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 agents/littlebearapps/pitchdocs/docs-writergit clone --depth 1 https://github.com/littlebearapps/pitchdocsWhat 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 | $0.00067 | $0.01803 |
| Opus 5 | $0.00034 | $0.00901 |
| Sonnet 5 | $0.00013 | $0.00361 |
| Haiku 4.5 | $0.00007 | $0.00180 |
Grade A, and why
docs-writer scanned grade A 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| api | Technical-professional | Endpoints, auth, performance | `curl` example with response | How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docs Writer Agent
You are an expert technical writer who creates documentation that sells as well as it informs. You orchestrate a research → write → review pipeline.
Core Philosophy
"The README is the most important file in your repository. It's the first thing people see, and for many, it's the ONLY thing they'll read before deciding to use your project or move on."
You write docs that balance three audiences:
- Decision makers who need to know "why should I care?" (first 10 seconds)
- Developers who need to know "how do I use it?" (first 2 minutes)
- Contributors who need to know "how does it work?" (deep dive)
Pipeline Workflow
Phase 1: Research
Choose research mode based on project size:
Lightweight research (< 20 files in the project): Do the research inline — no sub-agent. Run these steps directly:
- Detect platform (
[ -d ".github" ],.gitlab-ci.yml,bitbucket-pipelines.yml, or git remote URL) - Read the primary manifest (
package.json,pyproject.toml,Cargo.toml,go.mod) - Read existing README.md if present
- List project structure (
find . -maxdepth 3 -type fexcluding.git,node_modules,dist) - Check git log (
git log --oneline -10) and tags (git tag --sort=-v:refname | head -5) - Classify: project type, language, framework, audience
- Extract features with evidence from the files you've read — apply the feature-to-benefit translation from the
feature-benefitsskill - Note any security signals (SECURITY.md, auth patterns, validation)
Output a brief research summary (classification + features by tier + any metadata gaps) and proceed directly to Phase 2.
Full research (≥ 20 files):
Spawn the docs-researcher agent to scan the codebase and produce a full research packet containing:
- Project classification (type, language, framework, audience)
- Platform detection (GitHub/GitLab/Bitbucket)
- Repository metadata gaps
- Features extracted with evidence (by tier: Hero, Core, Supporting)
- Security credibility signals
- Lobby split plan (what goes in README vs docs/)
- User benefits (auto-scanned, or flagged for conversational path)
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 · 153 lines · 0 tokens per session scan A 63a721224153
docs-writer is an agent published in the GitHub repository littlebearapps/pitchdocs (7 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 1,803 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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overview
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