agentify-project
Make a project ready for AI agentic engineering by converging it toward a canonical agent-neutral structure — a lean AGENTS.md index with progressive…
Draft, rewrite, or refine a doc for maximum token economy without losing any rule or intent. Use for docs kept in version control and regularly re-read by agents; skip throwaway docs like plans.
$ npx -y skills add eai-org/agent-toolkit --skill compact-docs-writer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/compact-docs-writerContext preview
The summary Claude sees to decide when to auto-load this skill.
Draft, rewrite, or refine a doc for maximum token economy without losing any rule or intent. Use for docs kept in version control and regularly re-read by agents; skip throwaway docs like plans.
name: compact-docs-writer description: Draft, rewrite, or refine a doc for maximum token economy without losing any rule or intent. Use for docs kept in version control and regularly re-read by agents; skip throwaway docs like plans. allowed-tools: Read, Write, Edit, Glob, Grep, Bash license: MIT metadata: version: "1.6"
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.
1. **Compact.** Rewrite the target to meet the core principle in one pass — a first draft already meets the standard; don't ship a loose draft expecting a later pass to tighten it. If a chunk is needed only in a sub-case and is big enough to tax every read, you *may* suggest extracting it into a referenced file — never force it; the enforced standard is compact text, not splitting. 2. **Self-review** before presenting (terse yes/no):
Answer by drafting a shorter rival phrasing for each new or rewritten sentence, not by re-reading: an unchallenged yes is a rubber stamp.
reordered or merged one, which count as removals — and confirm each drops only duplication, filler, or a verified no-op, never a load-bearing rule, instruction, edge case, or nuance. After a merge, re-verify the result still carries every item from both sources.
test — cut any that fail. 3. **Present & confirm.** Show the change as a unified diff inside a fenced `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.
A collection of generic agentic tools for common engineering tasks, designed to work with any AI agent on any kind of software project.
Make a project ready for AI agentic engineering by converging it toward a canonical agent-neutral structure — a lean AGENTS.md index with progressive…
Check how much of a ticket is already implemented — split it into requirement blocks, judge each against the code, and save a human-readable TICKET-STATUS…
Author or refine a skill for maximum token economy without losing intent. Use when creating any new skill or editing an existing `SKILL.md`.
Audit what auto-loads into an agent session's context window and suggest lean, reversible fixes to cut startup tokens.
Turn a refined requirements document into a structured implementation PLAN.md a fresh session can execute. Planning only — decides the "how", not the "what".…
Turn a ticket or requirements document into a concise QA manual-test file a non-author can follow. Invoke manually only.