do-parallel
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ChineseParallel Multi-Perspective Analysis
并行多视角分析
$ARGUMENTS - Target agent/skill name + source material file path
$ARGUMENTS - 目标Agent/Skill名称 + 源材料文件路径
Operator Context
操作者上下文
This skill operates as an operator for intensive multi-perspective analysis, configuring Claude's behavior for true parallel independence across 10 analytical agents. It implements the Fan-Out / Fan-In architectural pattern -- dispatch independent agents in parallel, collect results, synthesize into unified recommendations -- with Domain Intelligence embedded in each perspective's focus constraints.
本Skill作为密集型多视角分析的操作者,配置Claude的行为以实现10个分析Agent的真正并行独立性。它采用Fan-Out / Fan-In架构模式——并行调度独立Agent、收集结果、整合成统一建议——每个视角的聚焦约束中都嵌入了领域智能。
Hardcoded Behaviors (Always Apply)
硬编码行为(始终生效)
- CLAUDE.md Compliance: Read and follow repository CLAUDE.md before execution
- Over-Engineering Prevention: Apply only Priority 1 and Priority 2 synthesized rules. Do not invent improvements beyond what the source material supports. No speculative enhancements.
- True Parallel Independence: All 10 Task invocations MUST be in a single message. Each agent receives ONLY its assigned perspective with zero cross-contamination.
- Artifact Persistence: Save synthesis document and completion report to files. Context is ephemeral; artifacts persist.
- Token Budget Awareness: Estimate total cost before execution. Warn if source material exceeds 5,000 words (drives cost above 50,000 tokens).
- Validate Inputs First: Verify target agent/skill exists and source material is readable before spawning any agents.
- No Behavior Changes: Synthesized rules ADD depth. They NEVER remove or significantly alter existing working patterns in the target.
- CLAUDE.md合规性:执行前阅读并遵循仓库中的CLAUDE.md
- 过度设计预防:仅应用整合后的1级和2级优先级规则。不得在源材料支持范围外凭空创造优化方案,禁止推测性增强。
- 真正并行独立性:所有10个任务调用必须在单条消息中完成。每个Agent仅接收其分配的视角,完全避免交叉干扰。
- 工件持久化:将整合文档和完成报告保存到文件中。上下文是临时的,工件需永久留存。
- Token预算感知:执行前估算总成本。如果源材料超过5000字(会导致Token成本超过50000),需发出警告。
- 先验证输入:在生成任何Agent之前,验证目标Agent/Skill是否存在,源材料是否可读。
- 不修改现有行为:整合后的规则仅用于增加深度,绝不删除或大幅修改目标中已有的有效模式。
Default Behaviors (ON unless disabled)
默认行为(除非禁用否则生效)
- 10 Perspectives: Use all 10 analytical frameworks (see )
references/perspective-prompts.md - Priority-Based Application: Apply Must-Have rules first, then Should-Have. Skip Nice-to-Have unless user requests.
- Synthesis Before Application: Collect all 10 reports and synthesize before making any changes to the target.
- Completion Report: Generate detailed report showing impact, changes, and perspective contributions.
- Graceful Degradation: If agents time out, proceed with available results (3+ of 10 sufficient).
- Git Commit: Commit improvements with descriptive message after application.
- 10个视角:使用全部10种分析框架(详见)
references/perspective-prompts.md - 基于优先级的应用:先应用必备规则,再应用推荐规则。除非用户要求,否则跳过可选规则。
- 先整合再应用:收集所有10份报告并完成整合后,再对目标进行任何修改。
- 完成报告:生成详细报告,展示影响、变更内容以及各视角的贡献。
- 优雅降级:如果Agent超时,使用已有结果继续执行(10个中完成3个及以上即可)。
- Git提交:应用优化后,使用描述性消息提交变更。
Optional Behaviors (OFF unless enabled)
可选行为(除非启用否则禁用)
- Reduced Perspectives: Use 5 perspectives instead of 10 to halve token cost
- Dry Run Mode: Generate synthesis without applying changes to target
- Compare Mode: Analyze two sources and extract differences
- 减少视角数量:使用5个而非10个视角,将Token成本减半
- 试运行模式:生成整合结果但不应用到目标
- 对比模式:分析两个源材料并提取差异
What This Skill CAN Do
本Skill能实现的功能
- Extract comprehensive insights from complex source material through 10 independent lenses
- Synthesize cross-perspective patterns into prioritized improvement recommendations
- Apply synthesized rules to enhance an existing agent or skill
- Produce detailed reports showing which perspectives contributed to each improvement
- Detect patterns that single-threaded analysis misses due to cognitive anchoring
- 通过10个独立视角从复杂源材料中提取全面洞察
- 将跨视角模式整合成优先级明确的优化建议
- 应用整合后的规则来增强现有Agent或Skill
- 生成详细报告,展示各视角对每项优化的贡献
- 发现单线程分析因认知锚定而遗漏的模式
What This Skill CANNOT Do
本Skill不能实现的功能
- Replace inline analysis for simple or straightforward material (use for single-target improvements)
/do-perspectives - Operate without sufficient token budget (requires 25,000-63,000 tokens)
- Guarantee all 10 agents complete (network/timeout issues may reduce count)
- Generate value from poor source material (marketing fluff, auto-generated docs)
- Skip the synthesis phase and apply raw per-perspective rules directly
- 替代单线程分析处理简单或直白的材料(单目标优化请使用)
/do-perspectives - 在Token预算不足的情况下运行(需要25000-63000个Token)
- 保证10个Agent全部完成(网络/超时问题可能减少完成数量)
- 从质量低下的源材料中产生价值(如营销话术、自动生成的文档)
- 跳过整合阶段直接应用各视角的原始规则
Instructions
操作步骤
Phase 1: VALIDATE INPUTS
阶段1:验证输入
Goal: Confirm target exists and source material is suitable before spending tokens.
Step 1: Parse arguments
- Extract target agent/skill name (first argument)
- Extract source material path (second argument)
- If either argument is missing, report usage:
/do-parallel <target-name> <source-path>
Step 2: Validate target
bash
undefined目标:在消耗Token前,确认目标存在且源材料合适。
步骤1:解析参数
- 提取目标Agent/Skill名称(第一个参数)
- 提取源材料路径(第二个参数)
- 如果任一参数缺失,报告使用方法:
/do-parallel <target-name> <source-path>
步骤2:验证目标
bash
undefinedFor agents
验证Agent
ls agents/{target_name}.md
ls agents/{target_name}.md
For skills
验证Skill
ls skills/{target_name}/SKILL.md
- Determine if target is an agent or skill based on which path exists
- Read target file to understand current state, capture line count
- If target does not exist in either location, stop and report error
**Step 3: Validate source material**
```markdownls skills/{target_name}/SKILL.md
- 根据路径存在情况判断目标是Agent还是Skill
- 读取目标文件以了解当前状态,记录行数
- 如果目标在任一位置都不存在,停止并报告错误
**步骤3:验证源材料**
```markdownSource Material Assessment
源材料评估
File: [path]
Word count: [N] words
Estimated token cost: [N * 10 * 3] = [total] tokens
Quality indicators:
- Contains concrete examples (not just abstract claims)
- Has systematic structure (sections, progression)
- Demonstrates expertise (technical depth, nuanced explanations)
- Sufficient length (500+ words minimum)
Assessment: SUITABLE / UNSUITABLE
- Read source file, confirm it is non-empty
- Estimate word count. If over 5,000 words, warn about elevated token cost.
- If material fails 2+ quality indicators, recommend inline analysis instead and ask user to confirm
**Step 4: Estimate token budget**
| Component | Estimated Tokens |
|-----------|-----------------|
| 10 agents x source material | source_words x 10 x ~3 |
| 10 agent outputs | ~5,000 (500 words each) |
| Synthesis | ~3,000 |
| Application | ~5,000 |
| **Total** | **sum of above** |
If total exceeds 60,000 tokens, warn user and request confirmation before proceeding.
**Gate**: Target exists and is readable. Source material is present and substantive. Token estimate is acceptable. Proceed only when gate passes.文件:[路径]
字数:[N]字
估算Token成本:[N * 10 * 3] = [总计]个Token
质量指标:
- 包含具体示例(而非仅抽象声明)
- 具有系统结构(章节、递进逻辑)
- 体现专业度(技术深度、细致解释)
- 长度足够(至少500字)
评估结果:适合 / 不适合
- 读取源文件,确认非空
- 估算字数。如果超过5000字,警告Token成本过高。
- 如果材料未满足2个及以上质量指标,建议使用单线程分析并请求用户确认
**步骤4:估算Token预算**
| 组件 | 估算Token数量 |
|-----------|-----------------|
| 10个Agent × 源材料 | 源文件字数 × 10 × ~3 |
| 10个Agent输出结果 | ~5000(每个500字) |
| 整合阶段 | ~3000 |
| 应用阶段 | ~5000 |
| **总计** | **以上各项之和** |
如果总计超过60000个Token,警告用户并请求确认后再继续。
**准入条件**:目标存在且可读,源材料存在且有实质内容,Token估算在可接受范围内。仅当所有条件满足时才可继续。Phase 2: MULTI-PERSPECTIVE ANALYSIS (TRUE PARALLEL)
阶段2:多视角分析(真正并行)
Goal: Spawn 10 independent agents to analyze source material from distinct frameworks.
Step 1: Launch all 10 agents in a SINGLE message
Each agent receives:
- The FULL source material
- ONE assigned perspective (from )
references/perspective-prompts.md - The target name for contextualized recommendations
- Instructions to produce 200-500 words of focused analysis
The 10 perspectives are:
- Structural Analysis
- Clarity and Precision
- Technical Explanation Patterns
- Audience Assumption Patterns
- Evidence and Citation Strategy
- Narrative Progression
- Paragraph and Sentence Architecture
- Header and Signposting Strategy
- Complexity Management
- Limitation and Nuance Handling
Step 2: Collect results with timeout awareness
Wait for all agents to complete. Monitor using this decision tree:
Agent running > 5 minutes?
|
+-- YES --> Check progress (non-blocking)
| |
| +-- Making progress? --> Wait 2 more minutes
| |
| +-- Stuck on web fetch? --> Mark as timed out, proceed
|
+-- NO --> Continue waitingStep 3: Assess completeness
| Agents Completed | Action |
|---|---|
| 8-10 of 10 | Full pipeline, excellent coverage |
| 5-7 of 10 | Proceed, note gaps in report |
| 3-4 of 10 | Proceed with caution, synthesis will be thinner |
| 1-2 of 10 | Abort parallel approach, fall back to inline analysis |
| 0 of 10 | Critical failure, investigate cause |
Gate: At least 3 of 10 perspectives have returned results. Proceed only when gate passes.
目标:生成10个独立Agent,从不同框架分析源材料。
步骤1:在单条消息中启动全部10个Agent
每个Agent会收到:
- 完整的源材料
- 一个分配的视角(来自)
references/perspective-prompts.md - 目标名称,用于生成上下文相关的建议
- 生成200-500字聚焦分析内容的指令
10个视角分别是:
- 结构分析
- 清晰度与精准性
- 技术解释模式
- 受众假设模式
- 证据与引用策略
- 叙事递进
- 段落与句子架构
- 标题与标识策略
- 复杂度管理
- 局限性与细微差别处理
步骤2:带超时感知的结果收集
等待所有Agent完成。使用以下决策树监控:
Agent运行超过5分钟?
|
+-- 是 --> 检查进度(非阻塞)
| |
| +-- 正在推进? --> 再等待2分钟
| |
| +-- 卡在网络请求? --> 标记为超时,继续执行
|
+-- 否 --> 继续等待步骤3:评估完成度
| 完成的Agent数量 | 操作 |
|---|---|
| 8-10个 | 完整流程,覆盖全面 |
| 5-7个 | 继续执行,在报告中说明缺口 |
| 3-4个 | 谨慎继续,整合结果会较简略 |
| 1-2个 | 终止并行方式, fallback到单线程分析 |
| 0个 | 严重故障,调查原因 |
准入条件:10个视角中至少有3个返回结果。仅当满足条件时才可继续。
Phase 3: SYNTHESIZE
阶段3:整合
Goal: Merge 10 independent analyses into prioritized, unified recommendations.
Step 1: Create cross-reference matrix
For each rule extracted by any perspective, track which perspectives identified it:
markdown
| Rule | Struct | Clarity | Tech | Audience | Evidence | Narrative | Para | Header | Complex | Nuance | Count |
|------|--------|---------|------|----------|----------|-----------|------|--------|---------|--------|-------|
| [Rule A] | X | X | | X | | X | | | X | | 5 |
| [Rule B] | | | X | | X | | X | X | | X | 4 |Step 2: Identify common themes
- Patterns appearing in 4+ perspectives are high-confidence findings
- Patterns appearing in 7+ perspectives are near-certain insights
- Group related rules into themes (e.g., "Progressive Disclosure" may appear in Audience, Complexity, and Structure)
Step 3: Extract unique insights
- Single-perspective findings that are high-value despite low frequency
- These often represent the unique value of parallel independence
- Example: Only Narrative Progression spots a "hook-payoff" pattern that would strengthen an agent's introduction section
Step 4: Prioritize rules
markdown
undefined目标:将10个独立分析结果合并为优先级明确的统一建议。
步骤1:创建交叉引用矩阵
对于任一视角提取的每条规则,记录哪些视角识别出了该规则:
markdown
| 规则 | 结构 | 清晰度 | 技术 | 受众 | 证据 | 叙事 | 段落 | 标题 | 复杂度 | 细微差别 | 计数 |
|------|--------|---------|------|----------|----------|-----------|------|--------|---------|--------|-------|
| [规则A] | X | X | | X | | X | | | X | | 5 |
| [规则B] | | | X | | X | | X | X | | X | 4 |步骤2:识别共同主题
- 在4个及以上视角中出现的模式是高可信度发现
- 在7个及以上视角中出现的模式是近乎确定的洞察
- 将相关规则分组为主题(例如,「渐进式披露」可能出现在受众、复杂度和结构视角中)
步骤3:提取独特洞察
- 尽管出现频率低,但价值高的单视角发现
- 这些通常体现了并行独立性的独特价值
- 示例:只有叙事递进视角发现了「钩子-回报」模式,可用于强化Agent的介绍部分
步骤4:规则优先级排序
markdown
undefinedPriority Rules for [Target]
[目标]的优先级规则
Must-Have (Priority 1)
必备(优先级1)
Rules present in 7+ perspectives OR critical impact:
- [Rule] - Found in: [list of perspectives]
- [Rule] - Found in: [list of perspectives]
出现在7个及以上视角中,或影响重大的规则:
- [规则] - 来自:[视角列表]
- [规则] - 来自:[视角列表]
Should-Have (Priority 2)
推荐(优先级2)
Rules present in 4-6 perspectives OR high impact:
- [Rule] - Found in: [list of perspectives]
- [Rule] - Found in: [list of perspectives]
出现在4-6个视角中,或影响较大的规则:
- [规则] - 来自:[视角列表]
- [规则] - 来自:[视角列表]
Nice-to-Have (Priority 3)
可选(优先级3)
Rules present in 1-3 perspectives OR moderate impact:
- [Rule] - Found in: [perspective]
- [Rule] - Found in: [perspective]
**Step 5: Save synthesis document**
- Write to `skills/do-parallel/artifacts/synthesis-{target}-{date}.md`
- Include the cross-reference matrix, themes, and prioritized rules
- This artifact persists for future reference and can inform later analyses
**Gate**: Synthesis document exists with at least 3 Must-Have and 3 Should-Have rules. Proceed only when gate passes.出现在1-3个视角中,或影响中等的规则:
- [规则] - 来自:[视角]
- [规则] - 来自:[视角]
**步骤5:保存整合文档**
- 写入`skills/do-parallel/artifacts/synthesis-{target}-{date}.md`
- 包含交叉引用矩阵、主题和优先级规则
- 该工件将永久留存,用于未来参考和后续分析
**准入条件**:整合文档存在,且包含至少3条必备规则和3条推荐规则。仅当满足条件时才可继续。Phase 4: APPLY
阶段4:应用
Goal: Improve the target agent/skill using synthesized recommendations.
Step 1: Read current target state
markdown
undefined目标:使用整合后的建议优化目标Agent或Skill。
步骤1:读取目标当前状态
markdown
undefinedBefore State
优化前状态
Target: [name]
Type: [agent/skill]
Lines: [N]
Sections: [list of H2/H3 sections]
Version: [current version]
**Step 2: Plan application**
Map each Priority 1 and Priority 2 rule to a specific location in the target:
```markdown目标:[名称]
类型:[Agent/Skill]
行数:[N]
章节:[H2/H3章节列表]
版本:[当前版本]
**步骤2:规划应用方案**
将每个优先级1和优先级2的规则映射到目标中的具体位置:
```markdownApplication Plan
应用规划
| Rule | Action | Target Section | Risk |
|---|---|---|---|
| [Rule 1] | Add subsection | Operator Context | LOW |
| [Rule 2] | Enhance existing | Instructions Phase 2 | LOW |
| [Rule 3] | Add new section | After Anti-Patterns | MEDIUM |
**Step 3: Apply Priority 1 rules**
- Add or enhance sections based on Must-Have recommendations
- Preserve all existing working patterns
- After each rule application, verify target file is still valid markdown
**Step 4: Apply Priority 2 rules**
- Add Should-Have enhancements where they integrate naturally
- Do NOT force rules that conflict with existing patterns
- If a Should-Have rule conflicts with an existing pattern, document the conflict in the report and skip
**Step 5: Commit changes**
- Create descriptive git commit explaining what was improved and from what source
- Bump version if target is a skill (e.g., 1.0.0 to 1.1.0)
**Gate**: Target file has been modified. Changes preserve existing behavior. Before/after diff shows additions only (no deletions of existing content). Proceed only when gate passes.| 规则 | 操作 | 目标章节 | 风险 |
|---|---|---|---|
| [规则1] | 添加子章节 | 操作者上下文 | 低 |
| [规则2] | 增强现有内容 | 操作步骤阶段2 | 低 |
| [规则3] | 添加新章节 | 反模式之后 | 中 |
**步骤3:应用优先级1规则**
- 根据必备建议添加或增强章节
- 保留所有现有有效模式
- 每条规则应用后,验证目标文件仍是有效的Markdown
**步骤4:应用优先级2规则**
- 在自然整合的地方添加推荐增强内容
- 绝不强制应用与现有模式冲突的规则
- 如果推荐规则与现有模式冲突,在报告中记录冲突并跳过该规则
**步骤5:提交变更**
- 创建描述性Git提交信息,说明优化内容及来源
- 如果目标是Skill,升级版本(例如从1.0.0到1.1.0)
**准入条件**:目标文件已修改,变更保留了现有行为,前后对比显示仅添加了内容(未删除现有内容)。仅当满足条件时才可继续。Phase 5: VERIFY AND REPORT
阶段5:验证与报告
Goal: Confirm improvements are sound and document the full analysis.
Step 1: Verify target integrity
markdown
undefined目标:确认优化合理,并记录完整分析过程。
步骤1:验证目标完整性
markdown
undefinedIntegrity Check
完整性检查
YAML frontmatter valid: [YES/NO]
Sections preserved: [list any missing sections]
Before lines: [N]
After lines: [M]
Net change: +[M-N] lines
Verification:
- All original H2 sections still present
- All original H3 sections still present
- No content was deleted (only additions)
- Markdown renders correctly
If any check fails, revert the problematic change and re-apply.
**Step 2: Generate completion report**
Use template from `references/perspective-prompts.md`. The report MUST include:
- Per-perspective key insights (one sentence each)
- Cross-reference showing which perspectives contributed to each improvement
- Before/after comparison (line counts, section counts)
- Estimated token usage breakdown
- Recommendations for future improvements
**Step 3: Save completion report**
- Write to `skills/do-parallel/artifacts/report-{target}-{date}.md`
- Present summary to user in conversation
**Step 4: Present results**
```markdownYAML前置元数据有效:[是/否]
章节保留情况:[列出缺失的章节]
优化前行数:[N]
优化后行数:[M]
净变化:+[M-N]行
验证项:
- 所有原始H2章节仍存在
- 所有原始H3章节仍存在
- 未删除任何内容(仅添加)
- Markdown渲染正常
如果任何检查项失败,回滚问题变更并重新应用。
**步骤2:生成完成报告**
使用`references/perspective-prompts.md`中的模板。报告必须包含:
- 每个视角的关键洞察(各一句话)
- 交叉引用,展示各视角对每项优化的贡献
- 前后对比(行数、章节数)
- 估算Token使用明细
- 未来优化建议
**步骤3:保存完成报告**
- 写入`skills/do-parallel/artifacts/report-{target}-{date}.md`
- 在对话中向用户展示摘要
**步骤4:呈现结果**
```markdownParallel Analysis Complete
并行分析完成
Target: [name]
Source: [source path]
Perspectives completed: [N] of 10
Rules extracted: [total across all perspectives]
Rules applied: [Priority 1 count] Must-Have + [Priority 2 count] Should-Have
Lines added: +[count]
New sections: [count]
Full report: skills/do-parallel/artifacts/report-{target}-{date}.md
Synthesis: skills/do-parallel/artifacts/synthesis-{target}-{date}.md
**Gate**: Completion report exists. Target file is valid. All phases documented.
---目标:[名称]
源材料:[源路径]
完成的视角数量:[N]/10
提取的规则总数:[所有视角的规则总数]
应用的规则数量:[优先级1数量]条必备 + [优先级2数量]条推荐
新增行数:+[数量]
新增章节数:[数量]
完整报告:skills/do-parallel/artifacts/report-{target}-{date}.md
整合文档:skills/do-parallel/artifacts/synthesis-{target}-{date}.md
**准入条件**:完成报告存在,目标文件有效,所有阶段均已记录。
---Examples
示例
Example 1: Improve Agent from Article
示例1:通过文章优化Agent
User says:
Actions:
/do-parallel technical-journalist-writer expert-writing-guide.md- Validate target agent exists, read source article (VALIDATE)
- Spawn 10 agents analyzing article from 10 perspectives (ANALYZE)
- Synthesize: 5 Must-Have rules, 7 Should-Have rules (SYNTHESIZE)
- Apply Priority 1 and 2 rules to agent file (APPLY)
- Generate report showing +180 lines added (VERIFY) Result: Agent enhanced with synthesized writing patterns from 10 independent analyses
用户输入:
操作:
/do-parallel technical-journalist-writer expert-writing-guide.md- 验证目标Agent存在,读取源文章(验证阶段)
- 生成10个Agent从10个视角分析文章(分析阶段)
- 整合:5条必备规则,7条推荐规则(整合阶段)
- 应用优先级1和2规则到Agent文件(应用阶段)
- 生成报告,显示新增180行(验证阶段) 结果:Agent通过10个独立分析的写作模式得到增强
Example 2: Improve Skill from Documentation
示例2:通过文档优化Skill
User says:
Actions:
/do-parallel systematic-debugging postgres-debugging-guide.md- Validate skill exists, assess documentation quality (VALIDATE)
- Launch 10 parallel analyses of PostgreSQL debugging patterns (ANALYZE)
- Synthesize database-specific debugging rules (SYNTHESIZE)
- Add new patterns to debugging skill references (APPLY)
- Report: 8 of 10 perspectives contributed new rules (VERIFY) Result: Debugging skill gains domain-specific PostgreSQL patterns
用户输入:
操作:
/do-parallel systematic-debugging postgres-debugging-guide.md- 验证Skill存在,评估文档质量(验证阶段)
- 启动10个并行分析,研究PostgreSQL调试模式(分析阶段)
- 整合数据库特定的调试规则(整合阶段)
- 将新模式添加到调试Skill的参考内容中(应用阶段)
- 报告:10个视角中有8个贡献了新规则(验证阶段) 结果:调试Skill获得了领域特定的PostgreSQL模式
Token Budget Management
Token预算管理
This is a high-cost skill. Understanding and managing token usage is essential.
本Skill的Token成本较高,理解并管理Token使用至关重要。
Cost Breakdown
成本明细
| Phase | Token Range | Notes |
|---|---|---|
| Phase 1: Validate | 500-1,000 | Reading target + source |
| Phase 2: Analysis | 20,000-50,000 | 10 agents x (source + output) |
| Phase 3: Synthesize | 2,000-5,000 | Cross-reference + prioritization |
| Phase 4: Apply | 3,000-8,000 | Reading target + modifications |
| Phase 5: Verify | 1,000-2,000 | Integrity checks + report |
| Total | 26,500-66,000 | 3-5x inline analysis cost |
| 阶段 | Token范围 | 说明 |
|---|---|---|
| 阶段1:验证 | 500-1000 | 读取目标和源材料 |
| 阶段2:分析 | 20000-50000 | 10个Agent ×(源材料 + 输出) |
| 阶段3:整合 | 2000-5000 | 交叉引用 + 优先级排序 |
| 阶段4:应用 | 3000-8000 | 读取目标 + 修改 |
| 阶段5:验证 | 1000-2000 | 完整性检查 + 报告 |
| 总计 | 26500-66000 | 是单线程分析成本的3-5倍 |
When Cost Is Justified
成本合理的场景
Use do-parallel when:
- Source material is difficult and hard to grasp from a single reading
- Multiple independent interpretations could reveal hidden patterns
- The target agent/skill is high-impact and warrants deep investment
- Token budget has room for 30,000-60,000 tokens
Use inline analysis (2,000-10,000 tokens) when:
- Source material is straightforward with obvious patterns
- A single reading captures the key insights
- Token budget is constrained
- Routine incremental improvement is the goal
当满足以下条件时使用do-parallel:
- 源材料难以理解,单次阅读无法掌握
- 多个独立解读可能揭示隐藏模式
- 目标Agent/Skill影响重大,值得深度投入
- Token预算允许消耗30000-60000个Token
当满足以下条件时使用单线程分析(2000-10000个Token):
- 源材料简单,模式明显
- 单次阅读即可获取关键洞察
- Token预算紧张
- 目标是常规增量优化
Cost Estimation Formula
成本估算公式
Estimated tokens = (source_words * 3 * 10) + 15,000
Example: 2,000-word article
= (2,000 * 3 * 10) + 15,000
= 60,000 + 15,000
= ~75,000 tokens (HIGH - consider trimming source or reducing perspectives)
Example: 800-word article
= (800 * 3 * 10) + 15,000
= 24,000 + 15,000
= ~39,000 tokens (ACCEPTABLE)估算Token数 = (源文件字数 × 3 × 10) + 15000
示例:2000字文章
= (2000 × 3 × 10) + 15000
= 60000 + 15000
= ~75000个Token(高成本 - 考虑精简源材料或减少视角)
示例:800字文章
= (800 × 3 × 10) + 15000
= 24000 + 15000
= ~39000个Token(可接受)Error Handling
错误处理
Error: "Target Agent/Skill Not Found"
错误:「目标Agent/Skill未找到」
Cause: Name mismatch or typo in first argument
Solution:
- List available agents with
ls agents/*.md - List available skills with
ls skills/*/SKILL.md - Retry with exact name matching repository
原因:第一个参数名称不匹配或拼写错误
解决方案:
- 使用列出可用Agent
ls agents/*.md - 使用列出可用Skill
ls skills/*/SKILL.md - 使用仓库中的准确名称重试
Error: "Source Material Too Short or Empty"
错误:「源材料过短或为空」
Cause: File path wrong, file empty, or material lacks depth
Solution:
- Verify file path is absolute and file exists
- If material is under 500 words, it likely lacks sufficient patterns
- Consider using inline analysis instead of parallel (lower cost, similar value for thin material)
原因:文件路径错误、文件为空或材料缺乏深度
解决方案:
- 验证文件路径为绝对路径且文件存在
- 如果材料不足500字,可能缺乏足够的分析模式
- 考虑使用单线程分析替代并行分析(成本更低,对薄材料的价值相当)
Error: "Agents Timing Out"
错误:「Agent超时」
Cause: Source material too large, network issues, or agent stuck on web fetch
Solution:
- Check if source exceeds 10,000 words (reduce or split)
- After 5 minutes, check agent progress with non-blocking query
- Proceed with completed perspectives if 3+ have returned
- See graceful degradation table in Phase 2, Step 3
原因:源材料过大、网络问题或Agent卡在网络请求
解决方案:
- 检查源材料是否超过10000字(精简或拆分)
- 5分钟后,使用非阻塞查询检查Agent进度
- 如果完成3个及以上视角,使用已有结果继续
- 参考阶段2步骤3的优雅降级表格
Error: "Synthesis Has Insufficient Rules"
错误:「整合结果规则不足」
Cause: Source material lacked depth, or perspectives returned shallow analysis
Solution:
- Review agent outputs for quality (are they 200-500 words with concrete patterns?)
- If most outputs are thin, the source material is unsuitable for parallel analysis
- Consider switching to inline analysis with a focused prompt
- Report to user: "Source material did not yield sufficient patterns for 10-perspective analysis"
原因:源材料缺乏深度,或视角返回的分析内容浅显
解决方案:
- 检查Agent输出质量(是否为200-500字的具体模式分析?)
- 如果多数输出内容单薄,说明源材料不适合并行分析
- 考虑切换到带聚焦提示的单线程分析
- 告知用户:「源材料无法为10视角分析提供足够的模式」
Anti-Patterns
反模式
Anti-Pattern 1: Using Parallel for Simple Material
反模式1:对简单材料使用并行分析
What it looks like: Running 10 agents on a 200-word README
Why wrong: Token cost of 25,000+ for material that inline analysis handles in 2,000 tokens. No depth to analyze from 10 angles.
Do instead: Use for single-target improvements or simpler inline analysis. Reserve do-parallel for complex, hard-to-grasp material.
/do-perspectives表现:对200字的README运行10个Agent
问题:消耗25000+Token处理单线程分析仅需2000Token的材料,10个视角没有深度可分析
正确做法:单目标优化或简单分析使用,仅对复杂、难以理解的材料使用do-parallel
/do-perspectivesAnti-Pattern 2: Applying All Rules Without Prioritization
反模式2:不做优先级排序就应用所有规则
What it looks like: Dumping all 30-50 extracted rules into the target without filtering
Why wrong: Low-frequency rules may conflict with existing patterns. Quantity overwhelms quality. Target becomes bloated.
Do instead: Apply Priority 1 first, then Priority 2. Skip Priority 3 unless explicitly requested.
表现:将提取的30-50条规则全部导入目标,不做筛选
问题:低频率规则可能与现有模式冲突,数量大于质量,导致目标臃肿
正确做法:先应用优先级1规则,再应用优先级2规则,除非明确要求否则跳过优先级3规则
Anti-Pattern 3: Skipping Synthesis Phase
反模式3:跳过整合阶段
What it looks like: Reading each agent report and applying rules one perspective at a time
Why wrong: Cross-perspective patterns are the primary value. Applying per-perspective rules misses common themes and introduces contradictions.
Do instead: Always collect all reports, identify common themes, then create unified recommendations before touching the target.
表现:逐个读取Agent报告并逐视角应用规则
问题:跨视角模式是并行分析的核心价值,逐视角应用会错过共同主题并引入矛盾
正确做法:收集所有报告,识别共同主题,创建统一建议后再修改目标
Anti-Pattern 4: Running Without Budget Awareness
反模式4:无预算意识地运行
What it looks like: Launching 10 agents on a 15,000-word document without estimating cost
Why wrong: Could consume 80,000+ tokens. Session may exhaust budget mid-execution, leaving work incomplete.
Do instead: Estimate cost in Phase 1. Source words x 10 agents x ~3 tokens/word = rough estimate. Warn if over 50,000.
表现:未估算成本就对15000字文档启动10个Agent
问题:可能消耗80000+Token,会话可能因预算耗尽而中途终止,导致工作未完成
正确做法:在阶段1估算成本,源文件字数 × 10个Agent × ~3Token/字 = 粗略估算,超过50000则发出警告
References
参考
This skill uses these shared patterns:
- Anti-Rationalization - Prevents shortcut rationalizations
- Verification Checklist - Pre-completion checks
- Pipeline Architecture - Phase-gated pipeline design
- Gate Enforcement - Phase transition rules
本Skill使用以下共享模式:
- Anti-Rationalization - 防止捷径式合理化
- Verification Checklist - 完成前检查
- Pipeline Architecture - 阶段门控的流水线设计
- Gate Enforcement - 阶段转换规则
Domain-Specific Anti-Rationalization
领域特定的反合理化
| Rationalization | Why It's Wrong | Required Action |
|---|---|---|
| "Source is simple, 10 perspectives overkill" | Simple source = use inline analysis instead | Check material depth in Phase 1, downgrade if thin |
| "3 perspectives returned, close enough" | 3 is minimum for synthesis, not ideal | Wait for timeout threshold, then proceed with available |
| "I can synthesize as I go" | Per-perspective application misses cross-cutting themes | Complete all collection before ANY synthesis |
| "Existing patterns in target are outdated" | Existing patterns may work; new rules ADD, never replace | Preserve all existing content, add depth only |
| 合理化借口 | 问题所在 | 要求的操作 |
|---|---|---|
| 「源材料简单,10个视角是过度设计」 | 简单材料应使用单线程分析 | 在阶段1检查材料深度,若单薄则降级为单线程分析 |
| 「3个视角返回结果,足够了」 | 3个是整合的最低要求,而非理想状态 | 等待超时阈值后,使用已有结果继续 |
| 「我可以边收集边整合」 | 逐视角应用会错过跨领域主题 | 收集完所有结果后再开始整合 |
| 「目标中的现有模式已过时」 | 现有模式可能仍有效,新规则仅用于补充,绝不替换 | 保留所有现有内容,仅添加深度 |
Reference Files
参考文件
- : All 10 perspective templates, synthesis format, completion report template, and source material guidance
${CLAUDE_SKILL_DIR}/references/perspective-prompts.md
- :包含所有10个视角模板、整合格式、完成报告模板以及源材料指南
${CLAUDE_SKILL_DIR}/references/perspective-prompts.md