agent-routing
Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe…
Load it without being asked at the start of any task whose deliverable is prose that another person will read, such as a PR description, commit message, README, doc, postmortem, blog post, issue or review comment, report, release note or essay, and do so before drafting, whether
Claude Code
$ npx -y skills add oaustegard/claude-skills --skill declauding --agent claude-code
Codex
$ npx skills add oaustegard/claude-skills --skill declauding -a codex -y
Cursor
$ npx skills add oaustegard/claude-skills --skill declauding -a cursor -y
Gemini CLI
$ npx skills add oaustegard/claude-skills --skill declauding -a gemini-cli -y
GitHub Copilot
$ npx skills add oaustegard/claude-skills --skill declauding -a github-copilot -y
OpenCode
$ npx skills add oaustegard/claude-skills --skill declauding -a opencode -y
Windsurf
$ npx skills add oaustegard/claude-skills --skill declauding -a windsurf -y
name: declauding description: Load it without being asked at the start of any task whose deliverable is prose that another person will read, such as a PR description, commit message, README, doc, postmortem, blog post, issue or review comment, report, release note or essay, and run it on the draft before the text is pushed, posted, published or saved. Draft first without this skill in context, from a positive writing brief if one exists. Also use it when someone says "de-claude", "de-slop", "humanize this", "this reads like AI" or "make it sound human", or asks for a voice, tone or register edit. It rewrites the constructions that mark prose as model-written into plain technical prose, and checks that the rewrite kept the draft's claims. The constructions include staged reveals, verdict headers, aphoristic closers, "it's not X, it's Y", em-dash drama, forced triads, flat-certainty adverbs, noun stacks and dropped 'that'. Do not use it on fiction, poetry, code or quoted text. For a full adversarial review of a deliverable, use challenging. metadata: version: 0.14.0
Turn LLM-shaped prose into prose that a human technical writer would have written.
It has two output modes. **Clean** (the default) returns only the rewritten text. **Annotated** returns a single-file HTML artifact: the rewritten text with every changed passage marked, and for each mark the original, the tic's name and the reason it goes. A toggle hides the marks so the result can be read straight through.
What you deliver depends on how the skill was called:
the rewrite, plus a short list of what changed if the edit was substantial.
summary in the conversation rather than pasting the whole result back. Edit prose only: leave code blocks, frontmatter, data, link targets and quoted specimens alone.
job (a PR description, a commit message, a doc). Return the final text and nothing else. No preamble, no summary, no tic list.
The rewrite must not contain a fact, name, number, date, quote or citation that is not in the source. A register pass is prone to this, because the fix for a vague sentence is a specific one, and that specific has to come from the source or from the author.
*Experts believe it plays a crucial role* becomes *the sources here do not say who studies it*, or gets cut. It does not become *researchers at Lanzhou University* unless the source says so. When a sentence needs real-world detail to work, ask for it or write the plain version without it.
Opinions and stance count as voice rather than fact. Keeping the author's judgment is required (see Overcorrection); adding a factual claim they did not make is a defect even when the result reads more human.
Most entries in `references/register.md` (the staging, flat-certainty and confiding families) describe one habit: building a sentence so the reader feels a finding arrive, instead of stating the finding.
Apply one test to each sentence: is it saying the thing, or performing having had the thought? Rewrite the second kind so that it says the thing.
The register has five families. Read the one the draft needs first.
| Entries | Family | Mechanism | |---|---|---| | 1–23, 37–42 | Staging | The sentence performs a finding arriving: reveals, verdict headers, aphoristic closers, welded epigrams | | 24–36 | Encyclopedic and chatbot | Nothing is staged; the writing runs on defaults: copula avoidance, participle tails, forced triads, chatbot residue | | 43–47 | Flat certainty | This skill's own output. An adverb, compound or absolute negative stands in for evidence: `plainly`, `quietly`, `refusal`, `re-derived`, `byte-identical`, `nothing` | | 48–52 | Confiding essayist | Staging aimed at the reader's trust: announced honesty, stranded auxiliaries, obituary headlines. From Simon Willison's [llm-cliche-highlighter](https://github.com/simonw/tools/blob/main/llm-cliche-highlighter.html), updated 2026-08-27 | | 53–56 | Compressed syntax | Nothing is staged; a claim is packed into fewer words than a reader can parse: noun stacks, verbed nouns, dropped *that*, a subject waiting behind a list for its verb. The fix makes the sentence longer. From [arXiv 2610.29162](https://arxiv.org/abs/2610.29162) |
A clean pass tends to land in the flat-certainty register, which is also the fastest-growing cluster of GitHub pull request descriptions: 0.70% of early 2025 and 39.5% of August 2026 (`references/corpus.md`). Its version of the test asks whether a sentence states the finding or performs having settled it. Fix it by checking that each adverb, compound or negative has evidence behind it. Do not put the staging back.
When you know which model wrote the draft, read its file in `references/models/` before step 2. When you know which model you are, read your own file too. Each file has two sections: what to look for in that model's drafts, and what to check in your own rewrite when you are that model.
| Model | File | Where its tics sit | |---|---|---| | Opus 5, Opus 5.5 | `models/opus-5.md` | Headers and paragraph closers; the linter misses most of them | | Opus 4.6, 4.8 | `models/opus-4.md` | Closers, em dashes, one metaphor reused across paragraphs | | Sonnet 4.6, 5 | `models/sonnet.md` | Em dashes and negation-first reversals | | Haiku 4.5 | `models/haiku.md` | The encyclopedic family; the linter catches most of it | | Sonnet 5.5, Haiku 5.5 | not yet profiled | Use the same tier's file as a prior and expect drift | | Fable 5.1 | `models/fable.md` | Bolded list leads and takeaways |
When Claude is cleaning its own draft, one file covers both sections; run step 2b in a subagent or separate context. When the author is unknown or human, skip th
A collection of Claude skills distributed as a Claude Code plugin marketplace. Skills are primarily designed for the Claude.ai PaaS skill compute environment; mileage will vary if used as Claude Code Agent Skills.
Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe…
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