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 petar-djukic/writing-skills --skill match-outlinegit clone --depth 1 https://github.com/petar-djukic/writing-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/petar-djukic/writing-skills/match-outline)<a href="https://agentmods.dev/skills/petar-djukic/writing-skills/match-outline"><img src="https://agentmods.dev/badge/skills/petar-djukic/writing-skills/match-outline/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/petar-djukic/writing-skills/match-outline"><img src="https://agentmods.dev/badge/skills/petar-djukic/writing-skills/match-outline.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 | $0.00117 | $0.01690 |
| Opus 5 | $0.00059 | $0.00845 |
| Sonnet 5 | $0.00023 | $0.00338 |
| Haiku 4.5 | $0.00012 | $0.00169 |
Grade A, and why
match-outline 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 7d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Match outline (whole-document voice analysis)
This skill answers "does my draft's structure and voice match the field?" It compares a draft's conventions against a corpus profile, extracts voice persona blueprints from exemplar papers, and rewrites drafts as a whole document with a plagiarism guard.
It complements match-structure (which provides the quantitative metrics,
frequency tables, and similarity math this skill imports) and filter-tells
(which detects generic AI-writing patterns at the paragraph level).
The rewrite uses cohere:command-a-03-2025 by default (GH-184;
MATCH_OUTLINE_MODEL or --model gpt-oss:120b-cloud for keyless/local). Pass
--model claude-sonnet-5 to use the Anthropic API instead. AI-sounding
output is expected at this stage — match-voice handles paragraph-level
diction cleanup downstream.
Pipeline role: the caller's structural step (GH-208)
This skill is a document rewriter, not a stage of the humanize chain.
A workflow command invokes it before humanize when the form needs
changing — a venue profile's structural_step field names it for exactly
that decision — and humanize's input contract assumes its work is already
done. It rewrites at section level, which is a different operation with a
different risk class than the chain's paragraph-level stages.
Verify content preservation after every rewrite, and include figure
blocks. The built-in check covers citations and numbers; it does not
cover figures. A 2026-08-31 run on the COMST introduction dropped an
entire  figure block while reporting "all citations and
numbers preserved." Until the check covers them, count ![ occurrences
(and table and code-fence blocks) before and after, and diff the
reference section verbatim. The rewrite also introduces typographic
unicode (U+2011 non-breaking hyphens, curly quotes) that downstream
gates normalize but this stage does not — normalize to ASCII before
handing the output on.
Where things live
- Quantitative profile:
<db-dir>/voice-profile.json, written bymatch-structure'sstyle.py corpus. - Qualitative profile:
<db-dir>/voice-profile.md, written by the model followingreferences/voice-analysis-instructions.mdPart 1. - Comparison reports:
<db-dir>/voice-reports/<draft-stem>-voice.md, followingreferences/comparison-report-template.md. - Exemplar blueprints:
<db-dir>/voice-blueprint-<slug>.md, extracted from chosen exemplar papers followingvoice-analysis-instructions.mdPart 3 (consensus vs idiosyncrasy). - Rewritten drafts:
<draft-stem>-rewritten.mdnext to the draft. The draft itself is never modified.
What ships with it
6 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.
- 7d ago Changed · +21 lines · +3 tokens per session 365a9c1c4171
- 12d ago First seen · 148 lines · 114 tokens per session scan A 29fb6ad6a429
match-outline is a skill published in the GitHub repository petar-djukic/writing-skills (4 stars, last pushed 8d ago), licensed MIT. It adds 117 tokens to every session and 1,690 once invoked, about $0.0006 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 2026-08-31.
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