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 tighten-stylegit 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/tighten-style)<a href="https://agentmods.dev/skills/petar-djukic/writing-skills/tighten-style"><img src="https://agentmods.dev/badge/skills/petar-djukic/writing-skills/tighten-style/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/tighten-style"><img src="https://agentmods.dev/badge/skills/petar-djukic/writing-skills/tighten-style.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 | $0.00122 | $0.01547 |
| Opus 5 | $0.00061 | $0.00773 |
| Sonnet 5 | $0.00024 | $0.00309 |
| Haiku 4.5 | $0.00012 | $0.00155 |
Grade A, and why
tighten-style 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tighten-style
Cuts what does not earn its place and gives paragraphs a spine, against the catalog in references/style-rules.md. Every finding cites a rule ID, so the report reads as a copyedit keyed to a document you can argue with rather than a list of opinions.
The rules are ours. They cover the ground Strunk covered and the ground he could not — he wrote before markdown and before a machine could produce fluent prose with nothing behind it.
The floor
Tightening stops at the target register's own density. Shorter is not the
target. Cutting past the measured function-word ratio manufactures the
clipped, aphoristic register that reads as machine-written — the failure
filter-tells calls overshoot. Where a writing-voice/ corpus exists,
match-structure supplies that floor; without one, stop at each rule's stated
threshold.
The floor is venue-keyed (GH-338). By default it is the author's own
density. With --venue <name>, the venue profile
(writing-voice/venues/<name>.yaml, see the writing-voice rule) supplies it
instead: the profile's targets (sentence_length_mean/stdev) become the
sentence floor — a whitepaper tightened to newsletter density reads clipped —
and its hedge_policy keys the TS-08 threshold: zero flags every hedge
(book voice, rule 10), minimal flags pairs, calibrated keeps single
calibrated hedges on empirical claims and only flags stacks. An explicit
--sent-floor still wins over the profile. No --venue, no change.
Never touch a direct quotation, a normative requirement, a citation, or a number.
Procedure
python3 <tighten-style>/scripts/check_style.py <file> [--json] [--hedge-policy zero|minimal|calibrated]
python3 <tighten-style>/scripts/tighten.py --article <file> [--venue <name>] # the rewrite, via Ollama
python3 <tighten-style>/scripts/tighten.py --article <file> --check-only # plan without model calls
- Run the checker for the findings and the reading pass for the judgment rules (TS-06, TS-07, TS-09, TS-11) it cannot see.
- Run the tightener for the rewriting. It selects the instead→do pairs for the rules each paragraph fired, prompts the second model family with the pairs — never the rule prose — and gates every candidate with match-voice's verify step. Paragraphs that fail the gate keep their original text.
- Read the register markers it prints (passive, agentive, nominalization, connectives, before → after). Rising markers on a shrinking draft mean the pass moved toward the assistant register, which is the failure this design exists to prevent.
- Do not tighten by hand-applying the rules with the drafting model. The full evidence is in references/hand-tightening-regression.md. GH-222 measured what that does: a paper excerpt moved from distance 26.1 to 6.5 from the AI-draft fingerprint under a faithful rules pass, overshooting the draft's own passive rate. The rules read as instructions by an instruction-tuned model ARE that model's register. The catalog stays for understanding and for the checker; the pairs are its delivery.
What ships with it
11 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.
- references/hand-tightening-regression.md 2.8 KB
- references/style-rules.md 8.3 KB
- references/tightening-pairs.yaml 6.2 KB
- scripts/check_style.py 20 KB runs code
- scripts/pairs.py 2.3 KB runs code
- scripts/test_check_style.py 4.8 KB runs code
- scripts/test_write_draft.py 5.6 KB runs code
- scripts/testdata/test_check_style.py 5.8 KB runs code
- scripts/testdata/test_pairs.py 3.6 KB runs code
- scripts/testdata/test_tighten.py 6.3 KB runs code
- scripts/tighten.py 20 KB runs code
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 · +2 lines aa11a01691c2
- 12d ago First seen · 123 lines · 122 tokens per session scan A 4c230902c64f
tighten-style is a skill published in the GitHub repository petar-djukic/writing-skills (4 stars, last pushed 8d ago), licensed MIT. It adds 122 tokens to every session and 1,547 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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