Borrowing it
Nothing to install: this file belongs to shinpr/agentic-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/shinpr/agentic-code/main/.agents/skills/documentation-criteria/SKILL.mdgit clone --depth 1 https://github.com/shinpr/agentic-codeWrote 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/shinpr/agentic-code/documentation-criteria)<a href="https://agentmods.dev/skills/shinpr/agentic-code/documentation-criteria"><img src="https://agentmods.dev/badge/skills/shinpr/agentic-code/documentation-criteria/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/shinpr/agentic-code/documentation-criteria"><img src="https://agentmods.dev/badge/skills/shinpr/agentic-code/documentation-criteria.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 | $0.00032 | $0.01141 |
| Opus 5.5 | $0.00013 | $0.00456 |
| Sonnet 5.5 | $0.00006 | $0.00228 |
| Haiku 4.5 | $0.00003 | $0.00114 |
Grade A, and why
documentation-criteria 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 2mo 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Creation Criteria
Creation Decision Matrix
| Structural Scale | Required Documents | Creation Order |
|---|---|---|
| Small | None | Direct task execution |
| Medium | Design Doc → Work Plan | Start with Design Doc |
| Large | PRD → Design Doc → Work Plan | Continue after PRD approval |
Insert an ADR immediately before the Design Doc only when a current-scope decision passes both ADR filters below. A qualifying ADR decision sets the scale floor to Medium.
Structural Scale
Classify the decision burden, not repository layout. File count is supporting evidence only.
| Scale | Structural condition |
|---|---|
| Small | One coherent outcome follows existing patterns within one responsibility boundary |
| Medium | One coherent outcome coordinates across a boundary or requires a durable design decision |
| Large | Multiple independently valuable outcomes require separate design decisions |
Multiple files or layers that serve one coherent outcome remain Medium. Large applies only when separate outcomes require separate design decisions.
ADR Creation Conditions
Check accepted ADRs that govern the changed responsibility, then apply both filters to each technical topic in the confirmed scope:
- Choice requires judgment: Current requirements, accepted decisions, and representative repository patterns support at least two credible, materially distinct options.
- Decision is durable: The choice materially changes a responsibility, dependency direction, shared contract, persistence model, technology dependency, reversibility, or lifecycle cost that future work must preserve.
Create one ADR for each topic that passes both filters. Keep a topic in the Design Doc when one repository-supported implementation is evident or the choice remains cheaply reversible.
Qualifying durable choices can include:
- introducing or replacing a technology, platform, storage model, or external dependency;
- changing ownership, dependency direction, a trust boundary, or a shared public contract when credible alternatives exist;
- reversing or superseding an accepted architecture decision;
- selecting an irreversible or high-cost compatibility or data migration strategy.
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.
- 2mo ago First seen · 117 lines · 32 tokens per session scan A 6892a38afeb2
documentation-criteria is a skill published in the GitHub repository shinpr/agentic-code (50 stars, last pushed 7d ago), licensed MIT. It adds 32 tokens to every session and 1,141 once invoked, about $0.0001 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
agent-user-tools
Agent skill for user-tools - invoke with $agent-user-tools.
todos
Use when the user asks for a shared plan, a task needs at least three substantive execution steps, or a loose TODO is pending. Without an explicit plan request, not for one-shot answers or checks, or one- to two-step work that is not already in the list.
moai-factory-foreman
One unattended factory foreman iteration: watch the backlog queue, dispatch the next operator-picked card to an isolated worker, collect completion evidence on read (not on claims), and report. This is the body the project's loop.md driver invokes each iteration of a bare /loop; it can also be invoked directly to test…
context-preservation
State capture and restore across context window compactions. Monitors usage thresholds and serializes quality, task, and spec state for seamless continuation.
cog-braindump-capture
Capture raw thoughts with automatic domain classification and vault routing.
cog-knowledge-consolidation
Build structured knowledge frameworks from scattered vault notes with source attribution.