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。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/whiteguo233/OpenBiliClaw --skill writing-specs命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | writing-specs |
| description | Use when authoring a spec or implementation plan, or planning multi-task work. |
Produce an authoritative design spec and an executable implementation plan before changing code. Resolve every placeholder against the live repository.
Create both documents with the same dated slug:
docs/plans/YYYY-MM-DD-<slug>-spec.mddocs/plans/YYYY-MM-DD-<slug>-plan.mdCommit the pair as docs: add <feature> spec and plan before implementation. Do not mix code
into this docs-first commit.
# <Feature> Spec — <measurable outcome>
**Created:** YYYY-MM-DD
**Scope:** <affected behavior, modules, and integrations>
**Out of scope:** <explicit non-goals>
## Goal
State the current cost or failure, quantified target outcomes, and the exact commands that
verify them.
## Design invariants (MUST hold in every phase)
1. **<Invariant name>:** <falsifiable rule and verification surface>.
2. **<Invariant name>:** <falsifiable rule and verification surface>.
## Current diagnosis
### D1. <Observed problem>
Record evidence at `path/to/file.py:line`; distinguish confirmed facts from hypotheses.
### D2. <Observed problem>
Record the caller, data flow, failure mode, and existing test coverage with `file:line` evidence.
## Priority classification
| Phase | Content | Tier | Why |
| --- | --- | --- | --- |
| 0 | <gate or prerequisite> | **MUST** | <risk it controls> |
| 1 | <main change> | RECOMMENDED | <measured leverage> |
List dependencies, then group work into **Wave A**, **Wave B**, and later waves by risk and
dependency. State what can ship independently and where work may safely stop.
## Phase designs
### Phase 0 — <name>
Define interfaces, algorithms, error behavior, tests, rollout, and numeric acceptance gates.
## Expected impact
| Lever | Measured effect |
| --- | --- |
| <phase> | <quantified result> |
## Documentation obligations
List every module doc, changelog, architecture diagram, CLI/config reference, installer doc,
and README surface triggered by the design.
Every invariant must be testable, every diagnosis must cite live code, and every claimed improvement must have a reproduction command.
# <Feature> — Implementation Plan
> **For Claude:** REQUIRED SUB-SKILL: superpowers:executing-plans (execute this plan task-by-task).
> **Spec:** [`YYYY-MM-DD-<slug>-spec.md`](./YYYY-MM-DD-<slug>-spec.md)
> **Status:** <revision and review state>
> **Execution order:** <dependency-ordered tasks and Wave grouping>
> **Tech:** <runtime, interpreter, focused tests, lint, format, and type-check commands>
**Invariants that MUST hold — re-read before each task:**
- <Restate the spec's invariant without weakening it.>
- <Restate the spec's invariant without weakening it.>
### Task N: <single deliverable>
**Files:** Add/modify/test exact paths.
Consumes: . Produces: .
[ ] Write one focused failing test for .
[ ] Run and confirm FAIL for the intended missing behavior.
[ ] Add the minimal implementation needed for that test.
[ ] Rerun and confirm PASS with no warnings.
[ ] Run the touched regression tests, lint/format checks, and MyPy command.
Numeric gate: .
Reproduce with ; record the result in the PR.
State the production/shadow/canary observation, commands, owner, duration, and rollback trigger.
Keep the handoff line and checkbox steps verbatim so superpowers/GSD execution conventions remain discoverable. Restate every spec invariant at the plan top and require the executor to re-read them before each task.
For a model-visible profile reduction, a concrete gate is: admission flip rate ≤ 3% and
Spearman rank correlation ≥ 0.95 on at least 100 aligned candidates, verified by
scripts/run_profile_diet_ab.py. The command, dataset provenance, baseline commit, and observed
values belong in the spec and the task acceptance block—not only in a PR comment.
Use
docs/plans/2026-07-05-llm-token-diet-spec.md
as a reference for quantified invariants, D1..Dn diagnosis, risk Waves, phase designs, impact,
and documentation obligations.