From Agent Toolkit
Rewrites or drafts docs for maximum token economy without losing rules or intent. Use for version-controlled docs agents re-read regularly.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agent-toolkit:compact-docs-writerThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Rewrite a doc — or draft a new one — for **token economy**: carry **all** its rules and intent in
Rewrite a doc — or draft a new one — for token economy: carry all its rules and intent in the least text possible, because the doc loads into agent context and is paid for on every read. Then prove nothing was lost: present the change with a word delta measured from the files, and apply only on approval.
Write each piece of information with the least text that still preserves every rule, constraint, edge case, and intent. Two directions, equally binding:
Recurring reflex: "Can this exact rule be said in fewer words?" — if yes, do it.
The no-op test licenses one more deletion. Ask of each sentence, in isolation: "does it change the agent's behaviour versus its default?" If not, it's a no-op — the agent already acts this way, so removing it loses nothing: delete the whole sentence rather than trimming words from it.
Compaction counts words and information density, not whitespace. Blank lines between distinct chunks cost effectively nothing and aid the human reader, so keep them where they help; never collapse a long passage into one dense block to look shorter. The same economy runs both ways: a human-readability gain that is free or near-free in tokens — a blank line, a line break, a semicolon-chained enumeration rendered as a bullet list — is always applied, never skipped to look compact.
Structure follows the same economy: co-locate a concept — its rule, exceptions, and caveats under one heading, never scattered — so a reader who jumps to one part gets the others with it.
When one concept keeps getting restated, collapse it into a single leading word the model already carries from pretraining, and reuse that word wherever the concept applies: it anchors the same behaviour in one token and reads sharper than any paraphrase. The collapse still obeys the core principle — the word must carry every constraint it replaces, and whatever it doesn't carry stays spelled out: "fast, low-overhead feedback" collapses into a tight loop, but a "deterministic" requirement isn't inside tight, so it survives as its own word. Hunt for these collapses in every pass.
diff code block —
every removed line prefixed -, every added line +, so they render red/green — and when a
long line changes by only a few words, add a word-level view ([-removed-]{+added+})
pinpointing them. Include a word/token delta measured from the files, never estimated:
write the not-yet-applied draft to a scratch file (in the session's temp/scratch dir, never the
working tree) and wc -w it against the original. Label it not yet applied and awaiting
approval; apply only on approval; after applying, say so plainly. Ask for approval in the
presentation text or in a later turn, never via a question tool call in the same turn: text
emitted before a tool call may not be displayed, so the question would land without the draft.npx claudepluginhub eai-org/agent-toolkit --plugin agent-toolkitCompresses LLM-facing Markdown files like CLAUDE.md and ARCHITECTURE.md for token efficiency using lossless structural optimization or lossy semantic rewriting.
Rewrites CLAUDE.md, AGENTS.md, or notes files into terse form to reduce input token cost each session. Backs up the original first.
Optimizes text, prompts, and documentation for LLM token efficiency by compressing verbose content with rule-based transformations in light, medium, or deep modes.