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 skills add Mathews-Tom/armory --skill api-docs-generatorgit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/api-docs-generator)<a href="https://agentmods.dev/skills/mathews-tom/armory/api-docs-generator"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/api-docs-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mathews-tom/armory/api-docs-generator"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/api-docs-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00068 | $0.01472 |
| Opus 5.5 | $0.00027 | $0.00589 |
| Sonnet 5.5 | $0.00014 | $0.00294 |
| Haiku 4.5 | $0.00007 | $0.00147 |
Grade A, and why
api-docs-generator 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 1mo 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API Docs Generator
Audits API endpoint documentation for completeness, generates enhanced docstrings with proper parameter descriptions and examples, documents all response codes, and produces Pydantic model examples — bridging the gap between auto-generated OpenAPI specs and genuinely useful API documentation.
Reference Files
| File | Contents | Load When |
|---|---|---|
references/fastapi-patterns.md |
FastAPI-specific documentation patterns, Path/Query/Body parameter docs | FastAPI endpoint |
references/example-generation.md |
Creating realistic field examples, model_config patterns | Example values needed |
references/response-codes.md |
Standard HTTP response documentation, error response schemas | Response documentation needed |
references/openapi-enhancement.md |
OpenAPI spec enrichment, tag organization, schema documentation | OpenAPI spec review |
Prerequisites
- Access to the API source code (route definitions, models)
- Framework identification (FastAPI, Flask, Django REST, Express)
Workflow
Phase 1: Analyze Endpoints
- Inventory endpoints — List all routes with HTTP method, path, handler function.
- Identify models — Request bodies (Pydantic models, dataclasses), response models, query parameters, path parameters.
- Map dependencies — Authentication requirements, middleware, shared dependencies.
- Read existing docs — Current docstrings, OpenAPI metadata, inline documentation.
Phase 2: Audit Documentation
For each endpoint, check:
| Check | What to Verify | Common Gap |
|---|---|---|
| Endpoint description | Handler has a docstring | Missing or "TODO" |
| Parameter descriptions | Each param has description= |
Path params undocumented |
| Request example | Body model has example= or json_schema_extra |
No request example |
| Response model | response_model= specified |
Returns raw dict |
| Error responses | 4xx/5xx documented with responses= |
Only 200 documented |
| Tags | Endpoint assigned to a tag group | Untagged endpoints |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 166 lines · 68 tokens per session scan A 441c8f0f013c
api-docs-generator is a skill published in the GitHub repository Mathews-Tom/armory (337 stars, last pushed 2d ago), licensed MIT. It adds 68 tokens to every session and 1,472 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
tg-bot-ops
A guide for operating and troubleshooting Telegram bots and systems that connect Telegram to an AI agent. It covers how messages arrive, get processed, and produce replies.
data-manager-api-setup
Guides developers through client library installation and authentication setup steps for the Data Manager API. Use this skill when a user is getting started with the Data Manager API and needs to setup their local environment, install the client library, or setup access to the API. Don't use for implementing audience…
deepstream-sop
Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the…
workers-best-practices
Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.
vss-search-archive
Use this skill when a user wants to search archived VSS video that is already registered in a configured deployment — by natural-language, similarity, attribute, object-ID, or lexical tag query. Not for fresh clip Q&A, live captioning, video summarization, deployment, or source ingestion/deletion.
new
Create a new project to start development quickly.