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/nickcrew/claude-cortex/doc-completeness-auditnpx skills add NickCrew/Claude-Cortex --skill doc-completeness-auditgit clone --depth 1 https://github.com/NickCrew/Claude-CortexWrote 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/nickcrew/claude-cortex/doc-completeness-audit)<a href="https://agentmods.dev/skills/nickcrew/claude-cortex/doc-completeness-audit"><img src="https://agentmods.dev/badge/skills/nickcrew/claude-cortex/doc-completeness-audit.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 | $0.00057 | $0.03169 |
| Opus 5 | $0.00028 | $0.01584 |
| Sonnet 5 | $0.00011 | $0.00634 |
| Haiku 4.5 | $0.00006 | $0.00317 |
Grade A, and why
doc-completeness-audit 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 today.
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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Completeness Audit
Determine whether a documentation set covers everything it should by building an inventory of what needs documenting and comparing it to what exists. The output is a prioritized gap report — not new documentation.
When to Use
- After shipping a feature — verify docs cover the new surface area
- Before a release — ensure no undocumented public APIs, CLI flags, or config options
- When users or new hires report "I couldn't find docs for X"
- Periodic health check on doc coverage
- After running
doc-maintenance(structural) anddoc-claim-validator(accuracy) to go wider
Quick Reference
| Resource | Purpose | Load when |
|---|---|---|
references/coverage-model.md |
Defines what "complete" means per doc type | Always (Phase 1) |
Workflow Overview
Phase 1: Inventory → Build the "should exist" list from code and config
Phase 2: Map → Match inventory items to existing documentation
Phase 3: Classify → Score each gap by audience impact
Phase 4: Report → Produce the prioritized gap report
Phase 1: Build the Inventory
Construct a list of everything that should be documented. Use four sources, checking all of them:
Source 1: Public Code Surface
Run the bundled inventory script to extract documentable surface area deterministically:
python3 skills/doc-completeness-audit/scripts/inventory.py --root . --json > inventory.json
# Or human-readable:
python3 skills/doc-completeness-audit/scripts/inventory.py --root .
# Run specific detectors only:
python3 skills/doc-completeness-audit/scripts/inventory.py --root . --detectors env_vars,cli_commands
The script scans source files across Python, JavaScript/TypeScript, Rust, Go, Ruby, Java, and shell, extracting six categories:
| Detector | What it extracts |
|---|---|
env_vars |
Environment variable references (os.environ, process.env, env::var, etc.) |
cli_commands |
CLI commands and flags (argparse, click, clap, cobra, commander) |
config_keys |
Configuration key access in config-related files |
http_endpoints |
HTTP route definitions (Flask, FastAPI, Express, Actix, Axum, net/http) |
public_exports |
Public module exports (__init__.py, export, pub fn, Go capitalized funcs) |
error_types |
Custom error/exception class definitions |
| Event types, webhooks, callbacks | Every event name and payload shape |
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.
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.
- today First seen · 399 lines · 57 tokens per session scan A a865a2e46c84
doc-completeness-audit is a skill published in the GitHub repository NickCrew/Claude-Cortex (37 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 3,169 once invoked, about $0.0003 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 2026-09-03.
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