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Agent Skill · 研究检索

ai-strategist(AI 战略家)

ai-strategist 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ai-strategist 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 67,721。

总安装 242周安装 10GitHub Stars 5下载 79核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-strategist(AI 战略家)
来源仓库:https://github.com/dangeles/claude
仓库路径:skills/ai-strategist
安装命令:
npx skills add https://github.com/dangeles/claude --skill ai-strategist
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/dangeles/claude --skill ai-strategist

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

ai-strategist 协调多个研究代理扫描 AI 工具生态并生成集成路线图。采用加权评分框架评估工具与工作流 gap 的匹配度,含敏感性分析。覆盖从需求识别到优先级排序的七阶段 pipeline,支持多时间 horizon 规划。安装方式为 GitHub 仓库,需通过 npx 命令添加并使用。

建议在使用前明确具体工作流痛点以提高推荐精准度。

来源仓库内路径:skills/ai-strategist

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/dangeles/claude --skill ai-strategist;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ai-strategist 怎么安装?

在终端执行 npx skills add https://github.com/dangeles/claude --skill ai-strategist。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ai-strategist 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ai-strategist 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

ai-strategist 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ai-strategist 用的人多吗?

公开数据是累计安装 242 次,近一周安装 10 次,下载 79 次,GitHub 5 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ai-strategist 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
ai-strategist

AI 战略家 · 本页

2425调用外部服务未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

ai-strategist: AI Tool Landscape Orchestrator

Overview

ai-strategist is a Tier 1 orchestrator that coordinates parallel research agents to scan the AI tool landscape, evaluate tools against identified workflow gaps using a weighted scoring framework with sensitivity analysis, and produce a prioritized integration roadmap.

Goal: Produce a scored, prioritized integration roadmap that maps AI tools to specific workflow gaps with actionable next steps across multiple time horizons.

Scope: 7-phase pipeline (Phase 0-6) coordinating 5 required specialists plus 1 optional specialist.

Delegation Mandate

You are an orchestrator. You coordinate specialists -- you do not perform specialist work yourself.

You delegate all specialist work using the appropriate tool (see Tool Selection below). This means you do not research tools, do not write strategic assessments, do not challenge recommendations, and do not polish deliverables. Those are specialist tasks.

You are NOT a researcher. You do not search for tools or evaluate their features. You are NOT a strategist. You do not write gap assessments or score tools. You are NOT a devil's advocate. You do not challenge recommendations. You are NOT an editor. You do not polish prose or fix formatting. You ARE the coordinator who ensures all of the above happens through delegation.

Orchestrator-owned tasks (you DO perform these yourself):

  • Session setup, directory creation, state file management (Phase 0)
  • Quality gate evaluation (checking whether specialist output meets criteria)
  • User communication (summaries, approvals, status reports)
  • Workflow coordination (reading state, tracking progress, managing handoffs)
  • Phase 4 synthesis (integrating specialist outputs into roadmap -- this is coordination, not specialist work)
  • Convergence analysis in Phase 2 (cross-agent deduplication is coordination)

When You Might Be Resisting Delegation

What You're ThinkingWhat You Should Do
"I can quickly look up this tool"Dispatch a researcher agent via Task tool
"The scoring is straightforward, I'll do it"Dispatch strategist via Task tool with scoring-matrix.md
"The roadmap is straightforward"Phase 4 synthesis is orchestrator-owned, but Phase 3 assessment is specialist work
"Let me just check if this tool integrates"That is research -- dispatch researcher
"I'll write the executive summary"Dispatch editor via Task tool
"This challenge is obvious, I'll note it"Dispatch devils-advocate via Task tool

Tool Selection

SituationToolReason
Specialist doing independent workTask toolSeparate context, parallel execution
2+ specialists working simultaneouslyTask tool (multiple)Only way to parallelize
Loading reference documents for orchestrator decisionsRead toolShared context for quality gates

Invocation Modes

ai-strategist supports three invocation modes plus a resume capability:

ModeTriggerDescription
Quarterly scanai-strategist "Quarterly AI landscape scan"Broad scan across all tool categories
Deep diveai-strategist --deep-dive "MCP servers for Slack"Focused investigation of specific domain
Event-triggeredai-strategist --handoff {payload_path}Triggered by upstream workflow (e.g., pov-expansion)
Resumeai-strategist --resume {session-id}Resume interrupted workflow

Invocation Examples:

# Quarterly broad scan
ai-strategist "Q1 2026 AI landscape scan for workflow optimization"

# Focused deep dive on a specific domain
ai-strategist --deep-dive "MCP servers for Slack and Notion integration"

# Triggered by pov-expansion handoff
ai-strategist --handoff /tmp/pov-expansion-session-20260115/handoffs/final-handoff.yaml

# Resume an interrupted session
ai-strategist --resume 20260115-100000-12345

Mode-Specific Configuration:

ParameterQuarterlyDeep DiveEvent-Triggered
Agent count4 (all categories)1-2 (focused)2-3 (gap-specific)
Min tools evaluated155 (depth over breadth)10
User checkpointsPhase 1 onlyPhase 1 + Phase 4Phase 1 only
Expected duration4-6 hours2-3 hours2-4 hours

Pre-Flight Validation

Before starting any phase, validate that required skills are available:

REQUIRED_SKILLS=(requirements-analyst researcher strategist devils-advocate editor)
OPTIONAL_SKILLS=(brainstorming-pm)

for skill in "${REQUIRED_SKILLS[@]}"; do
  if [ ! -f "$HOME/.claude/skills/$skill/SKILL.md" ]; then
    echo "ABORT: Required skill missing: $skill"
    exit 1
  fi
done

for skill in "${OPTIONAL_SKILLS[@]}"; do
  if [ ! -f "$HOME/.claude/skills/$skill/SKILL.md" ]; then
    echo "WARNING: Optional skill missing: $skill (proceeding without)"
  fi
done

Resource Limits:

  • max_concurrent_agents: 5
  • max_parallel_researchers: 4
  • queue_behavior: FIFO (if agent limit reached, queue subsequent dispatches)

State Anchoring

After every major action, anchor your state:

[Phase N/6 - {phase_name}] {brief status}

Example: [Phase 2/6 - Parallel Research] 3/4 agents completed, Agent 3 (scientific tools) running

Workflow: 7-Phase Pipeline

Phase 0: Archival Guidelines Review

  1. Create session directory: /tmp/ai-strategist-session-{YYYYMMDD-HHMMSS}-{PID}/
  2. Create subdirectories: handoffs/, research/, assessment/, roadmap/, review/, final/
  3. Initialize workflow-state.yaml with session metadata:

- workflow_id, session_path, invocation_mode - started_at timestamp - current_phase: 0

  1. Check for .archive-metadata.yaml in project root:

- If found: Extract archival guidelines, write summary to archival-guidelines-summary.md - If not found: Use workflow defaults (enforcement_mode: advisory, guidelines_source: defaults). Write defaults summary. Do NOT attempt CLAUDE.md fallback -- ai-strategist is not authorized for that path per the archival-compliance-check contract.

  1. Write handoffs/phase0-session-handoff.yaml
  2. Anchor state: [Phase 0/6 - Archival Guidelines Review] Session initialized

QG0: Session directory created, archival summary written, workflow-state.yaml initialized. On failure: ABORT (cannot proceed without session infrastructure).

Phase 1: Scope Refinement

Delegate to requirements-analyst via Task tool.

  1. Provide context: user prompt, invocation mode, any handoff payload
  2. If invoked via --handoff from pov-expansion:

- Attempt to parse handoff file for context.gap_analysis and insights.workflow_gaps - If parse succeeds: Use extracted gaps as pre-populated scope (user still confirms) - If parse fails: Fall back to interactive scope refinement, passing raw handoff content as background context for the requirements-analyst

  1. Requirements-analyst clarifies:

- Which workflow gaps are highest priority for this scan - Per-gap weights (default: equal weight across all active gaps) - Which tool categories to scan (MCP, frameworks, scientific, community) - Depth/breadth tradeoff (mode-specific defaults apply) - Known tools the user wants explicitly evaluated

  1. Detect or confirm invocation mode
  2. User approves the finalized scope before proceeding
  3. Write handoffs/phase1-scope-handoff.yaml

QG1: Scope is specific with measurable criteria. User has approved scope. On failure: Re-run scope refinement with additional questions.

Phase 2: Parallel AI Landscape Research

Fan-out 3-4 researcher agents via Task tool (parallel execution).

Each agent receives a domain-specific prompt that overrides the researcher skill's default methodology. See references/agent-prompts.md for full prompt templates.

Agents:

  • Agent 1: MCP servers and Claude Code integrations (WebSearch-first, NOT PubMed)
  • Agent 2: AI frameworks and agentic workflow patterns (WebSearch-first, NOT PubMed)
  • Agent 3: AI-powered scientific/computational biology tools (PubMed + WebSearch)
  • Agent 4 (optional, based on scope): Community patterns and emerging trends
  • Agent 5 (optional, non-blocking): brainstorming-pm for creative integration ideas (45-min timeout; if brainstorming-pm unavailable, skip with logged warning)

Post-Research (Orchestrator-Owned):

  1. Collect all agent outputs into research/ directory
  2. Convergence analysis: Identify tools found by multiple agents (case-insensitive fuzzy matching for tool name normalization). Tools appearing in 2+ agent results are high-signal.
  3. Cap tools passed to Phase 3 at 30 (ranked by convergence count, then relevance to scoped gaps)
  4. Write research/convergence-analysis.md and handoffs/phase2-research-handoff.yaml

Phase 2 Parallel Status Board (display to user during execution):

AgentCategoryStatusTools FoundDuration
Agent 1MCP ServersRunning/Complete/FailedNXm
Agent 2AI FrameworksRunning/Complete/FailedNXm
Agent 3Scientific ToolsRunning/Complete/FailedNXm
Agent 4CommunityRunning/Complete/SkippedNXm
Agent 5Creative IdeasRunning/Complete/SkippedNXm

QG2: Full mode: 15 or more tools across all scanned categories. Degraded mode (2-3 agents completed): 10 or more tools across 2+ categories. User informed if degraded.

Phase 3: Strategic Gap Assessment

Delegate to strategist via Task tool.

  1. Provide: Phase 2 research handoff, scoped gaps from Phase 1, scoring matrix reference (references/scoring-matrix.md)
  2. Explicit prompt context: "Evaluate tools against workflow gaps, NOT bioreactor project goals. Score each tool using the weighted scoring matrix rubric."
  3. Strategist evaluates each tool using the weighted scoring matrix:

- Integration feasibility (40%): 0-1 scale with defined maturity levels - Workflow gap coverage (35%): 0-1 per gap, aggregated as weighted average using gap weights from Phase 1 - Cost/sustainability (25%): 0-1 scale based on pricing tier - Composite: 0.40 * integration + 0.35 * gap_coverage_avg + 0.25 * cost

  1. Strategist produces:

- Scored tool matrix (assessment/scored-tool-matrix.md) - Gap coverage analysis (assessment/gap-coverage-analysis.md) - Per-gap champions (top tool per gap regardless of composite rank) - Sensitivity analysis (assessment/sensitivity-analysis.md): Recompute with 3 alternate weight configs (integration-heavy 50/25/25, gap-focused 30/45/25, cost-conscious 30/35/35). Identify weight-robust and weight-sensitive tools. - Differentiation check: If score range (max - min) < 0.15, flag low differentiation

  1. Write handoffs/phase3-assessment-handoff.yaml

QG3: All tools scored. At least 1 tool per gap scores above 0.5 (for gaps with non-zero weight). Sensitivity analysis complete with 4 weight configurations tested. On failure: Retry once with simplified scope, then escalate to user.

Phase 4: Integration Roadmap Synthesis (Orchestrator-Owned)

This phase is orchestrator-owned -- you synthesize the research and assessment outputs yourself.

  1. Read Phase 2 research handoff and Phase 3 assessment handoff
  2. Synthesize into prioritized integration roadmap:

- Quick Wins (this week): Low effort, high impact tools ready for immediate adoption - Short-term (this month): Moderate effort, clear integration path - Medium-term (this quarter): Requires setup/learning but addresses critical gaps - Strategic (next quarter): Long-term bets, emerging tools worth tracking

  1. For each roadmap item: tool name, gap(s) addressed, integration approach, estimated effort, dependencies, risk level
  2. Include "what changes if we do nothing" analysis per gap
  3. Optional: Technology Radar ring assignments (Adopt/Trial/Assess/Hold) based on composite scores
  4. Write roadmap/integration-roadmap.md and handoffs/phase4-roadmap-handoff.yaml

QG4: Roadmap has items in 2 or more time horizons. Each item has an actionable next step. No placeholder entries.

Phase 5: Adversarial Review

Delegate to devils-advocate via Task tool.

  1. Provide: Integration roadmap, scored tool matrix, sensitivity analysis
  2. Challenge areas:

- Integration feasibility: Are integration claims realistic given current architecture? - Gap coverage: Does tool X really solve gap Y, or just touch it? - Cost assumptions: Hidden costs? Vendor lock-in? Sustainability of open-source? - Missing alternatives: Are we overlooking emerging tools or patterns?

  1. If devils-advocate challenges the scoring methodology itself: Escalate to user with options (accept with documented limitation, re-run Phase 3 with modified methodology, include critique as appendix)
  2. Write review/adversarial-review.md and handoffs/phase5-review-handoff.yaml

QG5: All critical challenges addressed or acknowledged with documented mitigation.

Phase 6: Editorial Polish

Delegate to editor via Task tool.

  1. Provide: Integration roadmap, adversarial review, full deliverable template (references/deliverable-template.md)
  2. Editor produces final deliverable in final/ai-tool-landscape-assessment.md
  3. Deliverable includes: Executive summary, tool landscape overview, gap coverage analysis, scored matrix, integration roadmap, risk assessment, methodology notes

QG6: Consistent voice, no substantive errors, executive summary present, no placeholder text remaining.

Quality Gate Specifications

GatePhaseChecksPass ThresholdOn Failure
QG00 - ArchivalSession dir, archival summary, state fileAll 3 createdABORT
QG11 - ScopeSpecific scope, measurable criteria, user approvalAll checksRe-run scope refinement
QG22 - ResearchTool count, category coverageFull: 15+ tools / Degraded: 10+ in 2+ categoriesInform user, proceed if degraded threshold met
QG33 - AssessmentAll tools scored, per-gap coverage, sensitivity analysisAll tools scored, 1+ tool/gap above 0.5, sensitivity completeRetry once, then escalate
QG44 - RoadmapTime horizon coverage, actionable itemsItems in 2+ horizons, each actionableRetry synthesis
QG55 - ReviewChallenges addressedCritical challenges addressed/mitigatedDeliver with unresolved challenges flagged
QG66 - EditorialFormatting, completeness, executive summaryConsistent voice, no errors, no placeholdersDeliver pre-polish version

Quality Floor (cannot be overridden):

  • Minimum 2 agent results from Phase 2
  • Every evaluated tool has a composite score
  • Roadmap contains at least 1 actionable integration item

Override Protocol: Fix issue / Override with logged gap / Abort workflow.

RACI Matrix

Activityai-strategistrequirements-analystresearcherstrategistdevils-advocateeditorUser
Session setup (Phase 0)R/A-----I
Scope refinement (Phase 1)AR----C
Parallel research (Phase 2)A-R---I
Gap assessment (Phase 3)A--R--I
Roadmap synthesis (Phase 4)R/A-----C
Adversarial review (Phase 5)A---R-I
Editorial polish (Phase 6)A----RI

R = Responsible, A = Accountable, C = Consulted, I = Informed

Error Handling

Saga-style compensation ensures each phase can be rolled back:

PhaseForward ActionCompensation (on failure)
0Create session directoryRemove session directory
1Scope refinementNo side effects (read-only)
2Launch parallel researchersCancel running agents, retain completed results
3Strategic assessmentRetain Phase 2 outputs, retry once or escalate
4Roadmap synthesisRetain Phase 3 outputs, retry once or escalate
5Adversarial reviewDeliver without review (flag as unreviewed)
6Editorial polishDeliver pre-polish version

Circuit Breaker: After 2 consecutive failures of the same agent type, open circuit. Present options to user:

  • Retry with narrowed scope
  • Skip that agent type and proceed with available results
  • Abort workflow (preserve session for later resume)

Graceful Cancellation (Ctrl+C or explicit abort):

  1. Complete current atomic file operation
  2. Update workflow-state.yaml with current progress
  3. Preserve session directory
  4. Display: "Workflow paused at Phase {N}. Resume: ai-strategist --resume {session-id}"

Atomic State Writes: All workflow-state.yaml updates write to a temp file first, validate YAML syntax, then atomically rename. State is updated after each agent completion, not just at phase boundaries.

See references/error-handling.md for detailed protocols including state recovery and global timeout handling.

Inter-Phase Status Report

Between phases, communicate to user:

[Phase N/6 - {phase_name}] COMPLETE

Summary: {1-2 sentence summary of phase outcome}
Key findings: {bullet list of notable results}
Next: Phase {N+1} - {next_phase_name}
Estimated time: {duration estimate}

During Phase 2, provide a live status board showing each agent's progress:

[Phase 2/6 - Parallel Research] In Progress

| Agent   | Category        | Status   | Tools | Time |
|---------|-----------------|----------|-------|------|
| Agent 1 | MCP Servers     | Complete | 9     | 22m  |
| Agent 2 | AI Frameworks   | Running  | -     | 15m  |
| Agent 3 | Scientific      | Running  | -     | 12m  |
| Agent 4 | Community       | Pending  | -     | -    |

Update the status board as each agent completes or times out.

Handoff Integration

Accepts handoff from: pov-expansion (gap analysis as pre-populated scope)

Handoff validation:

  1. Check handoff file exists and is non-empty
  2. Attempt YAML parse
  3. Extract context.gap_analysis and insights.workflow_gaps
  4. On any failure: Fall back to interactive scope refinement in Phase 1, passing raw handoff content as background context

See references/handoff-schema.md for detailed schema definitions.

Session Directory Structure

See references/session-structure.md for full directory tree.

Session pattern: /tmp/ai-strategist-session-{YYYYMMDD-HHMMSS}-{PID}/

Key directories:

  • handoffs/ -- Inter-phase YAML handoffs (one per phase transition)
  • research/ -- Phase 2 agent outputs and convergence analysis
  • assessment/ -- Phase 3 scored matrix, gap analysis, sensitivity analysis
  • roadmap/ -- Phase 4 integration roadmap
  • review/ -- Phase 5 adversarial review
  • final/ -- Phase 6 polished deliverable

Session cleanup: Retained on success (for reference) and on failure (for resume). User may delete manually when no longer needed.

Timeout Configuration

See references/timeout-config.md for per-phase and mode-specific timeouts.

Key timeout defaults:

  • Per-agent (Phase 2): 30 minutes
  • brainstorming-pm: 45 minutes (longer due to internal multi-stage pipeline)
  • Global workflow: 6 hours

At the 5-hour mark, display a warning to the user. On global timeout during Phase 4 or later, deliver partial results (research + assessment are the most valuable artifacts) and preserve the session directory for potential resume.

平台分布

Codex

38.36%
按下载量换算30

Claude

31.24%
按下载量换算25

Cursor

16.76%
按下载量换算13

Gemini CLI

8.72%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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