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 skills add iliaal/ai-skills --skill writinggit clone --depth 1 https://github.com/iliaal/ai-skillsWrote 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/iliaal/ai-skills/writing)<a href="https://agentmods.dev/skills/iliaal/ai-skills/writing"><img src="https://agentmods.dev/badge/skills/iliaal/ai-skills/writing/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/iliaal/ai-skills/writing"><img src="https://agentmods.dev/badge/skills/iliaal/ai-skills/writing.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 | $0.00071 | $0.00944 |
| Opus 5 | $0.00036 | $0.00472 |
| Sonnet 5 | $0.00014 | $0.00189 |
| Haiku 4.5 | $0.00007 | $0.00094 |
Grade A, and why
writing 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ia-writing — 89% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Human writing
Edit human-facing prose while preserving meaning, factual accuracy, and the writer's voice. User instructions and the intended audience outrank these style preferences. Editing a draft does not authorize posting it.
Modes
- Edit (default): produce corrected text and a proportionate changelog.
- Detect: when asked to flag AI tells without rewriting, quote each observed pattern and give a brief fix. Do not rewrite, score, or infer authorship. Use Phase 1 of audit-workflow.md, then stop and offer an edit.
- Machine-facing text: tool descriptions, system prompts, skill/agent instructions, error strings, and inter-agent messages need precise specification language. Use
ia-refine-promptwhen appropriate; do not apply fragments, contractions, or invented personal voice mechanically.
Procedure
- Identify the draft's core point and 3–5 concrete voice signals to preserve: vocabulary, cadence, bluntness, humor, uncertainty, digressions, and intended polish. Keep this working note out of the delivered text.
- Match the requested surface and mode. Short commits, PR descriptions, comments, and posts need a quick audit. Long documents, essays, and reports use the two-phase audit-workflow.md.
- Lead with a concrete fact or point. Use active voice, specific actors where relevant, simple words, stable terminology, and meaningful numbers. Keep related words together, one topic per paragraph, and tone appropriate to the audience.
- Flag formulaic structures, vague claims, passive evasions, unnecessary qualifiers, artificial contrasts, synonym cycling, mechanical formatting, and fake-profound endings. A flag is a candidate, not a verdict.
- Apply restraint: leave natural sentences intact, preserve useful uncertainty, and retain lists/tables that carry real structure. Match the tone problem, not a forbidden token. Do not invent opinions, feelings, facts, or actors to satisfy a stylistic pattern.
- Read the result aloud. Check that edits remain proportional and that the writer would recognize the voice. Return the full corrected text when editing; include only the audit/changelog detail appropriate to the request and length.
What ships with it
8 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.
- 2d ago Changed · -135 lines 08a2bfc0c269
- 4d ago Changed · +4 lines e04ec449df4d
- 12d ago First seen · 178 lines · 71 tokens per session scan A ae8cfaed3682
writing is a skill published in the GitHub repository iliaal/ai-skills (41 stars, last pushed 3d ago), licensed MIT. It adds 71 tokens to every session and 944 once invoked, about $0.0004 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.
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review-spd
Findings-first code review workflow for AI coding agents. Use when the user asks to review uncommitted changes, commits in a date range, or a branch compared to the main branch / PR-style diff. Focuses on bugs, regressions, correctness risks, missing tests, security/data-safety issues, and other behavior-changing…