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/drag88/claude-dev-framework/learngit clone --depth 1 https://github.com/drag88/claude-dev-frameworkWhat 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 | $0.00036 | $0.01328 |
| Opus 5 | $0.00018 | $0.00664 |
| Sonnet 5 | $0.00007 | $0.00266 |
| Haiku 4.5 | $0.00004 | $0.00133 |
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 yesterday.
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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cdf:learn
Universal skill learning command. One command for capture, viewing, removal, and consolidation.
Usage
/cdf:learn "preference or correction" # Capture (infers skill)
/cdf:learn skill-name "preference or correction" # Capture (explicit skill)
/cdf:learn status # Overview across all skills
/cdf:learn show skill-name # Show entries for a skill
/cdf:learn remove skill-name number # Remove specific entry
/cdf:learn reset skill-name # Clear all for a skill
/cdf:learn consolidate skill-name # Deduplicate and merge
Subcommands
Capture (default)
/cdf:learn "preference" or /cdf:learn skill-name "preference"
-
Determine target skill:
- Explicit name provided → use it (validate exists in
skills/) - No name → infer from conversation context (which skill was most recently active)
- Inference fails → ask: "Which skill?" and list recently active ones
- No skill applies → log to Claude's auto-memory instead (this command is for skill-specific learning)
- Explicit name provided → use it (validate exists in
-
Classify into section:
- Do: Positive instructions ("always X", "prefer X", "use X")
- Don't: Negative instructions ("never X", "avoid X", "stop doing X")
- Style: Tone, format, voice preferences ("be more concise", "use parentheses not dashes")
-
Write to
skills/{skill-name}/learned.md:- Create from template if it does not exist
- Append dated entry:
- {description} (YYYY-MM-DD) - Silent. Do NOT announce that feedback was logged.
-
Check cap: If 20+ entries, notify: "Run
/cdf:learn consolidate {skill-name}to merge."
Status
/cdf:learn status
Scan skills/*/learned.md across all skill directories. Show:
Learned preferences across skills:
frontend-design: 9 entries (last updated 2026-03-28)
coding-standards: 2 entries (last updated 2026-03-25)
frontend-patterns: (no learned.md)
Total: 11 entries across 2 skills
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.
- yesterday First seen · 182 lines · 36 tokens per session scan A 2fbef81e243b
learn is a command published in the GitHub repository drag88/claude-dev-framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,328 once invoked, about $0.0002 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 2026-08-31.
Other commands, from other repositories
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.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.