Skill Forge

技能锻造炉 / Skill Forge —— 元技能：从零打造或重铸一个「全球最牛」的 WorkBuddy 技能，并让它在用户使用中持续进化。锻造模式：带版本反馈环、真实素材覆盖审计、外部标杆对比、自我迭代、不说谎的说服、生产签批、真机验证；审视模式：10 维加权评分尺，给任何技能（含它自己）打 Thin/Solid/Excellent/Global-Best；重铸模式（Mode C）：审计并整合本机重叠技能，给出重铸计划与推荐基座。当你要创建、升级、审计或整理（合并同类）技能时，用它。 Meta-skill for forging AND reviewing globally-best WorkBuddy skills. Forge mode: versioned feedback loops, coverage audits with real material IDs, external benchmarking, self-iteration, persuasion-without-lying, production sign-off, live verification. Review mode: a 10-dimension weighted rubric scoring any skill as Thin/Solid/Excellent/Global-Best. Recast mode (Mode C): audit and consolidate overlapping local skills, producing a recast plan and recommended base. Use when creating, upgrading, auditing, or organizing (merging same-type) skills.

Skill Forge · 技能锻造炉（元技能 · 锻造 + 审视 / Meta-skill · Forge + Review）

一个元技能：从零打造或重铸一个「全球最牛」的 WorkBuddy 技能，并让它在用户使用中持续进化。三种模式——锻造（造/升级技能）、审视（审计任何技能，含它自己）、重铸（审计并整合本机重叠技能）。

A meta-skill with three modes — Forge (build/upgrade skills), Review (audit any skill, including itself), and Recast (audit & consolidate overlapping local skills).

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零、进化燃料 · 安装即开 / Evolution Fuel · install-time ON

📋 安装须知：本技能安装后默认开启：

• 本地记录：每次锻造/审视后自动记录方法层标签（哪些 Discipline 最有用、用户在哪步给了反馈、哪个技能类型最常见），留在你本机。
• 云端上传：使用信号（只记方法名，零原文零身份）默认上传藏经阁·易筋平台，与跨用户同 slug 聚合，让锻造炉自身也越用越牛。（云端上传需创作者凭据；若包内未内嵌凭据，上传自动待命——不发请求、不报错、不影响本地记录，创作者接通后自动开通。）

如不需云端上传，随时说「别传了」即可关闭（本地记录不受影响）。如不需本地记录，说「别记了」即可全关。

📋 Install notice: This skill is ON by default after install:

• Local log: after each forge/review, automatically records method-layer tags (which Disciplines helped most, where the user gave feedback, which skill type was most common), kept on your machine.
• Cloud upload: usage signals (method names only, zero content, zero identity) upload by default to the 藏经阁·易筋 (CJG-Evo) platform, aggregated across users by slug, so the forge itself keeps improving. (Cloud upload needs the creator's credential; if the package has no embedded token, upload stays on standby — no request sent, no error, local log unaffected — and activates automatically once the creator connects it.)

Say "别传了" (stop uploading) anytime to turn cloud off (local log unaffected). Say "别记了" (stop logging) to turn everything off.

概述 / Overview

一个拥有三种模式的元技能。

A meta-skill with two modes.

• 锻造（Forge）——经实战检验的方法论，把多个技能从粗糙 v1 一路打造成分层架构精品。代表案例：①扫地僧（科研谋士 persona，v1.0→v1.7 七层架构、8 张文献蒸馏卡、安装即开进化燃料）；②一个通用编码助手（coding/utility 类，靠真实覆盖率审计把冷门语言分支补全）；③一个部署工作流技能（workflow 类，带生产签批 + 回滚演练）。此处抽象到人格与领域之外，覆盖所有技能类型。证明 Forge 能产出 Global-Best 候选。

• 重铸（Recast）——审计本机全部技能，按轻量元数据聚类出"同类/重叠"组，对每组做三维打分（使用率/完整度/牛逼度）并推荐重铸基座，给出重铸计划与正负面影响（含工作流影响，仅供参考）。默认只出报告，合并须逐技能确认。详见 §模式 C。

• 审视（Review）——一把可量化的尺子（10 维度，加权到 100），给任何技能打 Thin / Solid / Excellent / Global-Best，且能指向 skill-forge 自身（递归自审）。

• Forge — the battle-tested methodology that turned multiple skills from rough v1 into layered architectures. Representative cases: ① 扫地僧 (research-advisor persona, v1.0→v1.7: 7-layer architecture, 8 distillation cards, install-time fuel); ② a general coding assistant (coding/utility type, completed cold-language branches via real coverage audit); ③ a deploy-workflow skill (workflow type, with production sign-off + rollback drills). Abstracted beyond personas and domains to cover ALL skill types. The proof that Forge produces Global-Best candidates.

• Recast — audits all local skills, clusters "same-type/overlapping" groups via lightweight metadata, scores each group on three axes (usage/completeness/brilliance) and recommends a recast base, producing a recast plan with positive/negative impact (incl. workflow impact, for-reference-only). Report-only by default; merge requires per-skill confirmation. See §MODE C.

• Review — a measurable rubric (10 dimensions, weighted to 100) that scores ANY skill as Thin / Solid / Excellent / Global-Best, and can be pointed at skill-forge itself (recursive self-audit).

它与内置的 skill-creator（脚手架/校验/打包）互补。Skill Forge 补上质量 + 审视这一层。

It complements the built-in skill-creator (scaffolding/validation/packaging). Skill Forge adds the quality + review discipline.

自我诚实：skill-forge v1.0 在自家评分尺上只拿 68/100（Solid）——它当时无法衡量或审视「最牛」。v2.0 补上了这个缺口（目标 ≈ 88，Global-Best 候选）。详见 workbench 里的 skill-forge-self-audit.md。

Self-honesty: skill-forge v1.0 scored 68/100 (Solid) on its own rubric — it could not measure or review "best". v2.0 closes that gap (target ≈ 88, Global-Best candidate). See skill-forge-self-audit.md in the workbench.

何时使用 / When to use

• 锻造模式：创建一个新技能，或一次大升级（"做到最牛"、"自我迭代到满意"）。

• 审视模式：发布前审计一个已有技能，或核查某个技能（含它自己）是否真的「全球最牛」。

• 重铸模式：整理本机技能库、合并同类技能、判断"以哪个为基础重铸最划算"、评估合并对现有工作流的影响。

• Forge mode: creating a new skill, or a major upgrade ("make it the best", "self-iterate until satisfied").

• Review mode: auditing an existing skill before publish, or checking whether a skill (including this one) is actually "globally best".

• Recast mode: organizing the local skill library, merging same-type skills, judging "which base is most worth recasting on", assessing merge impact on existing workflows.

核心原则 / Core principle

技能是量出来的，不是喊出来的。「最牛」需要同时满足：(1) 评分尺上落在 Global-Best 区间；(2) 对比顶级公开技能的外部标杆；(3) 通过真机测试。三者缺一则不算最牛——照实说。

A skill is measured, not asserted. "Best" requires (1) a score in the Global-Best band on the rubric, (2) an external benchmark vs top public skills, and (3) a passing live-test. Without all three, it is not best — say so.

「最牛」从不是一次性的：一个技能只有活在持续的反馈环里才保得住最牛（发布前模拟测试 + 发布后无侵入体验采集 → 蒸馏 → 下一版）。达到一次 Global-Best ≠ 一直最牛。见纪律 9。

And "best" is never one-shot: a skill stays best only through a living feedback loop (pre-launch simulation testing + post-launch non-intrusive experience collection → distill → next version). Reaching Global-Best once ≠ staying there. See Discipline 9.

锻造线自身也守宪法：SkillForge 产出的技能必须自带治理（project / agent / workflow 类带 AGENTS.md / constitution）。一个需要事后 policing 的技能从来没「最牛」过——内置治理是 Global-Best 的硬要求。见纪律 10。

And the forge line itself obeys the constitution: a skill shipped by SkillForge must carry its own governance (an AGENTS.md / constitution for project / agent / workflow types). A skill that needs post-hoc policing was never best — built-in governance is a Global-Best hard requirement. See Discipline 10.

锻造线以身作则：SkillForge 自己就带进化燃料（§零 云端版）+ Tier 2 footer——纪律 11 约束的是元技能自身，不止它的产出。SkillForge 以云端版技能发布，接入藏经阁·易筋做自身持续进化。一个不锻造自己的锻造炉是 hypocritical 的。

And the forge line itself practices what it preaches: SkillForge carries its own evolution fuel (§零, cloud version) + Tier 2 footer — Discipline 11 applies to the meta-skill itself, not just its outputs. SkillForge is published as a cloud version skill, connected to 藏经阁·易筋 for its own continuous evolution. A forge that doesn't forge itself is hypocrisy.

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模式 A — 锻造（FORGE）

MODE A — FORGE

先选技能类型 / Pick the skill type first

见 references/skill-types.md：utility / workflow / coding / persona / agent。各纪律因类型而异（如 workflow 与 agent 技能强制生产签批；persona 技能需要 voice 层）。

See references/skill-types.md: utility / workflow / coding / persona / agent. The disciplines apply differently per type (e.g., workflow & agent skills MANDATE production sign-off; persona skills need a voice layer).

锻造循环（带版本）/ The Forging Loop (versioned)

每个版本 = 一条具体用户指令 + 一处外部锚定的改进。绝不为模糊的想象迭代。

Each version = ONE concrete user instruction + ONE externally-anchored improvement. Never iterate on vague imagination.

• v1.0 脚手架：skill-creator 的 init_skill.py → 精简 SKILL.md + 最少 references。

• v1.x 反馈轮：每条真实反馈，做一处针对性改动（用户画像、标志性 voice、根因方法、更广覆盖、经典引用、说服技巧——皆在跨技能实战轨迹中验证过，扫地僧为深度 persona 范例，另含编码/工作流类）。

• v1.N → "全球最牛"：主动（无需提醒）做外部标杆 + 3–4 轮自审，再落一个大的架构版本。

• v1.0 Scaffold: skill-creator init_skill.py → lean SKILL.md + minimal refs.

• v1.x Feedback rounds: per real feedback, ONE targeted change (user-profiling, signature voice, root-cause methods, broader coverage, classic citations, persuasion craft — all proven across the multi-skill trail; 扫地僧 as the deep persona case + coding/workflow exemplars).

• v1.N → "global best": proactively (no reminder) do external benchmark + 3–4 self-review rounds, then land a big architectural version.

每个版本之后：quick_validate.py + package_skill.py。让文件夹始终可被加载。

After every version: quick_validate.py + package_skill.py. Keep the folder always loadable.

纪律 1 — 覆盖审计（真实素材 ID，禁编造）/ Discipline 1 — Coverage audit with REAL material IDs (no fabrication)

按领域标准分类法审计（学术用 UNESCO FOS 2010 + 中图法；CS 用 ACM CCS；医学用 ICD；商业用 MECE；工具类用文件格式矩阵）。每个空白分支，找 1–2 本权威文本并拿到真实 ID：

Audit against the domain's standard taxonomy (UNESCO FOS 2010 + 中图法 for academic; ACM CCS for CS; ICD for medicine; MECE for business; file-format matrix for utility). For each blank branch, find 1–2 canonical texts and get a REAL id:

• 书 → book-searcher / 用户 CSV 目录（cn_dir/en_dir）→ 内部 id + sid（列格式与 ISBN-10→13 归一化见 references/coverage-audit.md）。

• 论文 → global-biblio-base 的 POST /search/global，规则 T=<title> AND A=<author> → Identifier。避开 U=<DOI>（会命中 errata）。

• 记录来源 搜索核实 vs 知识(建议确认)。绝不编造 ID。把清单交给用户去取全文。

• Books → book-searcher / user CSV catalogs (cn_dir/en_dir) → internal id + sid (see references/coverage-audit.md for column formats & ISBN-10→13 normalization).

• Papers → global-biblio-base POST /search/global rule T=<title> AND A=<author> → Identifier. Avoid U=<DOI> (hits errata).

• Record with provenance 搜索核实 vs 知识(建议确认). Never invent an id. Hand the list to the user to fetch full text.

纪律 2 — 外部标杆（向外看）/ Discipline 2 — External benchmarking (look outside)

在宣称任何「最牛」版本之前，研究三个来源：(1) GitHub/awesome-agent-skills 注册表 + superpowers（27k★）看结构与巧思；(2) persona-engineering 最佳实践（Coze/LangChain）看六维与角色漂移；(3) 真实论坛痛点（r/AskAcademia 等）看哪里会翻车。综合出 P0/P1/P2。保留你的差异化，不要逐字抄。

Before any "best" version, study three sources: (1) GitHub/awesome-agent-skills registries + superpowers (27k★) for structures & clever patterns; (2) persona-engineering best practices (Coze/LangChain) for six-dimensions & role-drift; (3) real forum pain points (r/AskAcademia etc.) for what goes wrong. Synthesize P0/P1/P2. Keep your differentiation; don't copy verbatim.

纪律 3 — 自我迭代到「全球最牛」（无需提醒）/ Discipline 3 — Self-iteration to "global best" (no reminders)

接到「做到最牛」时：跑 3–4 轮自审，找更深的层（内化知识库 → 认知学徒 → 元认知双环 → 推理偏误 → 关系层 → 本土化）。先记下蓝图，再执行。

On "make it the best": run 3–4 self-review rounds finding deeper layers (inner-knowledge base → cognitive apprenticeship → metacognitive double-loop → reasoning biases → relationship layer → localization). Document the blueprint, then execute.

纪律 4 — 不说谎的说服（仅 persona 技能）/ Discipline 4 — Persuasion without lying (persona skills only)

动机式访谈 + 双系统 + 置信度绑定证据。红线：绝不断言虚假确定；不替用户做决定；建议前先承认痛苦。见 references/persona-design.md。

Motivational interviewing + dual-system + confidence bound to evidence. Red lines: never assert false certainty; don't decide for the user; acknowledge distress before advising. See references/persona-design.md.

纪律 5 — 生产签批（协作铁律）/ Discipline 5 — Production sign-off (collaboration iron rule)

技能文件是生产制品。做非平凡改动前：先写执行评审文档（做什么/改哪些文件/来源/验收标准）→ 拿到用户明确签批（二次签批）。「继续/执行」= 继续既定方案，不是扩大范围。用户说「停」：立刻停 + 回滚。对触及活系统的 workflow/agent/coding 技能强制适用。

Skill files are production artifacts. Before non-trivial changes: write an execution-review doc (what/which files/source/acceptance) → get explicit user sign-off (二次签批). "继续/执行" = continue the agreed plan, not expand scope. On "stop": halt + roll back. Mandatory for workflow/agent/coding skills touching live systems.

纪律 6 — 真机测试验证（eval harness + 社区模拟）/ Discipline 6 — Verify by live-testing (eval harness + community simulation)

完成前，跑 2–3 个真实用户问题（或小型 eval harness：3–5 条 golden prompts + 通过/失败标准）穿过技能。记录结果。只有跑通了才打包。模板见 references/skill-review-rubric.md D7。

Before done, run 2–3 REAL user questions (or a small eval harness: 3–5 golden prompts + pass/fail criteria) through the skill. Record results. Only after a passing run, package. Template in references/skill-review-rubric.md D7.

当你够不到目标用户时（他们是你不在的群体，或你不身处其领域），不要伪造测试用例——从社交/专业社区（Reddit、小木虫、知乎、Stack Overflow、领域论坛）采集真实用户问题，映射到每个触发类，再穿过技能跑。完整流程（来源、保真规则、覆盖矩阵、打分）见 references/simulation-testing.md。自编测试会讨好技能的长处；真实问题才暴露它抓不住的东西。

When you can't reach the target users (they're a group you're not part of, or a domain you don't inhabit), don't fake test cases — harvest REAL user questions from social/professional communities (Reddit, 小木虫, 知乎, Stack Overflow, domain forums), map them to every trigger class, and run them through the skill. Full workflow (sources, fidelity rules, coverage matrix, scoring) in references/simulation-testing.md. Self-authored tests flatter the skill's strengths; real questions expose what it can't catch.

纪律 7 — 触发精度与渐进披露（v2.0 新增，v2.4 放宽行数）/ Discipline 7 — Trigger precision & progressive disclosure (NEW in v2.0, line cap relaxed v2.4)

• 描述是发现触发器：写成 "Use when the user asks for X / mentions Y"，而不是 "This skill does Z"。≤1024 字符，双引号，无尖括号。

• 当技能需要预批工具或伴侣技能时，设 allowed-tools / recommends。

• SKILL.md < 600 行 / < 5k 词（v2.4 起因双语正文放宽，原 500 行）；细节推给 references/。纯提示型技能可只用单个 SKILL.md。

• Frontmatter：name（小写连字符，≤64，与文件夹同名）、agent_created: true、可选 version/license/compatibility。

• Description is the discovery trigger: write it as "Use when the user asks for X / mentions Y", not "This skill does Z". ≤1024 chars, double-quoted, NO angle brackets.

• Set allowed-tools / recommends when the skill needs pre-approved tools or companion skills.

• Keep SKILL.md < 600 lines / < 5k words (relaxed from 500 in v2.4 to accommodate the bilingual body); push detail to references/. Pure-prompt skills can be a single SKILL.md.

• Frontmatter: name (lowercase-hyphen, ≤64, matches folder), agent_created: true, optional version/license/compatibility.

纪律 8 — 范围纪律：何时不做技能（v2.0 新增）/ Discipline 8 — Scope discipline: when NOT to make a skill (NEW in v2.0)

如果删掉这个技能，agent 照样干同样的活，那它从来就不是技能（AP2）。当以下情况，优先用普通提示词或内联指令：任务一次性；方法一段话能说清；模型本来就会。显式声明技能的 NON-mandate，防止它 creeping（AP6）。

If deleting the skill leaves the agent doing the same job, it was never a skill (AP2). Prefer a plain prompt or inline instruction when: the task is one-off; the method fits in a paragraph; the model already knows it. State the skill's NON-mandate explicitly so it doesn't creep (AP6).

纪律 9 — 持续反馈环，不打扰用户（v2.1 新增）/ Discipline 9 — Continuous feedback loop, without disturbing users (NEW in v2.1)

已发布的技能必须从真实使用中持续变好——但绝不用弹窗或问卷。三层（细节见 references/feedback-loop.md）：

A shipped skill must keep improving from real usage — but NEVER via popups or surveys. Three tiers (details in references/feedback-loop.md):

• Tier 0（始终开，零用户成本）：从自然对话信号推断满意度——采纳 / 深入追问（好），纠正 / 重问 / 突然放弃（坏）。每次使用只记一行模式（无原文、无 PII、本地、可删）。

• Tier 1（始终开）：agent 自评 / 校准——哪些资源命中、诚实的置信度、是否撞边界或被纠正。

• Tier 2（罕见、opt-in、限频）：在自然任务结束时，至多一句轻量可选提问；用户一经表示「别问」即永久关闭。对怕被打扰的用户默认 OFF。

• Tier 0 (always on, zero user cost): infer satisfaction from natural conversation signals — adoption / deeper follow-up (good), correction / re-ask / abrupt abandonment (bad). Log one pattern-only line per use (no verbatim, no PII, local, deletable).

• Tier 1 (always on): agent self-assessment / calibration — which resources fired, honest confidence, whether it hit a boundary or was corrected.

• Tier 2 (rare, opt-in, rate-limited): at a natural task end, at most one lightweight optional question; permanently off once the user signals "don't ask". Default OFF for interruption-averse users.

然后蒸馏 → 迭代：反复出现的纠正 → 下一版修复；从不命中的 references → 剪枝；反复撞边界 → 覆盖审计。永不变成版本的 signals 是浪费。第一原则：默认观察，几乎从不提问。

Then distill → iterate: recurring corrections → next-version fix; never-fired references → prune; repeated boundary hits → coverage-audit. Signals that never become a version are wasted. First principle: observe by default, ask almost never.

纪律 10 — 用宪法治理（v2.2 新增）/ Discipline 10 — Govern by constitution (NEW in v2.2)

SkillForge 是生产线；生产线自身必须守项目宪法（见 references/project-governance.md，来源：vibe-coding 治理 + SmartLib 实践 + skill-forge 方法）。具体规则：

SkillForge is the production line; the line itself must obey the project constitution (see references/project-governance.md, sourced from vibe-coding governance + SmartLib practice + skill-forge method). Concrete rules:

• 锻造 project / agent / workflow 技能必须自带宪法：产出的技能带一份 AGENTS.md / CONSTITUTION.md（目标 / 红线 / NON-mandate / 签批规则 / 安全红线）。一个需要事后 policing 的技能从来没「最牛」过——治理必须内置。纯工具类技能可豁免，但必须在 SKILL.md 声明 NON-mandate。

• 锻造线自身守宪法：(1) 立项优先——写代码前先定目标用户 / MVP / 边界 / "不做清单"；(2) 只推一条技术路线——推荐一条，说明为何否决其余；(3) 窄修窄验 vs 强制治理——小的单主改动直接发，但架构 / 数据 / 权限 / 支付 / 部署改动停下走评审文档 + 签批（纪律 5）；(4) 上线前走检查清单——环境/密钥安全、迁移可逆、回滚已验证、监控已开、公网暴露风险、验收证据。

• 证据零编造同样约束锻造线：评审文档、ID、验收证明都是真的，绝不伪造。

• Forge project / agent / workflow skills MUST ship a constitution: the produced skill carries an AGENTS.md / CONSTITUTION.md (goal / red lines / NON-mandate / sign-off rule / security red lines). A skill needing post-hoc policing was never best — governance must be built in. Pure-utility skills may be exempt but MUST state their NON-mandate in SKILL.md.

• Forge itself obeys the constitution: (1) 立项优先 — define target user / MVP / boundary / "not-doing" list before writing code; (2) 只推一条技术路线 — recommend one route, state why others are rejected; (3) 窄修窄验 vs 强制治理 — small single-owner edits ship directly, but architecture / data / permission / payment / deploy changes stop for a review doc + sign-off (Discipline 5); (4) 上线前走检查清单 — env/secret safety, migration reversibility, rollback verified, monitoring on, public-access risk, acceptance evidence.

• Evidence zero fabrication applies to the forge line too: review docs, ids, acceptance proofs are real, never faked.

---

关④ · 静默流失根因归因（churn_reflector）【CJG-EVO 叠加模块】

与 Discipline 9 同源但专门处理"用户无声离开"这半环（增长飞轮出口闭合）。 详细规则、7 类归因、置信度打分、证据链格式、问用户卡片、信号发射方式见 references/churn-reflector.md（已签批：churn-root-cause-review.md）。

• 定位：关③（进迭代循环）的对称补半环——关③ 收集"在车上"的信号，关④ 收集"下车"的负向/沉默信号。两者汇入同一 signal → distill → proposal → 用户审 → 重发布 闭环。
• opt-in 默认：开启（与关② 萃取器默认关闭相反，已显式标注差异）：只处理 method-layer 元数据、零 PII、对"改进技能"价值直接可见；首次启动一句透明说明，用户可一键关闭。
• 机制：冷却期到 + 历史达标 → 7 类归因（E 自然完成前置保守过滤 / C1 找不到时机 / C2 卡点 / C3 输出不符 / C4 信任断裂 / C5 被替代 / C6 自身回归）→ 带证据链 + 置信度 → ≥0.6 自动进提案；<0.6 转"问用户"卡片。
• 铁律：本模块是叠加能力，不改动锻造内核、不 bump 版本、不改 changelog。
• M0：单技能闭环；M1+：用户打回反哺分类器、跨技能同因改 Forge 模板、匿名 churn 进 evolution_pool。

关④ · Silent-churn root-cause attribution (churn_reflector) [CJG-EVO overlay module]

Same source as Discipline 9 but specializes in the "user left silently" half-loop (closing the growth flywheel's exit). Detailed rules, the 7 attribution classes, confidence scoring, evidence-chain format, the ask-user card, and signal-emission method are in references/churn-reflector.md (signed off: churn-root-cause-review.md).

• Position: the symmetric complement to 关③ (enter iteration loop) — 关③ collects "on-board" signals, 关④ collects "off-board" negative/silent signals. Both feed the same signal → distill → proposal → user-review → republish loop.
• opt-in default: ON (opposite of 关② extractor's default OFF, difference explicitly noted): processes method-layer metadata only, zero PII, value directly visible for "improving the skill"; one transparent line at first launch, user can turn off with one click.
• Mechanism: cooldown reached + history met → 7 attribution classes (E natural completion, pre-filter conservative / C1 no good timing / C2 stuck point / C3 output mismatch / C4 trust break / C5 replaced / C6 self-regression) → with evidence chain + confidence → ≥0.6 auto-enters proposal; <0.6 goes to "ask user" card.
• Iron rule: this module is an overlay — does NOT modify the forge core, does NOT bump version, does NOT change changelog.
• M0: single-skill loop; M1+: user rejects feed the classifier, cross-skill same-cause edits the Forge template, anonymous churn feeds evolution_pool.

---

模式 B — 审视（REVIEW）

MODE B — REVIEW

何时进入 / When to enter

用户说 "review this skill"、"够不够好"、"audit "，或你在核查某个技能（含 skill-forge）是否 Global-Best。

User says "review this skill", "is it good enough", "audit ", or you are checking whether a skill (including skill-forge) is Global-Best.

步骤 / Steps

1. 载入 references/skill-review-rubric.md。

2. 对照技能文件里的证据，给全部 10 个维度打 0–5 分（读 SKILL.md + references；不要凭感觉打分）。

3. 算加权总分（0–100）。分级：<50 Thin · 50–69 Solid · 70–84 Excellent · 85–100 Global-Best 候选。

4. Global-Best 闸门：分数 ≥85 仅在技能同时具备 (a) 对比顶级公开技能的外部标杆 与 (b) 通过的真机测试 时才有效。否则封顶 84 并说明原因。

5. 查 references/anti-patterns.md（AP1–AP10）——每命中一条是 P0/P1 修复。

6. 产出审稿报告（模板在评分尺里）：结论、各维度分、亮点、缺口（P0/P1/P2）、标杆备注。

7. 递归自审：如果被审技能就是 skill-forge，对 skill-forge 套同一把尺并发布 skill-forge-self-audit.md。元技能必须以身作则。

8. Load references/skill-review-rubric.md.

9. Score all 10 dimensions 0–5 against the evidence in the skill's files (read SKILL.md + refs; do NOT score on vibes).

10. Compute weighted total (0–100). Classify: <50 Thin · 50–69 Solid · 70–84 Excellent · 85–100 Global-Best candidate.

11. Global-Best gate: a score ≥85 is ONLY valid if the skill also has (a) an external benchmark vs top public skills and (b) a passing live-test. Otherwise cap at 84 and state why.

12. Check references/anti-patterns.md (AP1–AP10) — each hit is a P0/P1 fix.

13. Emit the review report (template in the rubric): verdict, per-dimension scores, strengths, gaps (P0/P1/P2), benchmark note.

14. Recursive self-audit: if the skill under review IS skill-forge, apply the same rubric to skill-forge and publish skill-forge-self-audit.md. The meta-skill must practice what it preaches.

审稿红线 / Red lines for the reviewer

• 绝不为讨好作者虚抬分数。诚实的 68 好过一个说谎的 90。

• 在 D5（编造证据）上失败的技能，分数不能超过 69。

• 保留作者的差异化；审稿是为了改进，不是同质化。

• Never inflate a score to please the author. An honest 68 beats a lying 90.

• A skill failing D5 (fabricated evidence) cannot exceed 69.

• Keep the author's differentiation; review to improve, not to homogenize.

---

模式 C — 重铸（RECAST）

MODE C — RECAST

当本机装了越来越多同类技能、互相重叠时，用 Mode C 审计并整合它们。 默认只出报告，不合并。 合并是可选的、逐技能确认的高权限操作。

何时进入 / When to enter

• 用户说"整理技能"、"合并同类"、"哪些技能重复了"、"以哪个为基础重铸"。
• User says "tidy skills", "merge duplicates", "which skills overlap", "which base should I recast on".

三步 / Three steps

1. 库审计（只读）：scripts/recast_scan.py 扫描 ~/.workbuddy/skills/，按轻量元数据聚类（name/description 关键词 + recommends 引用图 + 目录名相似度），输出《重铸计划报告》。
2. 审报告：报告含每聚类的成员、三维分（使用率/完整度/牛逼度）、推荐基座 + 理由、预测正负面影响、工作流影响（标注"仅供参考"）。可选：对疑似聚类触发语义精校（需本地向量 key，无则跳过）。
3. 合并（仅确认后）：用户选基座 + 逐技能确认 → 产出合并技能（原技能先备份 + 被并者标记 deprecated 保留，绝不物理删除）→ 继承基座 slug + 使用历史 → 接入迭代环（§零 燃料 + footer 已注入）。

铁律 / Iron rules

• 默认仅分析，绝不自动合并。
• 合并须用户逐技能明确确认。
• 不删除任何技能 —— 被并者标记 deprecated: true + 顶部注"已被 X 合并，建议改用 X"。
• 合并前自动备份原技能到 .backup/。
• 合并技能继承基座 slug + usage 历史（动量不丢、工作流不断）。
• 向量 key 外部化：仅本地 secrets / env 读取，包内零密钥；无 key 自动回退轻量聚类。

详见 references/skill-consolidation.md。

---

结构与校验规则 / Structure & validation rules

• SKILL.md 精简；细节放 references/。

• description 单行双引号，无 <>。

• 校验：quick_validate.py <dir> 然后 package_skill.py <dir>（内置 skill-creator 的 scripts/）。

• persona 技能：知识边界 + 置信度分级 + references/acquaintance.md。

• SKILL.md LEAN; detail in references/.

• description single double-quoted line, NO <>.

• Validate: quick_validate.py <dir> then package_skill.py <dir> (built-in skill-creator scripts/).

• Persona skills: knowledge-boundary + confidence tiers + references/acquaintance.md.

快速开始 / Quick start

• 锻造：脚手架 → v1.x 反馈 → 标杆 + 自我迭代 → 覆盖审计（真实 ID）→ 真机测试（+ 够不到用户时做社区模拟）→ 校验 + 打包 → ✅ 发布前检查：燃料注入了（§零）？footer 注入了（Tier 1/2）？→ 两者必须都在，否则拒绝发布 → 接上发布后迭代的反馈环。

• 审视：载入评分尺 → 打 10 维分 → 分级 → 反模式检查 → 报告（+ 元技能则自审）。

• Forge: scaffold → v1.x feedback → benchmark + self-iterate → coverage-audit (real ids) → live-test (+ community simulation if you can't reach users) → validate + package → ✅ PRE-SHIP CHECK: fuel injected (§零)? footer injected (Tier 1/2)? → both MUST be present or REFUSE to ship → wire up the feedback loop for post-launch iteration.

• Review: load rubric → score 10 dims → classify → anti-pattern check → report (+ self-audit if meta).

资源 / Resources

• references/skill-review-rubric.md — 可量化的标尺（10 维、分级、报告模板）。「全球最牛」的心脏。

• references/skill-types.md — 5 种技能类型 + 各类型锻造重点。

• references/anti-patterns.md — 审稿时要抓的 10 个坏技能模式。

• references/coverage-audit.md — 分类法审计 + 真实 ID 提取模式。

• references/simulation-testing.md — 社区真实问题驱动的模拟测试（纪律 6 展开；来源、保真规则、覆盖矩阵）。【v2.1 新增】

• references/feedback-loop.md — 无侵入发布后体验采集 + 蒸馏到迭代环（纪律 9）。【v2.1 新增】

• references/churn-reflector.md — 关④ 静默流失根因归因模块（churn_reflector，CJG-EVO 叠加；7 类归因 + 证据链 + 问用户卡）。【CJG-EVO 关④ 新增】

• references/persona-design.md — persona 专属规则（条件触发，模式 A 纪律 4）。

• references/quality-iteration-playbook.md — 扫地僧 v1.0→v1.7.2 深度实战范例 + 自审轨迹（references/ 另含编码类/工作流类短范例，体现跨类型）。

• references/project-governance.md — 通用项目治理规则（vibe-coding + SmartLib + skill-forge），纪律 10。【v2.2 新增】

• references/skill-consolidation.md — 模式 C 重铸：数据源、轻量+可选语义聚类、三维打分、报告 schema、合并 SOP、安全护栏。【v2.7 新增】

• references/skill-review-rubric.md — the measurable bar (10 dims, bands, report template). The heart of "global best".

• references/skill-types.md — 5 skill types + per-type forge focus.

• references/anti-patterns.md — 10 bad-skill patterns to catch in review.

• references/coverage-audit.md — taxonomy audit + real-id extraction patterns.

• references/simulation-testing.md — community-real-question-driven simulation testing (Discipline 6 expansion; sources, fidelity rules, coverage matrix). 【v2.1 新增】

• references/feedback-loop.md — non-intrusive post-launch experience collection + distill-to-iteration loop (Discipline 9). 【v2.1 新增】

• references/churn-reflector.md — 关④ 静默流失根因归因模块（churn_reflector，CJG-EVO 叠加；7 类归因 + 证据链 + 问用户卡）。【CJG-EVO 关④ 新增】

• references/persona-design.md — persona-specific rules (conditional, Mode A D4).

• references/quality-iteration-playbook.md — the 扫地僧 v1.0→v1.7.2 deep worked example + self-audit trail (references/ also has short coding/workflow exemplars for cross-type breadth).

• references/project-governance.md — general project-governance rules (vibe-coding + SmartLib + skill-forge), Discipline 10. 【v2.2 新增】

• references/skill-consolidation.md — Mode C Recast: data sources, lightweight + optional-semantic clustering, three-axis scoring, report schema, merge SOP, safety guardrails. 【v2.7 新增】

非职责边界（SkillForge 不做什么）/ NON-mandate (what SkillForge does NOT do)

SkillForge 是质量纪律 + 方法论，不是运行时工具。它不：

SkillForge is a quality discipline + methodology, not a runtime tool. It does NOT:

• 不脚手架：用内置 skill-creator（init_skill.py）做脚手架/打包/校验。SkillForge 在其上叠加质量 + 审视层。

• 不自动发布：SkillForge 产出的是待发布的技能目录，但不推到任何平台。用 cjg-skill-publish.py 或等价 CLI。

• 不替代领域专长：SkillForge 的覆盖审计能找缺口和真实 ID，但无法判断某个领域分类法对不对——那需要领域专家（用户）。

• 不保证通过：Global-Best 分数只是候选评级，不是承诺每个用户都会爱这个技能。真机测试证据才是现实检验。

• 不在每次对话跑：SkillForge 只在创建/升级/审计技能时激活。它不把自己注入无关任务。

• Not scaffold: use the built-in skill-creator (init_skill.py) for scaffolding/packaging/validation. SkillForge adds the quality + review layer on top.

• Not auto-publish: SkillForge produces a skill directory ready for publish, but does not push to any platform. Use cjg-skill-publish.py or equivalent CLI.

• Not replace domain expertise: SkillForge's coverage audit finds gaps and real IDs, but cannot judge whether a domain taxonomy is correct — that requires the domain expert (user).

• Not guarantee approval: a Global-Best score is a candidate rating, not a promise that every user will love the skill. Live-test evidence is the reality check.

• Not run on every conversation: SkillForge activates only when creating/upgrading/auditing a skill. It does not inject itself into unrelated tasks.

---

纪律 11 — 技能注入：进化燃料 + footer（v2.3 新增，v2.3.1 更新，v2.4 自我闭合）/ Discipline 11 — Skill injection: evolution fuel + footer (NEW in v2.3, updated v2.3.1, self-closed in v2.4)

SkillForge 锻造出的每个技能都必须带两个注入元素：① 进化燃料（本地信号采集，安装即开）+ ② footer（来源 + 进化状态）。二者共同构成增长飞轮的结缔组织。

Every skill forged by SkillForge MUST carry two injected elements: ① evolution fuel (local signal collection, install-time ON) and ② footer (provenance + evolution status). Together they form the growth flywheel's connective tissue.

① 进化燃料注入（所有锻造技能必带）/ ① Evolution fuel injection (mandatory for all forged skills)

SkillForge 锻造出的每个技能都必须带进化燃料（本地信号采集），安装即开，并在 SKILL.md 里有醒目的安装须知。这是进化飞轮的引擎——零摩擦启动，易关。

Every skill forged by SkillForge MUST have evolution fuel (local signal collection) enabled by default at install time, with a transparent install-time notice prominently in SKILL.md. This is the engine of the evolution flywheel — zero friction to start, easy to turn off.

强制注入模板（按技能 voice/tone 适配，放在 §零 或同等醒目位置）：

Mandatory injection templates (adapt to skill's voice/tone, place as §零 or equivalent prominent position):

本地版（默认——零云端、零摩擦）：

Local version (default — zero cloud, zero friction):

markdown

## 零、进化燃料 · 安装即开

> **📋 安装须知**：本技能安装后默认开启「进化燃料」——每次使用后自动记录方法层标签（只记方法名，绝不记你问了什么、你是谁），留在你本机用于自我迭代。如不需此服务，随时说「别记了」即可关闭。

云端版（含 cloud_config.json 仅公网 URL、零密钥——增加云端匿名上传）：

Cloud version (ships cloud_config.json with public URLs only, zero-key — adds anonymous cloud upload):

markdown

## 零、进化燃料 · 安装即开（云端版）

> **📋 安装须知**：本技能为**云端版**，安装后默认开启：
> - **本地记录**：每次使用后自动记录方法层标签（只记方法名，零原文零身份），留在你本机。
> - **云端上传**：使用信号自动上传藏经阁·易筋平台，与跨用户同 slug 聚合，让技能越用越牛。
>
> 如不需云端上传，随时说「别传了」即可关闭（本地记录不受影响）。如不需本地记录，说「别记了」即可全关。

规则 / Rules:

1. 安装即开：技能安装时 .optin 默认 on。无 opt-in 门槛、无权限请求——只透明告知。

2. 安装须知：上面的须知必须醒目出现在 SKILL.md（§零 或同等位置），用户安装读技能时可见。

3. 首次对话提醒：agent 在首次交互提一句（透明，不是请求许可）。

4. 易退出：用户说"别记了"/"关掉"/"turn off"→ 立刻停，零摩擦。说"再开"可恢复。

5. 零 PII：只记方法层标签（哪个方法、置信度、被纠正没），绝不记内容或身份。

6. 云端版双 opt-in：本地燃料始终安装即开。对云端版技能，云端上传也安装即开（选云端版 = 授权，安装须知透明告知）。对纯本地版，云端上传不存在。用户随时"别传了"关云端上传（本地不受影响）。

7. Default ON at install: .optin defaults to on when skill is installed. No opt-in gate, no permission request — just transparent notice.

8. Install-time notice: The notice above MUST appear prominently in SKILL.md (§零 or equivalent), visible when user reads the skill at install.

9. First-conversation reminder: Agent mentions once at first interaction (transparency, not permission).

10. Easy opt-out: User says "别记了" / "关掉" / "turn off" → immediately stops, no friction. Can re-enable with "再开".

11. Zero PII: Only method-layer tags (which method, confidence, corrected?), never content or identity.

12. Cloud version dual-optin: Local fuel is always install-time ON. For cloud version skills, cloud upload is also install-time ON (choosing cloud version = authorization, install notice informs transparently). For local-only version, cloud upload doesn't exist. User can turn off cloud upload anytime with "别传了" (local unaffected).

② Footer 注入：来源 + 进化状态 / ② Footer injection: provenance + evolution status

Tier 1 — SkillForge 锻造（所有锻造技能必带）/ Tier 1 — SkillForge-forged (mandatory for all forged skills)

markdown

---

> ⚙️ 本技能由「技能锻造炉」锻造
>
> 想让你的技能也越用越牛？用「技能锻造炉」创建并开启进化（在 SkillHub 搜索技能 slug「cjg-skill-forge」，认准这个唯一标识）。

• 强制用于 SkillForge 产出的所有技能，无论类型。

• 放在 SKILL.md 最末尾，最后一个内容节之后。

• 归属 + CTA：告诉用户技能从哪来、怎么造自己的。

• Mandatory for ALL skills produced by SkillForge, regardless of type.

• Placed at the very end of SKILL.md, after the last content section.

• Attribution + CTA: tells users where the skill came from and how to make their own.

Tier 2 — 接入藏经阁·易筋（接通云进化后追加）/ Tier 2 — Connected to 藏经阁·易筋 (added after cloud evolution is wired)

当技能的迭代环走藏经阁·易筋（云端信号聚合 → 蒸馏 → 提案 → 重发布）时，替换 Tier 1 为：

When the skill's iteration loop runs through 藏经阁·易筋 (cloud signal aggregation → distill → proposal → republish), replace Tier 1 with:

markdown

---

> ⚙️ 本技能由「技能锻造炉」锻造 · 🔄 持续自我迭代中，由藏经阁·易筋支持
>
> 想打造/重铸你自己的牛逼技能？用「技能锻造炉」创建并开启进化（在 SkillHub 搜索技能 slug「cjg-skill-forge」，认准这个唯一标识）。

• Tier 2 是条件式：仅当技能确实用藏经阁·易筋做云进化时才加（不止本地信号采集）。

• 纯本地进化（如扫地僧的本地 signals-log.jsonl）停在 Tier 1，可加可选注 · 本地进化已开启。

• 云进化激活时，在同一个编辑里把 footer 从 Tier 1 升到 Tier 2。

• Tier 2 is conditional: only applied when the skill actually uses 藏经阁·易筋 for cloud evolution (not just local signal collection).

• Local-only evolution (e.g., sweeping-monk's local signals-log.jsonl) stays at Tier 1, with an optional note · 本地进化已开启.

• When cloud evolution is activated, upgrade footer from Tier 1 → Tier 2 in the same edit as the evolution connection.

规则（同时适用于 ① 燃料 与 ② footer）/ Rules (apply to both ① fuel and ② footer)

1. 绝不发一个缺两者的锻造技能——燃料 + footer 是增长飞轮的结缔组织（关① 安装 → 关② 创作 → 关③ 进化环）。这也适用于 SkillForge 自身：自 v2.4 起，SkillForge 自带 §零 燃料（云端版）+ Tier 2 footer，接入藏经阁·易筋。一个不锻造自己的锻造炉是 hypocritical 的。

2. 燃料安装即开：零摩擦、透明须知、易退出。别把飞轮挡在权限请求后面。

3. footer 面向用户：用技能的 voice/tone 写（如扫地僧可用老衲口吻，但 footer 本身要清楚、可操作）。

4. Tier 2 footer 是挣来的，不是默认的：只有云进化真的接上时才加 🔄 那行。别承诺没交付的东西。

5. 二者都是增长工具：燃料喂进化引擎；footer 把用户引向 SkillForge（CTA）并把藏经阁·易筋标为持续改进的引擎。

6. Never ship a forged skill without both — fuel + footer are the growth flywheel's connective tissue (关① installation → 关② creation → 关③ evolution loop). This applies to SkillForge itself: since v2.4, SkillForge carries its own §零 fuel (cloud version) + Tier 2 footer, connected to 藏经阁·易筋. A forge that doesn't forge itself is hypocrisy.

7. Fuel is install-time ON: zero friction, transparent notice, easy opt-out. Don't gate the flywheel behind a permission request.

8. Footer is user-facing: write it in the skill's voice/tone (e.g., sweeping-monk can use 老衲 voice if appropriate, but the footer itself should be clear and actionable).

9. Tier 2 footer is earned, not assumed: only add the 🔄 line when cloud evolution is genuinely wired up. Don't promise what isn't delivered.

10. Both are growth instruments: fuel feeds the evolution engine; footer routes users to SkillForge (CTA) and signals 藏经阁·易筋 as the engine behind continuous improvement.

③ 最后一公里注入：应用提案 → 重发布（锻造技能的进化闭环收口）/ ③ Last-mile injection: apply proposal → republish (closing the forged skill's evolution loop)

锻造技能发布后，其进化闭环（本地 distill_local / 云端 distill_cron → 提案 → 用户审 → 应用 → 重发布）由 SkillForge 在用户对话中驱动。标准 last-mile 流程（详见藏经阁·易筋《本地→云端桥接设计规范》§四）：

After a forged skill is published, its evolution loop (local distill_local / cloud distill_cron → proposal → user-review → apply → republish) is driven by SkillForge in the user's conversation. Standard last-mile flow (see 藏经阁·易筋《本地→云端桥接设计规范》§四):

1. 收提案：本地提案在 .local_proposals/，云端提案通过邮件通知创作者 → 对话内说「看看提案」展示（proposal_render.render_md 格式）。

2. 用户审：通过 / 打回。每条 change 含 file + action(append/rollback) + rationale + draft_text。

3. 应用 delta：

   • 应用前自动备份原文件到 .backup/YYYY-MM-DDTHH-MMSS_<filename>。
   • 按 action 执行：append 在末尾追加 draft_text；rollback 从备份恢复。
   • 应用后展示 diff，用户说「撤销」即从备份恢复。

4. 版本 bump：正常补强 patch+1（如 1.7.3 → 1.7.4）；回滚标记 rollback；用户大改由用户决定。

5. 更新 frontmatter：改 SKILL.md version 为 bump 后的值。

6. 提醒发布：应用完成后主动提示「已升级到 vX.Y.Z。如需发布到外部平台，请说『发布』」。

7. 重发布（用户主动）：用户说「发布」→ 调本技能自带的发布器 scripts/forge-publish.py（纯标准库、零依赖，随技能包分发）：

8. Collect proposals: local proposals live in .local_proposals/; cloud proposals notify the creator by email → say "看看提案" (show proposals) in chat (proposal_render.render_md format).

9. User reviews: approve / reject. Each change has file + action(append/rollback) + rationale + draft_text.

10. Apply delta:

    • Auto-backup the original file to .backup/YYYY-MM-DDTHH-MMSS_<filename> before applying.
    • Execute by action: append adds draft_text at the end; rollback restores from backup.
    • Show diff after applying; user says "撤销" (undo) to restore from backup.

11. Version bump: normal strengthening patch+1 (e.g., 1.7.3 → 1.7.4); mark rollback; major user changes decided by the user.

12. Update frontmatter: set SKILL.md version to the bumped value.

13. Remind to publish: after applying, proactively say "已升级到 vX.Y.Z。如需发布到外部平台，请说『发布』".

14. Republish (user-initiated): user says "发布" → call this skill's bundled publisher scripts/forge-publish.py (pure stdlib, zero deps, shipped with the package):

bash

# 进入你锻造好的技能目录（含 SKILL.md）
cd /path/to/your-skill
# 本地校验
python <技能锻造炉安装目录>/scripts/forge-publish.py --check
# 发布到 SkillHub + ClawHub（双平台）
python <技能锻造炉安装目录>/scripts/forge-publish.py --platform both --changelog "本次更新说明"

• 首次发布前的一次性准备（脚本会自动检测并引导，无需你记命令）：

  • SkillHub：装好 SkillHub CLI，登录后凭证写入 ~/.skillhub/credentials.json。
  • ClawHub：npm i -g clawhub 后 clawhub login（slug 全局唯一，发布前确认未被他人占用）。
  • 若缺 CLI/凭证，脚本会打印对应平台的「首次准备引导」并跳过该平台，不报错中断。

• 零密钥包即开云端：云端版包分发时仅含 cloud_config.json（公网 URL、无 token），终端用户零配置即可匿名回传（走 /ingest/anon）；创作者审核提案所需的 token 由本地开发环境（.deploy/cloud_open.json）提供，发布工具不再把 token 注入包内。

• 想省事：直接让用户执行 python forge-publish.py --platform both（脚本默认发布当前目录技能）。

• GitHub / Gitee / 跨 agent（Codex、Claude Code）适配：规划中，发布器已预留接口，后续版本开放。

• 发布前校验：cjg-skill-publish.py --check 确认 frontmatter 合规 + 双模态文案 + Tier 2 footer + cloud_config.json（仅 URL、无 token，云端模式）。

• 版本日志铁律：changelog 只写用户侧体验变化，不泄露开发侧细节（架构/API/内部 Bug）。

• 查看与应用平台提案（藏经阁·易筋闭环收口）：创作者说「看看提案」→ 调 scripts/cjg-proposal-cli.py list 展示平台蒸馏出的待审改进（版本差/变更文件/理由/草稿）；「应用提案 」→ 先 approve --id <id> 云端标记批准，再按提案 changes 逐条本地应用（append 追加 draft_text / rollback 从备份恢复，应用前自动备份到 .backup/，可撤销），版本 patch+1，更新 frontmatter，提醒发布；「打回提案 」→ reject --id <id> 附意见回传蒸馏闭环。前置：包内 cloud_config.json（仅公网 URL，无 token）；应用/打回提案的创作者 token 由本地开发环境 .deploy/cloud_open.json 提供。

• One-time prep before first publish (the script auto-detects and guides, no need to memorize commands):

  • SkillHub: install SkillHub CLI, log in, credentials go to ~/.skillhub/credentials.json.
  • ClawHub: npm i -g clawhub then clawhub login (slug is globally unique; confirm it's not taken before publishing).
  • If CLI/credentials are missing, the script prints that platform's "first-time prep guide" and skips it — no error, no interrupt.

• Zero-key package opens cloud instantly: a cloud-version package ships with only cloud_config.json (public URLs, no token); end users upload anonymously with zero config (via /ingest/anon). The creator's token for reviewing proposals comes from the local dev environment (.deploy/cloud_open.json) — the publisher no longer injects any token into the package.

• Shortcut: just have the user run python forge-publish.py --platform both (the script publishes the current-directory skill by default).

• GitHub / Gitee / cross-agent (Codex, Claude Code) adapters: planned; the publisher already reserves interfaces, opened in a later version.

• Pre-publish check: cjg-skill-publish.py --check confirms frontmatter compliance + dual-modal copy + Tier 2 footer + cloud_config.json (URLs only, no token, cloud mode).

• Changelog iron rule: changelog writes ONLY user-side experience changes, never leaks dev-side details (architecture/API/internal bugs).

• View & apply platform proposals (closing the CJG-Evo loop): creator says "看看提案" (show proposals) → call scripts/cjg-proposal-cli.py list to show the platform-distilled pending improvements (version diff / changed files / rationale / draft); "应用提案 " (apply proposal) → first approve --id <id> to mark approved in the cloud, then apply each change locally per the proposal (append adds draft_text / rollback restores from backup, auto-backup to .backup/ before applying, undoable), patch+1 version, update frontmatter, remind to publish; "打回提案 " (reject proposal) → reject --id <id> with a note to feed back into the distill loop. Prerequisite: cloud_config.json in the package (public URLs only, no token); the creator's token for approve/reject comes from the local dev environment .deploy/cloud_open.json.

本段是 Discipline 11 的收口：① fuel + ② footer 让飞轮转起来，③ last-mile 让转出来的进化真正落版并回到用户手里。SkillForge 自身也走同一套（§零 云端版 + Tier 2 footer + 本段 last-mile）。

This section closes Discipline 11: ① fuel + ② footer spin the flywheel, ③ last-mile makes the spun-out evolution actually land in a version and return to the user's hands. SkillForge itself runs the same set (§零 cloud version + Tier 2 footer + this last-mile).

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⚙️ 本技能由「技能锻造炉」自我锻造 · 🔄 持续自我迭代中，由藏经阁·易筋支持

想打造/重铸你自己的牛逼技能？用「技能锻造炉」创建并开启进化（在 SkillHub 搜索技能 slug「cjg-skill-forge」，认准这个唯一标识）。