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/jjmartres/ai-coding-agents/document-projectnpx skills add jjmartres/ai-coding-agents --skill document-projectgit clone --depth 1 https://github.com/jjmartres/ai-coding-agentsWrote 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/jjmartres/ai-coding-agents/document-project)<a href="https://agentmods.dev/skills/jjmartres/ai-coding-agents/document-project"><img src="https://agentmods.dev/badge/skills/jjmartres/ai-coding-agents/document-project.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 | $0.00079 | $0.01114 |
| Opus 5 | $0.00039 | $0.00557 |
| Sonnet 5 | $0.00016 | $0.00223 |
| Haiku 4.5 | $0.00008 | $0.00111 |
Grade A, and why
document-project 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Documentation Generator
Generate complete, professional documentation structures for software projects. Automatically adapts content and structure based on project language (Python/Go), context (OpenSource/internal), and existing files.
Core Documentation Files
Always generate these five core files:
- README.md - Project overview, quick start, badges
- ARCHITECTURE.md - System design, components, data flow
- USER_GUIDE.md - Usage examples, configuration, troubleshooting
- DEVELOPER_GUIDE.md - Development setup, testing, contribution workflow
- CONTRIBUTING.md - Contribution guidelines, code standards, PR process
Workflow
1. Context Detection
Before generating docs, detect:
- Language: Scan for
go.mod,pyproject.toml,requirements.txt,setup.py - Project type: Check for
Dockerfile,terraform/,k8s/, AI/ML indicators - Existing docs: Identify what already exists to avoid duplication
- License: Detect from LICENSE file or ask user
- Context: Determine if OpenSource or internal based on repo structure
2. Ask Clarifying Questions
Ask user ONE question at a time to fill gaps:
- "What's the primary purpose of this project in one sentence?"
- "Who's the main audience? (developers, ops, end-users, all)"
- "Is this OpenSource or internal? (affects badges, contact info)"
- "Any company-specific tooling to mention? (Jira, Slack channels, etc.)"
3. Content Adaptation
Read references/templates.md to select appropriate template variants based on detected context.
Language-specific elements:
- Python: Package managers (
uv,pip,poetry), testing (pytest), linting (ruff,mypy) - Go: Build commands, testing,
golangci-lint, module structure
Context-specific elements:
- OpenSource: Badges, CODE_OF_CONDUCT, security policy, community guidelines
- Internal: Slack channels, internal tools, compliance requirements, team contacts
Project type adjustments:
- AI Agents: MCP architecture, prompt patterns, example interactions
- Infrastructure: Terraform/K8s setup, deployment procedures, DR plans
- Microservices: API schemas, service mesh, health checks
- CLI Tools: Installation methods, command examples, flags
What ships with it
1 file 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.
- 3d ago First seen · 156 lines · 79 tokens per session scan A e382c278dfd5
document-project is a skill published in the GitHub repository jjmartres/ai-coding-agents (43 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 1,114 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
cocowatch
Developer engagement observer — non-blocking, always-on observational layer that tracks Delegation Intensity, Review Depth, and Engagement Zone throughout a session. Summary surfaced at $ship and FULL checkpoints.
audit
Eight audit modes (codebase — owns "audit the codebase" — docs/process, performance, threat-model, motion, SEO, debt). Triggers "nuclear review", "whole codebase review", "adversarial audit", "fable audit", "correctness audit", "audit the docs", "doc drift", "process audit", "perf audit", "why is it slow", "bundle…
the-grilling
Use when adversarial brainstorming is needed after reconnaissance — the Proposer, Devils-Advocate, and Synthesizer each run ONE pass (no rounds); the Don is grilled on the IDEA first, one question at a time, then the proposal is attacked.
using-gangsta
Use when starting any conversation — establishes how to find and use Gangsta skills, enforces the 1% invocation rule, maps platform tools, and routes the Don's intent through the Gangsta Agents Family hierarchy.
lighthouse
Lighthouse audit + improvement loop until targets met. Triggers "lighthouse", "page speed", "improve scores", "LCP", "CLS", "INP", "core web vitals". Repo-wide perf audits ("perf audit") go to /audit performance mode instead.
interrogation-debugging
Use when encountering any bug, test failure, or unexpected behavior — finds the rat in the code through systematic root-cause interrogation before any fix attempts.