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/mountainunicorn/add/docsnpx skills add MountainUnicorn/add --skill docsgit clone --depth 1 https://github.com/MountainUnicorn/addWhat 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 | $0.00020 | $0.04078 |
| Opus 5 | $0.00010 | $0.02039 |
| Sonnet 5 | $0.00004 | $0.00816 |
| Haiku 4.5 | $0.00002 | $0.00408 |
Grade A, and why
docs 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 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.
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 — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADD Docs Skill v{{VERSION}}
Generate, update, and verify project documentation. Uses a discovery-first approach: the skill learns your codebase structure on first run, caches that knowledge in a manifest, and uses it for fast, accurate doc generation on every subsequent run.
Works with any project type — web APIs, libraries, CLIs, data pipelines, monorepos, or anything else.
Token economy: doc rendering is mechanical work. When sub-agent dispatch is available, delegate bulk generation (diagram emission, API doc rendering, README regen) to the fast tier per rules/model-roles.md; keep the frontier-model context for judgment — what to document, staleness calls, and review.
Overview
The Docs skill manages documentation artifacts that drift as code evolves:
- Discovery Manifest — Cached codebase map (entry points, types, services, architecture) that powers all other scopes
- Architecture Diagrams — Mermaid diagrams reflecting current flows, traced from manifest data
- API / Interface Documentation — Appropriate to project type: OpenAPI for web APIs, module docs for libraries, usage docs for CLIs
- README / CLAUDE.md — Keep project overview accurate (structure, commands, architecture)
- Freshness Check — Detect stale docs without modifying anything, with file-level fingerprinting
Documentation is generated from code, not written by hand. The source of truth is always the implementation. The manifest bridges the gap between raw source and generated docs.
All archetype lookup tables, the manifest schema, report formats, and configuration defaults live in ${CLAUDE_PLUGIN_ROOT}/references/docs-archetypes.md — referenced throughout as "the archetype reference".
Pre-Flight Checks
- Read
.add/config.json- Load project name, stack, maturity level
- Load architecture details (languages, frameworks, database)
- Load
docsconfiguration block (defaults in the archetype reference) - Determine project archetype (see Project Archetypes section)
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 · 353 lines · 20 tokens per session scan A e2730d6587dc
docs is a skill published in the GitHub repository MountainUnicorn/add (11 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 4,078 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 2026-08-30.
Other skills, from other repositories
swarm
Launching multi-agent parallel work with the Agentic SDLC. Use when a task benefits from decomposition into parallel subtasks.
finish
Completing a development branch for merge readiness. Use when implementation and tests pass and the branch needs formal preparation for review and merge.
grill
Interrogating requirements to validate before building. Use before swarm decomposition, design decisions on ambiguous features, or when scope creep risk is high.
team
Referencing the agent roster, roles, coordination model, and dispatch modes. Use when spawning agents or checking permissions.
ticket
Associate every PDS task with a GitHub issue. Orchestrator finds or creates the ticket, posts plan and acceptance criteria as a checkbox list, updates it as work progresses. Use at Phase 1 of every swarm.
triage
Triage insights into actionable GitHub issues across repos. Use after running /insights to convert analysis into tracked work.