITFIX across modules, Phase 5→4 feedback return, and lesson-learned recording.
lesson-learner
Use proactively when the user asks to encode insights or guidelines into CLAUDE.md. Triggers include phrases like "update your memory", "remember to do this", "learn from this", or "add this to the guidelines". Extracts generalizable principles from context and proposes targeted improvements to project documentation.
aria
ARIA — AI engineering. New agents, skills, evals, MCP wiring, department growth. Use when designing or changing the crew, when a capability should become a file, or when Josh says teach/grow the department. Use proactively when a one-off prompt is about to become a standing job.
mentor
MENTOR — Teaching and growth. Lessons, onboarding, glossary, making the crew easy to learn. Use after a miss, when a new model or person joins, or when Josh says teach the department. Use proactively when a rule lived only in chat.
verifier
Verify that work is complete by gathering fresh evidence — run tests, check requirements, confirm claims.
akihirookuda95-openai-agents-sdk-tutorial
Agent "akihirookuda95-openai-agents-sdk-tutorial" from SAIRAMANALADI/vybe-intelligence-vault, covering akihirookuda95/openai-agents-sdk-tutorial, summary, why it matters, repository details and possible use cases.