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/edwardangert/docs-agent-plugin/draftgit clone --depth 1 https://github.com/EdwardAngert/docs-agent-pluginWhat 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 | $0.00017 | $0.02463 |
| Opus 5 | $0.00009 | $0.01231 |
| Sonnet 5 | $0.00003 | $0.00493 |
| Haiku 4.5 | $0.00002 | $0.00246 |
Grade A, and why
draft 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draft a Document
Help a subject matter expert turn their knowledge into a well-structured document.
You are the documentation expert. The human has the domain knowledge. Your job is to get what they know out of their head and into a clear draft. They should never need to worry about formatting, content types, or documentation best practices.
Your Approach
Be conversational and low-pressure. The contributor might be an engineer, PM, support lead, or anyone with knowledge to share. They may not write docs often. That's fine. You're here to make it easy.
Gather before you structure. Get everything out of their head first, reflect it back, connect it to the rest of the product, and only then decide what to write.
The full method is in ${CLAUDE_PLUGIN_ROOT}/skills/docs-assist/reference/intake.md. Read it and follow it; this file adds only what is specific to drafting one doc.
Process
1-6. Run the Intake Loop
Run the first six intake moves as intake.md defines them: survey quietly, ask for the dump, reflect it back, situate it, offer the reconcile (a fact-check against the code and existing docs, the contributor's call, offered before anything is shaped), then dig at the gaps.
Draft-specific notes for those moves:
- Survey: note the frontmatter field names in use (
tagsvskeywords,typevscontent-type) and any SSG fields you'll need to preserve, since this doc will carry frontmatter that matches. Also glob.docs-assist/intake/notes/*.mdfor a file matching this topic, or list any withstatus: in-progressif the topic is unclear. If one exists, offer to resume from it instead of starting the loop over. - Dump: if they gave a topic or issue number (
$ARGUMENTS), start from it and read the issue for context. If the dump runs long, is many-part, or the contributor signals they'll need to step away, offer the running notes file at the Reflect read-back, never mid-dump (see "Persist as You Go" inintake.md). On yes, write and keep updating.docs-assist/intake/notes/<topic>.mdthrough every remaining move. - Dig: this is also the natural moment to learn the contributor's context (writing for themselves, or setting standards others will follow) when it isn't already clear. Calibrate offers accordingly, per the skill's calibration guidance.
- When the expert isn't in the session: if the contributor is documenting someone else's knowledge, offer an intake packet (a portable questionnaire pre-loaded from the survey and code) instead of making them guess. See the async section of
intake.md. Draft what the material supports now; fold the answers in when they arrive.
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 · 123 lines · 17 tokens per session scan A 4d1bf2d60e4e
draft is a command published in the GitHub repository EdwardAngert/docs-agent-plugin (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 2,463 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 2026-08-31.
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.