Install
$ agentstack add skill-khasky-awesome-agent-skills-awesome-style-mimic ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Style mimic — learn a site's voice, rewrite anything in it
Two modes sharing one artifact: Learn deep-crawls a website and produces a self-contained style guide; Apply rewrites documents in that guide's voice so a whole batch reads as one author. The guide file is the only thing that crosses sessions — everything else is working state.
Bundled files (load on demand):
scripts/crawl-ingest.mjs— the crawl engine: state, corpus writes, link filtering, serial
classification, quotas, stop criterion. Learn mode runs it; never reimplement it by hand.
references/rewriter-contract.md— the per-document rewriter instructions Apply mode gives
each subagent verbatim.
references/example-styles/— complete style guides produced by Learn mode (buffer.com,
hubspot.com, zapier.com, ahrefs.com, semrush.com, hootsuite.com, clickup.com, linktr.ee). The format reference, and directly usable with Apply mode. Their samples are SYNTHETIC — composed to demonstrate each register, not quoted from the sites (the publishable policy below).
Mode dispatch
- Argument is a URL → Learn mode.
- Arguments are a style-guide path + a file/folder target → Apply mode.
- Ambiguous → ask.
Learn mode
Output: styles/.md (host without www.). Working state: style-crawl// (state.json, corpus/, analysis/) — resumable, deletable after the guide lands.
0. Browser preflight
Learn mode drives a real browser through whatever automation the agent has — Playwright MCP (--extension bridge to the user's live Chrome, or a spawned browser), or an equivalent browser tool; without one, say so and stop (a plain HTTP fetch tool cannot render JS-heavy sites and silently misses content — do not degrade to it without telling the user).
With the Playwright MCP bridge: list tabs first (a lone about:blank means the bridge is not attached — stop and have the user fix it); never touch the bridge's own connect.html tab; open ONE working tab and reuse it. Warn the user the browser is busy while the crawl runs. Dismiss cookie/consent banners once on the first page — they pollute extracted text.
1. Crawl loop (BFS by real links — no sitemap.xml)
All bookkeeping lives in the bundled ingester:
node /scripts/crawl-ingest.mjs --dump dump-.json --dir style-crawl/ [--origin ]
It ingests a dump of fetched pages and prints one JSON line: nextBatch (up to 8 URLs), counters, coverage %, done. No --dump → just re-read state; that is also how a crawl RESUMES after interruption. First run needs --origin.
The loop is two tool calls per batch of 8 pages:
- Fetch batch — one browser-evaluate call that
fetch()es the batch same-origin inside
the page, parses each response with DOMParser, strips nav,header,footer,script,style,noscript,svg,iframe,form,aside plus [role="navigation"],[aria-hidden="true"], and returns [{requested, url, title, text (≤30k chars), links[]}] per page (~250 ms pause between fetches). Save the result to a file (Playwright MCP: the filename parameter on browser_evaluate), named dump-.json — page text must stay OUT of the conversation context, and the host-specific name keeps concurrent sessions from clobbering each other. Insert \n before closing block tags before parsing so textContent keeps paragraph breaks:
html = html.replace(//gi, '\n');
- Ingest — run the script; feed its
nextBatchinto the next fetch. Repeat until
done: true.
Fast-path validity check: extract the FIRST page twice — live (navigated tab) and via fetch() — and compare. Empty or much shorter fetch text → the site is JS-rendered: fall back to per-page navigation + a settle wait + the same extraction in the live DOM, still dumping to the file and ingesting with the same script (single-page dumps are fine).
Rules:
- Read-only: navigation and DOM reads only; never click actions or type into forms (consent
dismissal excepted). The script already skips login/cart/account paths, media files, .md/.txt page mirrors, feeds, and localized mirrors (/es, /fr, …) — style is learned from the primary language.
- One crawl at a time machine-wide; never crawl the same host from two sessions (shared
state.json corrupts).
- Mid-crawl steering by editing
state.jsonbetween batches is allowed: trim a low-value
serial quota (author bios, changelog entries), or purge noise the skip-list missed (legal archive years, malformed URLs) — remove such URLs from frontier AND discovered so coverage math stays honest.
- Report the ingester's counter line to the user every ~3 batches.
2. Serial pages and the stop criterion (handled by the script)
Serial = templated pages whose count can run to thousands (blog posts, products, glossary terms, tags). The script samples them (default quota 18 per pattern) instead of exhausting them: auto-serial path patterns plus automatic promotion when ≥8 URLs share a / template. done fires at ≥80% coverage of NON-serial pages with all quotas met, or an empty frontier. Safety cap 300 pages: on capHit: true, STOP and tell the user the real coverage — never present a capped crawl as full.
3. Analysis fan-out (parallel, from disk)
Split corpus/*.md into batches of ~15 files; spawn one subagent per batch, all concurrently. Each reads its files and writes STYLE observations (not content summaries) to style-crawl//analysis/batch-N.md with fixed sections: Lexicon / Voice & POV / Rhythm / Structure / Formatting / Genre notes / Golden-sample candidates (3–5 verbatim excerpts ≤120 words with source file and why), returning only a 5-line summary.
4. Synthesis
One agent (or the main context) reads all analysis/batch-*.md, reconciles (majority wins; genre differences become sub-profiles, not contradictions), and writes styles/.md with exactly these sections: Voice profile · Tone rules (Do/Don't) · Lexicon · Rhythm & syntax · Structure (with the site's invariant CTA strings quoted verbatim) · Formatting habits · Genre notes · Samples · Rewrite instructions. The guide must be self-contained — Apply sessions see only this file. See references/example-styles/buffer.com.md for the target shape and depth.
Two sample policies — pick by the guide's destination, ask when unclear:
- Private/local guide (default): golden samples — 8–10 verbatim excerpts across genres,
each verified letter-for-letter against its corpus file before inclusion (drop or fix any that don't match), labeled with genre and source URL. Verbatim anchors give Apply mode the highest fidelity.
- Publishable guide: synthetic samples — the excerpts are COMPOSED by you in the
described style about invented, generic subject matter: no sentence taken from the site, no real claims or people, no source URLs. Section opens with "Composed to demonstrate the register — not text from the site." Short phrase-level microcopy patterns (CTA strings, verdict openers) may stay verbatim. Converting an existing golden-sample guide to publishable = rewrite only its samples section this way and strip sample attributions.
Apply mode
Inputs: a style-guide path + a target (file or folder). Read the guide FIRST, fully — its Golden samples anchor the tone; its Rewrite instructions override defaults below.
Target resolution
Single file → one rewrite. Folder → glob prose-bearing sources (.md .mdx .txt .html .htm .astro .svelte .vue .jsx .tsx — component files: copy strings only), skipping node_modules, build output, lockfiles, pure code/config. List the set first when >20 files.
Output rules
Target inside a git repo (git -C rev-parse --show-toplevel exits 0) → ask which mode, unless the user already named one:
- Separate worktree (recommended) — `git -C worktree add -b
restyle/ -restyle, rewrite in-place inside the worktree, user reviews with git diff and merges or removes it (their call, never yours). Worktrees cut from HEAD — warn if git status` shows uncommitted changes on target files.
- Mirror folder —
-styled/next to the target; originals untouched. - In-place — only on explicit request; warn first on a dirty working tree.
Non-repo target → mirror folder by default; in-place only on explicit request.
Fan-out and the one-author guarantee
One subagent per file, spawned in parallel. Each subagent gets: the style-guide path, one source path, one output path, the file mode (markdown/html/component), and the FULL text of references/rewriter-contract.md — identical guide + identical contract per file is what keeps one authorial voice across the batch. Never relay a summary of the guide; each subagent reads the guide file itself. A failed file gets one retry, then is reported — never silently dropped.
After all rewrites land (2+ files), run ONE consistency-pass subagent over the whole output set (for >15 files: first/last 3 paragraphs plus a middle excerpt each): find cross-document drift — lexicon used in one file but violated in another, tone shifts, inconsistent heading/CTA patterns — fix findings directly with edits, return the path: what changed list. Report that list to the user; it is the evidence the batch reads as one author.
Verification (both modes)
- Learn: the final report cites the ingester's numbers (pages visited, non-serial coverage %,
serial patterns sampled, failures) — from state.json, not memory — and states that every golden sample was grep-verified verbatim against the corpus.
- Apply: the final report lists files rewritten/skipped/failed, the consistency-pass fix
list, and where the originals are (untouched mirror / worktree branch / in-place).
- Both: anything unverifiable (a capped crawl, a file the rewriter refused) is stated
explicitly, never implied as done.
Anti-patterns
- Crawling
sitemap.xmlinstead of real navigation links — sitemaps list URLs the site's own
linking never surfaces and miss the link-graph signal of what matters.
- Letting page text into the conversation context during the crawl (dump to disk; context
compaction must not be able to lose corpus data).
- Presenting a capped or partial crawl as full coverage.
- Style guides padded with abstractions ("friendly but professional") instead of quotable
mechanics (exact CTA strings, verdict-first FAQ openers, em-dash pivots).
- Rewriters translating the source document (style transfers across languages; words do not),
inventing facts, or "improving" content beyond voice/rhythm/lexicon/formatting.
- Publishing a golden-sample guide as-is — verbatim excerpts of someone else's site do not
belong in a public repo; convert to synthetic samples first (the publishable policy above).
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: khasky
- Source: khasky/awesome-agent-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.