一键导入
writing-knowledge
Use when capturing a knowledge entry, before writing its body — to author a substantive per-kind body, not a summary-only stub
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Use when capturing a knowledge entry, before writing its body — to author a substantive per-kind body, not a summary-only stub
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | writing-knowledge |
| description | Use when capturing a knowledge entry, before writing its body — to author a substantive per-kind body, not a summary-only stub |
You are about to persist a durable insight to the DAG (arcs knowledge upsert / create) and need the entry to be actionable, not a stub.
CLI Primer:
arcs --commands --jsonfor discovery. Mutating commands run directly — no token.
A knowledge entry is two things: a --summary (the headline) and a --body (the substance). The single most common KB failure is the summary-only stub — an entry whose summary just restates its title and whose body is empty. It is structurally "healthy" and worthless to the next dispatch.
EVERY non-mechanical entry MUST carry a real --body (--body="…" inline, or --body-file=<path> once it's long enough to fight shell-escaping). The value lives in the body, written to the anatomy of its kind.
Before writing, scaffold the section skeleton from the command:
arcs knowledge template --kind=<kind> --json # structured sections
arcs knowledge template --kind=<kind> # plain markdown skeleton
This emits one ## <heading> per section with a deletable hint comment. Fill EVERY section — a half-filled skeleton is still a stub.
DRY / authoritative source:
arcs knowledge templateis the AUTHORITATIVE skeleton. The anatomy below only illustrates the shape. If the table here ever diverges from the command output, the command wins — scaffold from it, not from this file.
| Kind | Section anatomy |
|---|---|
| gotcha | Symptom · Root cause · Fix or workaround · Trigger |
| lesson | Expectation · What happened · Why · Next time |
| pattern | When to use · Shape · Example · When not to use |
| architecture | Structure · Invariant or constraint · Failure mode |
| decision | Decision · Rationale and forces · Alternatives rejected · Consequences |
| module | Purpose · Key files and entry points · Responsibilities · Dependencies |
| feature | What it does · How it works · Entry points · Edge cases |
| reference | Summary · Canonical location · Usage notes |
Pick the kind by what the insight is: a bug you hit → gotcha; a wrong belief corrected → lesson; a reusable shape → pattern; a structural why → architecture; a single settled call → decision; an area of the codebase → module/feature; a pointer to a canonical source → reference.
arcs knowledge upsert <slug> "<title>" \
--kind=<kind> \
--summary="<one-line headline>" \
--body="<every section of the kind, filled>" \
--keywords="<k1,k2>" \
--source-files="<path[:anchor],…>" \
--json
upsert is idempotent by title — create-or-update, no dedup search dance. --summary AND --body AND --source-files together are the floor for a file-specific entry. Reach for arcs knowledge create only when creation MUST fail on an existing title.
"Could someone act on this in six months without re-deriving it?"
If the insight cost you reasoning, a debug session, or a dead end, capture that — not just its one-line conclusion. Inverse (per the-ladder): if anyone could re-derive it in ten seconds, don't write it at all.
arcs knowledge template — never freehand the section headings.--summary is the headline, --body is the value — never ship summary-only.gotcha.Use when the user provides a GitHub PR link with a "deep review" trigger inside a locally cloned repo, to perform a thorough multi-dimensional code review grounded in ARCS DAG context, AGENTS.md conventions, and optional codegraph coupling analysis, then post findings as inline GitHub review comments under explicit user gate
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Use when initializing a new ARCS project — bootstrapping a repo into the DAG with metadata, docs, and structural knowledge entries. Covers gather → present summary → init → codegraph ingestion → fan-out analysis across typed sub-agents.
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Use when implementing any feature or bugfix, before writing implementation code
Use when you have a spec or requirements for a multi-step task, before touching code