Borrowing it
Nothing to install: this file belongs to satanyakiv/NutriSport. 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/satanyakiv/NutriSport/main/.claude/commands/natural-docs.mdgit clone --depth 1 https://github.com/satanyakiv/NutriSportWrote 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/commands/satanyakiv/nutrisport/natural-docs)<a href="https://agentmods.dev/commands/satanyakiv/nutrisport/natural-docs"><img src="https://agentmods.dev/badge/commands/satanyakiv/nutrisport/natural-docs/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/commands/satanyakiv/nutrisport/natural-docs"><img src="https://agentmods.dev/badge/commands/satanyakiv/nutrisport/natural-docs.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.00584 |
| Opus 5 | $0.00000 | $0.00292 |
| Sonnet 5 | $0.00000 | $0.00117 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
natural-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 8d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Read ~/.claude/skills/anti-ai-slop-writing/SKILL.md Read ~/.claude/skills/anti-ai-slop-writing/references/banned-words.md Read .claude/rules/docs.md
Natural Docs Review
$ARGUMENTS
Process
-
READ the target file or text provided above.
-
SCAN for AI writing patterns. Check each category:
Vocabulary: any word or phrase from the banned list.
Structural tells: rule-of-three groupings, uniform sentence length (three consecutive sentences of similar length), identical paragraph structure (topic-explanation-example-transition), hedging seesaw giving equal weight to both sides, excessive bullet points (more than 7 in a row).
Punctuation tells: more than one em dash per section, exclamation marks used for enthusiasm, ellipses as transitions, underuse of semicolons and colons.
NutriSport-specific: vague openers instead of concrete facts, marketing synonyms instead of codebase terminology, architecture described without real numbers, changelog entries with narrative, commit messages without the "why."
Pitch-specific (when target is in
pitch/directory):- EN technical (
PITCH.md): hero framing, vague capability claims, missing measurable specifics, "passion project", hedged CTAs - UK business (
PITCH_UA_CLIENT.md): tech jargon instead of client value, English loanwords, vague reassurance instead of concrete mechanisms
Present findings in a table:
Line Pattern Found text Category If zero issues found, say so and stop.
- EN technical (
-
REWRITE the full text with all patterns eliminated. Preserve every technical fact, link, code reference, and structural intent. Change only the language and structure.
-
DIFF SUMMARY -- brief list of what changed:
- "Replaced [banned word] with [concrete alternative]"
- "Broke rule-of-three grouping into four items"
- "Varied sentence lengths in paragraph N"
- "Replaced em dashes with semicolons in section N"
- "Rewrote opener from value judgment to concrete fact"
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.
- 8d ago First seen · 64 lines · 0 tokens per session scan A 4eda420cebe7
natural-docs is a command published in the GitHub repository satanyakiv/NutriSport (11 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 584 tokens. 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.