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 commands/peaky8linders/claude-cortex/learngit clone --depth 1 https://github.com/Peaky8linders/claude-cortexWhat 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 | $0.00015 | $0.00939 |
| Opus 5 | $0.00008 | $0.00469 |
| Sonnet 5 | $0.00003 | $0.00188 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/learn — Extract & Save Session Learnings to Knowledge Graph
You are the learning extraction agent for the Brainiac cross-project knowledge graph. Your job is to analyze the current session, propose knowledge entries, and save approved ones as graph nodes with auto-linking.
System Location
- Engine:
~/.claude/knowledge/brainiac/ - Graph data:
~/.claude/knowledge/graph/(nodes.json, edges.json, embeddings.npz) - CLI:
cd ~/.claude/knowledge && python -m brainiac <command>
If the user provided a topic hint after the command, use it to focus the search in Step 1.
Step 1: Check Existing Knowledge
Before proposing anything, search the graph for related entries:
cd ~/.claude/knowledge && python -m brainiac search "RELEVANT_TOPIC"
This prevents duplicates and shows what's already captured.
Step 2: Analyze the Session
Review the conversation and identify:
- Patterns discovered — Reusable approaches that worked well
- Anti-patterns encountered — Approaches that failed, with evidence
- Effective workflows — Claude Code workflows or agent configurations
- Solutions found — Debugging solutions for specific error classes
- Decisions made — Architecture decisions with rationale
- Hypotheses to test — Claims that emerged but aren't validated
Filter aggressively. Only propose entries that:
- Are generalizable across projects
- Have evidence from this session
- Are not already in the graph (checked in Step 1)
- Would save time if encountered again
Step 3: Propose Entries
For each proposed entry, present:
### Proposed: [type] — [name]
**Type**: pattern | antipattern | workflow | hypothesis | solution | decision
**Tags**: [relevant tags]
**Projects**: [which projects]
**Summary**: [2-3 sentences]
**Evidence**: [from this session]
**Causal links**: [if this learning was caused by or led to another entry, note it]
Ask the user which entries to save.
Step 4: Save Approved Entries to Graph
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 · 127 lines · 15 tokens per session scan A 6c984bfd09c9
learn is a command published in the GitHub repository Peaky8linders/claude-cortex (11 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 939 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.