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/fradser/dotclaude/helpgit clone --depth 1 https://github.com/FradSer/dotclaudeWhat 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 | $0.00007 | $0.02502 |
| Opus 5 | $0.00003 | $0.01251 |
| Sonnet 5 | $0.00001 | $0.00500 |
| Haiku 4.5 | $0.00001 | $0.00250 |
Grade B, and why
help 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 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
grep '^iteration:' .claude/autoresearch.local.md How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch Plugin Help
Explain the following to the user:
What is autoresearch?
A Claude Code plugin inspired by karpathy/autoresearch — an autonomous research loop where Claude acts as a researcher, continuously editing one artifact, running a scorer, logging results, and iterating overnight without human intervention.
Unlike the original (which is hardwired to ML training), this plugin is domain-agnostic: you supply the editable artifact, the scorer, and the optimization direction, so the loop works on any problem that reduces to "edit something, run a command that prints one number, keep the change if the number improved." ML training is just one configuration of it.
Core idea:
- Point Claude at one artifact it may edit and one scorer command that prints a number
- Let it experiment autonomously — make a change, score it, keep or discard, repeat
- Wake up in the morning to a log of experiments and (hopefully) a better score
How the loop runs (this plugin's mechanism):
Rather than an external shell loop, this plugin uses a Claude Code Stop hook. Every time Claude tries to end its turn, the hook intercepts the exit and re-injects the same research prompt — so Claude keeps experimenting in one continuous session. Claude sees its previous work in git history and results.tsv, building incrementally toward a better score. The hook stops the loop when a configured bound is hit (max experiments, wall-clock budget, or a completion promise).
This is the same spirit as the "ralph-loop" idea behind the original project (re-feeding a prompt until done), adapted to Claude Code's hook system instead of a while loop over a CLI.
Requirements
- A git repository (the loop runs in a dedicated git worktree so your main checkout, current branch, and even a dirty tree are never touched)
- At least one bound:
--max-experimentsand/or--max-wall-clock - An evaluator: a
--score-cmd(prints a number as its last stdout line) and/or a--check-cmd(objective pass/fail gate, exit 0 = pass) - Whatever runtime the evaluator needs (interpreter, data, GPU, ...) — that is its concern, not the plugin's
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 · 165 lines · 7 tokens per session scan B 55ae71698606
help is a command published in the GitHub repository FradSer/dotclaude (587 stars, last pushed 20d ago), licensed MIT. It adds 7 tokens to every session and 2,502 once invoked, about $0.0000 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 2026-08-30.
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.