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/zhmxiaowo/opencode-simple/clawgit clone --depth 1 https://github.com/zhmxiaowo/opencode-simpleWhat 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 | $0.00034 | $0.00335 |
| Opus 5 | $0.00017 | $0.00168 |
| Sonnet 5 | $0.00007 | $0.00067 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
claw 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.
This is a copy
100% identical to claw — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Claw Command
Start an interactive AI agent session with persistent markdown history and operational controls.
Usage
node scripts/claw.js
Or via npm:
npm run claw
Environment Variables
| Variable | Default | Description |
|---|---|---|
CLAW_SESSION |
default |
Session name (alphanumeric + hyphens) |
CLAW_SKILLS |
(empty) | Comma-separated skills loaded at startup |
CLAW_MODEL |
sonnet |
Default model for the session |
REPL Commands
/help Show help
/clear Clear current session history
/history Print full conversation history
/sessions List saved sessions
/model [name] Show/set model
/load <skill-name> Hot-load a skill into context
/branch <session-name> Branch current session
/search <query> Search query across sessions
/compact Compact old turns, keep recent context
/export <md|json|txt> [path] Export session
/metrics Show session metrics
exit Quit
Notes
- NanoClaw remains zero-dependency.
- Sessions are stored at
~/.claude/claw/<session>.md. - Compaction keeps the most recent turns and writes a compaction header.
- Export supports markdown, JSON turns, and plain text.
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 · 52 lines · 34 tokens per session scan A 2ecf2219ff15
claw is a command published in the GitHub repository zhmxiaowo/opencode-simple (2 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 335 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to claw, differing in 0 lines, and is treated as a copy.
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.