paperclip: Skill for Claude Code

.agents/skills/doc-maintenance/SKILL.md

doc-maintenance is a skill for Claude Code, Codex from paperclipai/paperclip. It costs 44 tokens per session (1,615 once invoked), scanned A, original, MIT.

A workflow for checking Paperclip’s README, specification, and product documentation against recent code changes. Documentation drift means the written information no longer matches the product.

In plain words
What is it for?
Use it after major feature changes or during regular reviews to inspect recent history, identify documentation drift, make minimal corrections, and prepare a pull request.
Why use it?
It finds inaccurate or outdated user-facing claims while keeping edits small and focused instead of rewriting the documentation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is paperclipai/paperclip's own configuration. It tells Claude Code and Codex how to work on paperclip itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything paperclip configures →

About the project

Paperclip is a Node.js server and React application for coordinating AI agents as a business team, with goals, organizational roles, budgets, governance, and work tracking in one interface. It is used by people managing multiple agents or autonomous agent organizations across tools such as OpenClaw, Claude Code, Codex, and Cursor. The catalogue includes skills, agents, an MCP add-on, and an instruction for working with Paperclip.

paperclipai/paperclip · 107,044 stars · on GitHub · paperclip.ing

Reuse

Borrowing it

Nothing to install: this file belongs to paperclipai/paperclip. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/paperclipai/paperclip/master/.agents/skills/doc-maintenance/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/paperclipai/paperclip

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for doc-maintenance

README.md
[![agentmods](https://agentmods.dev/badge/skills/paperclipai/paperclip/doc-maintenance/github.svg)](https://agentmods.dev/skills/paperclipai/paperclip/doc-maintenance)
Your own site
<a href="https://agentmods.dev/skills/paperclipai/paperclip/doc-maintenance"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/doc-maintenance/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.

agentmods 80×15 button for doc-maintenance

Your own site · 80×15
<a href="https://agentmods.dev/skills/paperclipai/paperclip/doc-maintenance"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/doc-maintenance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,615 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Snyk pass 7 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.01615
Opus 5.5 $0.00018 $0.00646
Sonnet 5.5 $0.00009 $0.00323
Haiku 4.5 $0.00004 $0.00161

Measured 1mo ago against content hash e633cf4d7f3a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

doc-maintenance 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.

.agents/skills/doc-maintenance/SKILL.md · 200 lines

How it starts

The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Doc Maintenance Skill

Detect documentation drift and fix it via PR — no rewrites, no churn.

When to Use

  • Periodic doc review (e.g. weekly or after releases)
  • After major feature merges
  • When asked "are our docs up to date?"
  • When asked to audit README / SPEC / PRODUCT accuracy

Target Documents

Document Path What matters
README README.md Features table, roadmap, quickstart, "what is" accuracy, "works with" table
SPEC doc/SPEC.md No false "not supported" claims, major model/schema accuracy
PRODUCT doc/PRODUCT.md Core concepts, feature list, principles accuracy

Out of scope: DEVELOPING.md, DATABASE.md, CLI.md, doc/plans/, skill files, release notes. These are dev-facing or ephemeral — lower risk of user-facing confusion.

Workflow

Step 1 — Detect what changed

Find the last review cursor:

# Read the last-reviewed commit SHA
CURSOR_FILE=".doc-review-cursor"
if [ -f "$CURSOR_FILE" ]; then
  LAST_SHA=$(cat "$CURSOR_FILE" | head -1)
else
  # First run: look back 60 days
  LAST_SHA=$(git log --format="%H" --after="60 days ago" --reverse | head -1)
fi

Then gather commits since the cursor:

git log "$LAST_SHA"..HEAD --oneline --no-merges

Step 2 — Classify changes

Scan commit messages and changed files. Categorize into:

  • Feature — new capabilities (keywords: feat, add, implement, support)
  • Breaking — removed/renamed things (keywords: remove, breaking, drop, rename)
  • Structural — new directories, config changes, new adapters, new CLI commands

Ignore: refactors, test-only changes, CI config, dependency bumps, doc-only changes, style/formatting commits. These don't affect doc accuracy.

For borderline cases, check the actual diff — a commit titled "refactor: X" that adds a new public API is a feature.

Step 3 — Build a change summary

Produce a concise list like:

Since last review (<sha>, <date>):
- FEATURE: Plugin system merged (runtime, SDK, CLI, slots, event bridge)
- FEATURE: Project archiving added
- BREAKING: Removed legacy webhook adapter
- STRUCTURAL: New .agents/skills/ directory convention

Read the full file on GitHub · 200 lines

Files

What ships with it

2 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.

Changes

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.

  1. 1mo ago First seen · 200 lines · 44 tokens per session scan A e633cf4d7f3a

Subscribe to this mod's changes

doc-maintenance is a skill published in the GitHub repository paperclipai/paperclip (107,044 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 1,615 once invoked, about $0.0002 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.