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 skills/reasvyn/docsgrep/doc-writingnpx skills add reasvyn/docsgrep --skill doc-writinggit clone --depth 1 https://github.com/reasvyn/docsgrepWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/reasvyn/docsgrep/doc-writing)<a href="https://agentmods.dev/skills/reasvyn/docsgrep/doc-writing"><img src="https://agentmods.dev/badge/skills/reasvyn/docsgrep/doc-writing.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00026 | $0.00689 |
| Opus 5 | $0.00013 | $0.00345 |
| Sonnet 5 | $0.00005 | $0.00138 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
doc-writing 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 3d 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.
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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Before writing docs
- Read
AGENTS.mdfor the documentation update checklist. - Read an existing tool doc in
docs/tools/as a template.
Three documentation layers
| Layer | Location | Audience |
|---|---|---|
| Inline code docs | JSDoc/TSDoc in src/**/*.ts |
Developers reading source |
| Tool reference | docs/tools/<tool_name>.md, docs/README.md |
CLI/MCP users |
| Project-level | Root README.md, CONTRIBUTING.md |
Contributors, newcomers |
Files to update when changing a tool
| What changed | Files to update |
|---|---|
| Tool behavior or args | docs/tools/<tool_name>.md + JSDoc on exported function |
| Tool name or description | src/config/tools.json |
| New tool added | docs/tools/<tool_name>.md, docs/README.md, src/tools/help-info.ts |
| New public function/class | JSDoc on that export in source |
| Major architectural change | README.md |
JSDoc/TSDoc conventions
- Every exported function and class gets a JSDoc block.
- Document
@param,@returns, and any side effects (FS writes, network, process spawn). - Use
{@link}to cross-reference related tools or types.
/**
* Scans the target directory for documentation files.
*
* @param args.dirPath - Root directory to scan from.
* @param args.includePath - Optional glob patterns to include.
* @returns List of discovered documentation file paths.
*/
export async function handleFindDocs(args: FindDocsArgs): Promise<McpToolResponse> {
Per-tool doc template
Create docs/tools/<tool_name>.md:
# `<tool_name>`
One-line tagline.
## Description
Detailed description paragraph(s).
## Arguments
| Argument | Type | Required | Description |
|----------|------|----------|-------------|
| `dirPath` | `string` | Yes | Target directory path |
| `includePath` | `string[]` | No | Glob patterns to include |
## Example
```json
{
"name": "<tool_name>",
"arguments": { "dirPath": "." }
}
```
## Response
Description of the response shape. Include example JSON if complex.
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.
- 3d ago First seen · 97 lines · 26 tokens per session scan A bde4a4ac3dd4
doc-writing is a skill published in the GitHub repository reasvyn/docsgrep (0 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 689 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…