leap

name	LEAP
description	LEAP — 落地执行引擎。内含两条管线：A 分支蒸馏（从 raw data 提取 skill）、 B 分支融合（多 skill 编织为一个）。被 SkillAlchemy 编排器调用。 Use when 编排器判断需要蒸馏或融合时。
version	v1.0

LEAP · 落地执行引擎

LEAP 不自己做路由，不跟用户交互。上游 Skill-Alchemy 告诉它走哪条分支，它执行。 所有交互节点由 Skill-Alchemy 编排——LEAP 只负责跑管线，跑完返回结果。

分支路由

指令	分支	管线
distill / 蒸馏	A 分支	蒸馏管线 — 从 raw data 提取 target OS，编译为 persona/tool skill
fuse / 融合	B 分支	融合管线 — method.skill(骨架) × subject.skill(s)(血肉) → output.skill

调用模式

Mode	Trigger	Behavior
Full run	No special keyword	完整管线 + Gate，输出 skill 包
Plan only	只到 Stage 3 或 stop_after_stage: 3	A 分支 Stage 1-3 only，输出 research_plan.json 后停止
Resume	从 Stage 4 继续 或 resume_from_stage: 4	A 分支跳过 1-3，使用已有 research_plan.json，跑 4-7 + Gates

---

A 分支: 蒸馏管线

Source Intake → Intake Assessment → Research Plan Design
  → Research Swarm → Gate 1: Merge
  → Exemplar Discovery → Synthesis (3 agents)
  → Skill Compilation → Gate 2: Validation

Core principle: extract the operating system behind the source, not just the content or answer.

---

A-Stage 1: Source Intake

Input: person, author, method, organization, domain, URL, repo, or local files.

Create package workspace at output/<target-slug>-skill/:

output/<target-slug>-skill/
├── README.md
├── SKILL.md.draft
├── references/             # agent reports + exemplars
├── intermediate/           # structured data
├── examples/               # persona: required; tool: optional
└── validation/             # only for deep mode (Phase 8)

templates/ 不再预建——输出模板在 LEAP 共享层，产出 skill 不需要。 validation/ 只在 depth=deep 且 Phase 8 执行时创建。

Write intermediate/open_world_task.json with target, goal, sources, depth_level:

depth_level	Effect	Use case
quick	Agent count ≤3, skip Phase 8, mark quality: draft	Rapid prototype
standard	No correction to auto-assessment, Phase 8 suggested	Daily use (default)
deep	Agent count upper bound +1 (≤8), Phase 8 required, dual review	Release quality

---

A-Stage 2: Intake Assessment

Step 1: Source Modality Analysis

Classify every source by what it can reveal:

Modality	Examples	Reveals
transcript_interview	podcasts, video captions, Q&A	spontaneous reasoning, analogies, changed positions
longform_text	books, papers, essays, newsletters	core arguments, methodology, narrative structure
secondary_criticism	reviews, biographies, analysis	external perspective, blind spots, competing views
video_subtitle	YouTube, B站 captions	speech patterns, unscripted reasoning
social_media	posts, threads	expression patterns, real-time reactions
code_repo	git repos, PRs	architecture patterns, API contracts, testing strategy

For each modality present, note what operations it could reveal. Skip absent ones.

Step 2: Domain Inference

Read domains/<domain>/domain.md to confirm. Record primary + secondary domains.

Step 3: Evidence Depth Assessment

Don't count sources — assess their density.

• ≥3 high-density sources across ≥2 modalities → rich (5-8 agents)
• 1-2 high-density sources → moderate (3-5 agents)
• 0 high-density, all medium/low → sparse (2-3 agents)

Apply depth_level correction: quick→floor+cap at 3, standard→no change, deep→ceiling+1, cap at 8.

Step 4: Skill Mode Determination

Target type	Skill mode	Behavior
Person / author / expert	persona	First-person role-play. Has 角色扮演规则, 身份, 我怎么说话, 决策启发式
Domain / method / organization	tool	Third-person analytical. Has Activation Rules, Agentic Protocol, Operation Models

---

A-Stage 3: Research Plan Design

How to select dimensions

1. Read primary domain pack: domains/<primary-domain>/domain.md → Research dimensions
2. If persona, also read domains/persona-os/domain.md. This cross-cutting layer provides OS extraction lenses (decision under constraint, failure processing, value conflict resolution, attention allocation, etc.).
3. Cross dimensions with source modality: match → active, no match → skip
4. Apply evidence depth cap: sparse→merge, moderate→1:1, rich→split
5. Derive new dimensions from source if domain pack menus don't cover what's visible

Self-Check Before Writing research_plan.json

1. Depth match — Does agent_count fall within the depth range?
2. Modality coverage — Are source_modalities_used actually present?
3. Dimension coverage — Is every active dimension covered?
4. Merge intent — Deliberate or lazy? Document the rationale.

Search direction must target dilemmas

Good: "What was the hardest decision at [event]? What options did they have?"
Bad:  "What is their leadership style?"

Every agent's search_direction should name a specific moment, event, or decision that can be traced to a verifiable source.

Plan-Only Mode Stop Point

If invoked with 只到 Stage 3 or stop_after_stage: 3:

写完 research_plan.json 后立即停止。输出：

Research plan 已生成，保存在 intermediate/research_plan.json。

[N] 个 agent，维度：
  R1 — [dimension]: [search_direction 摘要]
  R2 — [dimension]: [search_direction 摘要]
  ...

不要进入 Stage 4。等待 Skill-Alchemy 返回确认/调整后的指令。

---

A-Stage 4: Research Swarm

Resume mode: 如果在 从 Stage 4 继续 模式下调用，直接从已有的 intermediate/research_plan.json 读取 agent 配置。跳过 Stage 1-3。

Launch N agents in parallel. Each agent writes references/R<NN>-<agent_id>.md:

Status: pass (or warning / fail)

## Key Findings
## Dilemma Decision Cases (≥2 required)
  ### Case N: [one-line summary]
  - 困境 (Dilemma): specific conflict or hard choice
  - 约束 (Constraints): what limited their options
  - 决策步骤 (Decision Steps): what they did, step by step
  - 结果 (Outcome): what happened
  - 可提取的操作 (Extractable Operation): generalizable rule/pattern/heuristic
## Evidence Sources (source_id, type, confidence)
## Supported Candidate Operations
## Rejected or Weak Candidate Operations
## Target-specific Patterns
## Boundaries and Uncertainties
## Recommendations for Later Skill Compilation

Agent Contract: Every report begins with Status: pass / warning / fail. Dilemma Decision Cases are the most important section for persona targets — they are the raw material from which mental models and heuristics are built.

Agent Timeout Rule: If any research agent hasn't produced a report within 10 minutes, do not wait. Proceed with completed agents. Gate 1 checks:

• Persona: ≥4 total Dilemma Cases across all completed reports → pass. <4 → downgrade depth to quick and relaunch with fewer (≤2) agents.
• Tool: ≥2 total Dilemma Cases → pass. <2 → same downgrade.
• Mark missing agents in merge_report.json: "agents_lost": ["R2", "R3"].

---

A-Gate 1: Research Merge

Agent uses data-analysis skill to process reports:

1. Read all R1-Rn reports
2. Extract Status + all sections
3. Dilemma Case gate: persona targets — per-report 0 cases = warning, combined total <4 = fail
4. Build merge_report.json, evidence_matrix.json, operation_candidates.json
5. Detect cross-report contradictions
6. Write merge-summary.md

---

A-Stage 5: Exemplar Discovery

从 skills.sh 的公开 skill 池中实时检索最佳 exemplar，注入 Compilation 做 few-shot 结构参照。

检索流程

1. 使用 find-skills 搜索： 调 skills.sh 的 find-skills 接口，用目标的关键词搜索。返回 top 20 个候选 skill_key。

2. 并发下载候选 SKILL.md + 机械评分：

   • 对 20 个候选并发下载 SKILL.md（GitHub raw）
   • 每个跑 python3 scripts/score_skill.py --skill <path> --json
   • 按 quality_score 排序：elite（≥11）优先，draft（<9）丢弃

3. 自动择优注入：

   • 取 top 3-5 个精英 exemplar（elite ≥11 优先）
   • 写入 references/exemplars/exemplar-<N>.md
   • 评分结果写入 references/exemplar_candidates.json 备查

4. 取不到时的处理：

   • 候选全部 <9 分 → 扩大搜索词重新搜一轮
   • 两轮仍无精英 → 标记 exemplar_discovery.json 的 status: "degraded"
   • 但必须尝试到至少一个 exemplar。 这是编译质量的基础。

---

A-Stage 6: Synthesis

#	Agent	What it does
S1	Taxonomy Alignment	Separate generic lenses from evidence-backed ops. Promote only source-backed.
S2	SOP Compression	Compress operations into model cards. Primary input: Dilemma Decision Cases. Each card's Action must trace to case decision steps.
S3	Package Design	Design activation rules, protocol, output modes, templates, boundary rules.

---

A-Stage 7: Skill Compilation

Compile final package from all research + synthesis reports + exemplars.

Compilation inputs（按优先级）

1. skill-grammar.md — 必须先 Read。 对照反模式清单逐条检查输出。对照精英模板确认 section 结构。编译前不读 skill-grammar → 禁止编译。
2. Fetched exemplars — 必须注入。 精英 skill 的结构是 few-shot 参照。至少参照 1 个 exemplar 的 section 组织方式。
3. All R+S reports (content + evidence)
4. Domain pack (domain-specific conventions)

Package contents

<skill-name>/
├── SKILL.md                   # lean entry point — runtime loaded
├── skill.json                 # metadata (name, version, skill_mode, domain)
├── README.md                  # storefront (see template in shared layer)
├── references/
│   ├── sop_models.md          # full operation model cards (runtime on-demand)
│   └── research_notes.md      # human-readable evidence summary
├── examples/
│   └── demo_conversation.md   # persona: 3-4 scenarios (required)
├── intermediate/              # pipeline audit trail
└── validation/                # quality reports

运行时只加载 SKILL.md。sop_models.md 由运行时协议按需 Read。 R1-Rn、S1-S3、intermediate/、validation/ 为审计文件。

Persona MUST include examples/demo_conversation.md（3-4 场景：常见/边界/拒答）。缺失 → fail。 Tool 模式不强制要求 examples/。

编译后清理

编译完成且自评通过后，删除中间产物，保持 output 干净：

1. 删除 references/exemplar_candidates.json — 临时评分文件，编译已用
2. 删除 references/exemplars/ — 中间参照副本，编译已用
3. 删除空目录 — validation/（如果空）、任何其他空目录
4. 保留 R*.md（研究证据）、intermediate/（审计追踪）、产出包
5. intermediate/ 保留（审计追踪），references/R*.md 保留（证据溯源）

编译后自评（/10 分）

编译完成后，对照 skill-grammar.md 精英 checklist 自评打分：

检查项	分值
≥5 个具体步骤（chain-of-steps 或运行时协议）	2
有边界声明（Boundary Rules / 边界）	2
description 含触发词（"Use when" / 触发场景）	2
100-350 行篇幅	1
反模式 0 命中	1
Persona: 有「我绝不会说」+「我的标志句式」	2
满分	10

写入 intermediate/self_quality_report.json：

{
  "score": 8,
  "max": 10,
  "breakdown": {"steps": 2, "boundary": 2, "trigger_desc": 2, "length": 1, "anti_patterns": 1},
  "persona_extras": {"forbidden_phrases": true, "signature_line": true},
  "grade": "standard"
}

评分标准：

• ≥7 分 → quality: standard，可发布
• <7 分 → quality: draft，警告用户「自评低于发布标准」

此评分不替代 Phase 8 验证，但提供了可对比的基线。 Ablation 实验时跑同一个目标 with/without exemplar，对比分数。

---

A-Gate 2: Validation

Mechanical validation

python3 scripts/quality_check.py --skill <package_dir>/SKILL.md

Checks: all required sections present, no forbidden patterns, skill.json parses.

Phase 8: Content Quality Validation (agent-driven)

Gating by depth_level:

depth_level	Phase 8	双 agent 交叉审核	失败处理
quick	跳过	跳过	—
standard	建议执行（不强制）	跳过	warning 写入 quality_report
deep	强制	强制	fail → 退回 Compilation 重修，最多 2 轮

双 agent 交叉审核（deep only）：启动两个独立 agent 分别审核，一个从内容质量角度、一个从声音一致性角度。

8.1 Known-position test (3 questions)

Agent 读生成的 SKILL.md 的 Core Operation Models，出 3 道该 skill 应能答对的题，实际回答并评分。≥2/3 pass。

检查：

• 回答是否遵循 Agentic Protocol / 运行时协议？
• 是否在 Boundary Rules 内？
• 是否引用了真实存在的 evidence？

8.2 Edge-case test (1 question)

出 1 道边界外的问题，验证是否诚实拒答而非编造。如果 skill 自信地回答了该拒绝的事 → fail。

8.3 Triple validation (nuwa-style)

对每个心智模型检查：

• 跨域复现：这个模型在 ≥2 个不同领域/话题中出现吗？仅在一个上下文出现 → 降级为启发式
• 生成力：能用它推断该 persona 对全新话题的立场吗？不能 → 太模糊
• 排他性：这和任何该领域的聪明人说的有区别吗？没有 → 共享领域智慧，非独有 OS

≤1/3 → reject。2/3 → medium。3/3 → high-confidence core。

8.4 Voice / decision heuristic consistency

• Activation Rules 覆盖真实使用场景（不只是生成场景）？
• 运行时协议可执行（不是 vague "consider X"）？
• 决策启发式 falsifiable？「Think long-term」不合格。
• 「我绝不会说」和「标志句式」都存在？缺任一 → fail。
• 能从 100 字回复识别出 persona 吗？

8.5 Loop guard

任一检查 fail → 退回 Skill Compilation 重修。最多 2 轮。2 轮后记录残余弱点，发布当前最佳版本。

8.6 Output

Write validation/content_quality_report.json:

{
  "known_position_test": {"passed": 3, "failed": 0, "status": "pass"},
  "edge_case_test": {"status": "pass"},
  "triple_validation": {"models_checked": 5, "passed_3of3": 3, "passed_2of3": 1, "rejected": 1},
  "voice_consistency": {"has_forbidden_phrases": true, "has_signature_line": true, "status": "pass"},
  "heuristic_falsifiability": {"checked": 4, "falsifiable": 4, "status": "pass"},
  "loop_count": 0,
  "overall": "pass"
}

Do not relax thresholds

Fix source segmentation, taxonomy alignment, or evidence binding. Don't lower thresholds to hide failures.

---

B 分支: 融合管线

method.skill (骨架) × subject.skill(s) (血肉) → output.skill

WEAVE is not a concatenator. If you can tell where one skill ends and another begins, the weave failed.

---

B-Step 1: Retrieve Skills

确认融合所需 skill 是否就绪：

primary: "采访技巧"        ← workflow 骨架
secondary: ["北斗导航"]    ← style/persona 来源
depth: "standard"

对每个所需 skill，按顺序检索：

1. 本地 output/ 目录（之前生成过的 skill）
2. 已安装 skill（~/.claude/skills/）
3. find-skills 在线搜索（skills.sh 公开 skill 池，语义搜索）
4. GitHub raw 在线拉取

如果 skill 不存在：

• 告诉用户需要先生成哪个 skill，建议用 A 分支蒸馏
• 或者让用户提供已有 skill 的路径

Skill 拉取后跑 python3 scripts/score_skill.py --skill <path> --json 确认质量。 draft（<9 分）skill 不应作为融合源——垃圾进垃圾出。

走 find-skills 找到候选时，输出评分结果到 references/fusion_candidates.json：

[
  {"skill_key": "xxx", "score": 12, "summary": "...", "recommended_role": "primary"},
  {"skill_key": "yyy", "score": 9, "summary": "...", "recommended_role": "secondary"}
]

Skill-Alchemy 会展示这些候选给用户确认。LEAP 不自己做交互。

---

B-Step 2: Parse

2.1 Parse primary skill (skeleton)

Extract:

• Workflow: every step, in order. Number them.
• Output format: what the skill produces at each step
• Decision points: if-then branches, conditional logic
• Constraints: what this skill cannot/will not do

The primary skill determines the structure of the output.

2.2 Parse secondary skill(s) (flesh)

For each secondary skill, extract:

• Role/persona: how they speak, their identity, their worldview (persona) OR their domain lens, their operation models (tool)
• Style elements: tone, rhythm, vocabulary, forbidden phrases, signature patterns
• Heuristics/decision rules: their falsifiable operating rules
• Constraints: what this skill cannot/will not do
• Evidence anchors: verifiable sources that back their patterns

The secondary skills determine the texture of the output.

---

B-Step 3: Weave

Fusion depth is controlled by depth_level.

quick — Style Injection

Each style element from secondary skills is injected into the primary workflow at the most relevant step. Minimal rewriting.

采访技巧 Step 3 "生成核心问题"
  → 注入北斗导航的提问风格：从具体经历切入、先建立共鸣再追问

standard — Structured Weave (default)

1. Rewrite the role. Create a new unified identity.

   • Bad: "你是北斗导航。你是一个采访者。"
   • Good: "你是北斗导航式采访策划助手。你学习并复用他的采访方法， 但不声称是他本人。"

2. Weave workflow × style. For each step in the primary workflow, embed relevant style/pattern from secondary skills.

   • Each style injection must cite its source skill section.
   • No step should feel "unstyled" — every step gets at least one texture element.

3. Merge constraints. Union of all source skill constraints. Remove duplicates. Flag conflicts (if primary says "do X" and secondary says "never do X").

4. Check for gaps. Are there steps in the workflow that no secondary skill has pattern coverage for? Mark as [通用模式] — filled by general best practices, not specific to any source.

deep — Weave + Gap Resolution

Same as standard, plus:

1. Detect conflicts. When two source skills contradict on a point, resolve explicitly. Default: primary skill wins on workflow decisions, secondary skill wins on style decisions. Document every conflict and resolution.

2. Fill gaps. For steps marked [通用模式], launch a lightweight research agent to find domain-specific patterns.

3. Cross-validation. Verify that every style claim in the output can be traced back to a specific section of a source skill. Verify that no constraint was dropped.

---

B-Step 4: Output

Generate output.skill using the SKILL.md templates in the shared layer below.

Role naming convention

• "北斗导航式采访策划助手" — "式" indicates derived, not identical
• "基于张一鸣产品观的决策框架" — "基于" indicates the source

Package contents

<skill-name>/
├── SKILL.md                   # lean entry point — runtime loaded
├── skill.json                 # metadata (name, version, skill_mode, source_skills)
├── README.md                  # storefront (see shared layer)
├── references/
│   └── sop_models.md          # full operation model cards (runtime on-demand)
├── examples/
│   └── demo_conversation.md   # persona: 3-4 scenarios (required)
└── validation/                # quality reports (deep mode only)

编译后清理

与 A 分支相同：

1. 删除 references/fusion_candidates.json（临时评分文件）
2. 删除所有中间参照文件（如有临时 exemplar 拷贝）
3. 删除空目录
4. 保留 references/（审计追踪）

---

B-Gate 1: Structural Validation

python3 scripts/quality_check.py --skill <package_dir>/SKILL.md

Checks: all required sections present, no forbidden patterns, skill.json parses.

---

B-Gate 2: Weave Quality Check

Skeleton Integrity (可机械检查)

• 所有 primary workflow 步骤都保留？少一个 → fail
• 步骤顺序未变？变了但没文档化 → warning
• 步骤数对比：output 步骤数 ≥ primary 步骤数

Style Injection (可机械检查)

• 每个 primary workflow 步骤至少嵌入了一个 secondary style 元素
• 每个 style 声明可追溯到源 skill（—北斗导航.skill §我怎么说话）
• 无虚构 style——每个声明都能在源 skill 里找到对应章节

Role Clarity (需 agent 判断)

• Role 描述用「式/风格的/基于/学习并复用」——从不声称身份
• 全文 role 一致，不切换人称

Constraint Preservation (可机械检查)

• 源 skill 约束数 vs 输出约束数：输出 ≥ max(各源约束数)，去重后不应减少
• 无矛盾约束（如有冲突需显式文档化并给出解决理由）

Anti-stitching (需 agent 判断)

• 全文一个声音，找不到「这里换了一个 skill」
• 过渡自然，不说「根据 skill A...skill B 说...」

---

B-Gate 3: Phase 8 Content Validation

Gating by depth_level:

depth	Phase 8	双审核	失败处理
quick	跳过	跳过	—
standard	建议	跳过	warning
deep	强制	强制	退回重修 ≤2轮

融合专项检查

8.1 Fusion identity test (3 questions): Agent 出 3 道跨源 skill 的问题——答案需要同时用到 primary 和 secondary 的知识。 ≥2/3 能正确融合回答 → pass。

8.2 Boundary non-leakage test (1 question): 出 1 道超出所有源 skill 约束的问题。验证输出诚实拒答而非编造。

8.3 Source traceability: 每个 output 声明是否能追溯到具体的源 skill 章节？≥90% 可追溯 → pass。

8.4 Anti-stitching blind test: 将输出与两个源 skill 拼接版对比。独立 reviewer 能否区分哪个是 WEAVE 输出、 哪个是拼接版？无法区分 → fail。若 reviewer 区分准确率 ≤60% → 编织成功。

8.5 Loop guard: 任一 fail → 退回 B-Step 3 Weave 重修。最多 2 轮。

---

共享层

A 和 B 分支共用以下模板和基础设施。

SKILL.md 输出模板

Tool 模式 (skill_mode: "tool") — 7 个必选章节

## Activation Rules
触发 + 不触发的具体例子。各列 4-5 个场景。

## Agentic Protocol
可执行步骤，不是「考虑 X」而是「做 X 然后 Y」：
Step 1: 阶段判定
Step 2: 模型匹配（读 sop_models.md）
Step 3: 执行诊断
Step 4: 输出（选 output mode）

## Core Operation Models
H1-Hn 摘要表。格式：
| # | 模型 | 核心命题 | 主要来源 |
|---|------|---------|---------|
| H1 | **模型名** | 一句话 | 来源 |
完整卡片在 references/sop_models.md。

## Output Style
- 「先给一句话结论，再展开。不把整个模型卡片贴出来。」
- 用自然段落，不用 markdown 表格（除非用户明确要对比表）
- 引用来源时说「PG 在 2012 年文章里指出...」不说「根据 references/sop_models.md 的 H1」
- 禁止词：「根据框架分析...」「按照模型卡片...」「让我来系统分析...」
- 回答完就停，不问「需要我进一步展开吗」

## Output Modes
| Mode | 触发条件 | 输出结构 |
|------|---------|---------|
| ... | ... | ... |
4-7 种模式。

## Boundary Rules
7-8 条编号规则。覆盖：证据边界、适用范围、禁止事项、版本截止。

## References
指针表：sop_models.md + research_notes.md + R 报告 + S 报告。

Persona 模式 (skill_mode: "persona") — 8 个必选章节 + 1 个可选

## 角色扮演规则（最重要，放第一）
直接以[人名]的身份回应。用「我」说话。
读者已经知道你是谁。不要每轮都交代出身背景。
用我的语气、节奏、词汇说话。不确定的时候在角色里犹豫。
如果有人明显第一次和你说话，简短带一句免责。
说「退出角色」或「切回正常」就退出。

## 身份
3-5 句，第一人称。不是生平——是一个握手。
只写对理解这个人看世界方式最重要的几个事实。

## 我看世界的方式
3-5 个心智模型，每个一段不超过 5 行。
对话式段落，不是结构化卡片。用这个人的声音写。
证据和局限在 references/sop_models.md，不内联。

## 我怎么说话
第一句是最强的输出格式约束：
「我是[身份]，不是[对立身份]。不分点论述，不列一二三四。」
句式（长短、问答比例）· 词汇（高频、禁用）· 节奏（先结论/先铺垫）
幽默（自嘲/讽刺/荒诞/无）· 确定性（我不确定型/显然型）
「我绝不会说」（2-3 句这个人永远不会说的话——比正向描述更能建立辨识度）
「我的标志句式」（1 句让人一眼认出的标志性表达）
引用习惯 + 禁忌

## 决策启发式
3-5 条。格式：规则名 — 一句话描述 + 适用场景。每条必须 falsifiable。
❌「Think long-term」（不可证伪）
✅「如果一个三分钟内想不清楚，放进 Too Hard 筐」（可证伪）
证据在 references/sop_models.md，不内联。

## 运行时协议
5 步 SOP 驱动流程：
1. 匹配模型：Read references/sop_models.md，扫描「When to use」找到匹配的模型卡片
2. 按模型行动：严格按 Action 步骤组织回答，引用 Evidence 标注出处
3. 检查边界：对照 Boundary 字段，越界诚实拒答，Failure mode 主动避开
4. 事实性问题先查：涉及具体事实 → WebSearch → 用心智模型框架分析
5. 纯经验判断直接回：价值观/闲聊 → 直接回应，超出认知 → 「这我不专业，不乱说」

## 边界
~5 行。不能代表真人。信息截止日期。
最后一行标注：深度: quick/standard/deep · 质量: draft/standard/validated

## 参考
指针：references/sop_models.md + references/research_notes.md

## 价值观（可选）
只在人物有强烈、独特、公开记录的价值观时加。不是 filler。

Every persona skill MUST include examples/demo_conversation.md — 3-4 short conversation scenarios: a common ask, an edge case, a boundary refusal.

Every persona skill MUST include a README.md using this template:

# [人名] · [英文名或标签]

> 「[一句最能代表这个人的话]」

[一句话：谁，做了什么，为什么值得听。不超过30字。]

## 安装
    cp -r [skill] ~/.claude/skills/[name]/

## 触发方式
[3-5个典型触发场景，用自然语言描述]

## 心智模型
| # | 模型 | 一句话 |
|---|------|--------|
| 1 | [名称] | [15字内] |

## 他会怎么说
- **标志句式**：[一句话]
- **绝不会说**：[一句话] / [一句话] / [一句话]

## 免责
基于公开资料提炼的模拟角色，不代表本人立场。信息截止 [年份]月。

README rules: top quote MUST be real and verified. Keep under 40 lines. 心智模型 table = exactly the same models as 我看世界的方式。 标志句式/绝不会说 = exactly the same as 我怎么说话.

Forbidden in all modes

• Exact file paths from one benchmark instance
• Oracle/verifier logic
• Copied protected expression
• For persona: do not fabricate quotes, do not claim generated text IS the person's
• Progressive disclosure: SKILL.md is the lean entry point; detailed evidence in references/

---

共享基础设施

• domains/ — 12 domain packs（11 主域 + persona-os），A 分支 Stage 3 研究维度选择
• references/skill-grammar.md — skill 写作方法论（skills.sh 数据验证），A/B 分支编译必读
• scripts/score_skill.py — 13 分机械评分，A-Stage 5 / B-Step 1 运行时质量过滤
• scripts/quality_check.py — A/B 分支 Gate 机械验证
• scripts/download_subtitles.sh + scripts/srt_to_transcript.py — 视频源处理（按需使用）
• scripts/build_corpus.py + scripts/build_component_index.py — 构建 skill-grammar 的数据挖掘工具（开源构建用，运行时不需要）
• find-skills（skills.sh）— 在线 skill 语义检索，A-Stage 5 / B-Step 1 的候选发现层