Agent skill

Art Of War Agents

by LeoYeAI in LeoYeAI/openclaw-master-skills

Apply Sun Tzu's Art of War to AI agent orchestration. An agent skill from LeoYeAI/openclaw-master-skills.

MITAuto-check passedAgent Workflows

Install Art Of War Agents

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill art-of-war-agents -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills art-of-war-agents --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/art-of-war-agents .claude/skills/art-of-war-agents && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
art-of-war-agents
GitHub stars
2.2k
Token cost
~5k tokens
SKILL.md length
1,762 words
Files
8 (incl. scripts, references)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Apply Sun Tzu's Art of War to AI agent orchestration. An agent skill from LeoYeAI/openclaw-master-skills.

  • Works in 12 steps: 始计篇 (Laying Plans) — Task Assessment → 作战篇 (Waging War) — Cost Awareness → 谋攻篇 (Attack by Stratagem) — Planning… → …
  • : task assessment (五事七计)
  • SKILL.md covers ⚡ Quick Reference Card, 🎯 Core Principles, The Thirteen Chapters → Agent… and Quick Decision Tree, plus 1 more section
  • Runs Python scripts from its folder

What it does

Art Of War Agents is an agent skill from LeoYeAI/openclaw-master-skills. Apply Sun Tzu's Art of War to AI agent orchestration. Use for: task assessment (五事七计), deployment decisions, multi-agent strategy, token cost optimization, risk management, and agent collaboration patterns. Maps all 13 chapters to practical agent workflows with decision trees and troubleshooting.

Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `CARD.md`, `CHANGELOG.md` and `README.md`).

It sits in Agent Workflows, covering Multi-agent orchestration and LLM cost and token optimization. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • : task assessment (五事七计)
  • Deployment decisions
  • Multi-agent strategy
  • Token cost optimization

Example prompts

  • “/art-of-war-agents”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. 始计篇 (Laying Plans) — Task Assessment
  2. 作战篇 (Waging War) — Cost Awareness
  3. 谋攻篇 (Attack by Stratagem) — Planning Over Force
  4. 军形篇 (Tactical Dispositions) — Defense First
  5. 兵势篇 (Energy/Impetus) — Momentum & Combination
  6. 虚实篇 (Weak Points & Strong) — Strategic Targeting
  7. 军争篇 (Maneuvering) — Indirect Approaches
  8. 九变篇 (Nine Variations) — Adaptability
  9. 行军篇 (Marching) — Environmental Awareness
  10. 地形篇 (Terrain) — Task Classification
  11. 九地篇 (Nine Grounds) — Commitment Levels
  12. 火攻篇 (Fire Attack) — Tool Usage

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Art Of War Agents loads about 5k tokens when it runs, and up to ~8k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 1,762 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,762 words, ~4,965 tokens.

Download SKILL.mdSave it as .claude/skills/art-of-war-agents/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
art-of-war-agents
description
Apply Sun Tzu's Art of War to AI agent orchestration. Use for: task assessment (五事七计), deployment decisions, multi-agent strategy, token cost optimization, risk management, and agent collaboration patterns. Maps all 13 chapters to practical agent workflows with decision trees and troubleshooting.

📜 Art of War Agents

兵者,国之大事,死生之地,存亡之道,不可不察也

"War is vital — the province of life or death. It must be thoroughly studied."

Sun Tzu's 13 chapters → AI agent orchestration patterns.

This skill provides a complete framework for strategic agent deployment: when to use agents, which ones, how to organize them, and how to avoid wasting tokens on unwinnable battles.


⚡ Quick Reference Card

┌─────────────────────────────────────────────────────────────┐
│           ART OF WAR AGENT DECISION FLOW                    │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  1. 始计 → 值得做吗?(五事七计评估)                          │
│     ├─ Score ≥70% → 部署                                    │
│     ├─ Score 50-70% → 先补弱点                              │
│     └─ Score <50% → 不做,重新规划                          │
│                                                             │
│  2. 谋攻 → 能不做吗?(上兵伐谋)                             │
│     ├─ 可自动化/消除 → 不做                                 │
│     ├─ 单 agent 可解 → 单 agent                               │
│     └─ 必须多 agent → 继续                                  │
│                                                             │
│  3. 作战 → 预算多少?(速战速决)                             │
│     ├─ <10k tokens → 单 agent,快速迭代                       │
│     └─ >10k tokens → 多 agent,设检查点                      │
│                                                             │
│  4. 军形 → 有风险吗?(先胜后战)                             │
│     ├─ 高风险 → 防御优先,多重验证                          │
│     └─ 低风险 → 速度优先                                    │
│                                                             │
│  5. 部署 → 监控信号 (行军篇)                                │
│     ├─ 重复提问 → 补充上下文                                │
│     ├─ 输出变长但质量降 → 强制简洁                          │
│     ├─ 过度自信 → 要求来源                                  │
│     └─ 循环论证 → 介入重定向                                │
│                                                             │
│  6. 验证 → 三反原则 (用间篇)                                │
│     └─ 至少 3 个独立来源交叉验证                              │
│                                                             │
└─────────────────────────────────────────────────────────────┘

🎯 Core Principles

知彼知己 (Know Enemy, Know Yourself)
  • 知彼: Understand the task deeply — requirements, constraints, success criteria
  • 知己: Know your agents' capabilities, limitations, and costs
  • 百战不殆: With both, every agent deployment is calculated, not hopeful
上兵伐谋 (Best Strategy Attacks Plans)
  • Planning > Execution. Invest in task analysis before spawning agents
  • A well-planned single agent beats three confused agents
  • "Win before fighting" — structure the problem so the solution is obvious
奇正相生 (Orthodox + Unorthodox)
  • 正: Standard workflows, proven patterns, reliable agents
  • 奇: Creative approaches, novel combinations, experimental agents
  • Use 正 for reliability, 奇 for breakthrough. Never rely on 奇 alone.
速战速决 (Speed is Essential)
  • Prolonged agent runs waste tokens and drift from intent
  • Set clear termination conditions upfront
  • If an agent isn't making progress in 2-3 iterations, reassess
先胜后战 (Victory Before Battle)
  • Ensure conditions favor success before deploying agents
  • If you can't articulate what "winning" looks like, don't start
  • Retreat is better than wasting resources on unwinnable tasks

The Thirteen Chapters → Agent Patterns

1. 始计篇 (Laying Plans) — Task Assessment

Before deploying any agent, run the Five Constants + Seven Metrics:

五事 (Five Constants):

  1. 道 (Wisdom): Does this task align with overall goals?
  2. 天 (Timing): Is now the right time? Dependencies ready?
  3. 地 (Environment): Do we have the right context/data/tools?
  4. 将 (Capability): Which agent(s) have the right skills?
  5. 法 (Process): What's the workflow? Success criteria?

七计 (Seven Metrics):

  • Which side has better task clarity?
  • Which side has more capable agents?
  • Which side has better context/data?
  • Which side has clearer success criteria?
  • Which side has better tool access?
  • Which side has more disciplined execution?
  • Which side will waste fewer resources?

Decision: If you can't answer these, don't deploy. Plan first.

2. 作战篇 (Waging War) — Cost Awareness

Agent runs cost tokens. Treat them like war costs:

  • 速战速决: Set iteration limits. Force conclusions.
  • 因粮于敌: Use existing outputs/data; don't regenerate what exists
  • 胜久则钝: Long-running agents lose focus and waste tokens
  • 预算先行: Estimate token cost before starting; track as you go

Rule: If a task can be done in 1 agent iteration, don't use 3.

3. 谋攻篇 (Attack by Stratagem) — Planning Over Force

Hierarchy of agent deployment:

  1. 上策: Restructure the problem so it solves itself (no agent needed)
  2. 中策: Single focused agent with clear instructions
  3. 下策: Multiple agents in complex orchestration

Avoid siege warfare: Don't throw more agents at a poorly-defined problem.

全胜思维: The best outcome is winning without fighting — automate or eliminate the task entirely.

4. 军形篇 (Tactical Dispositions) — Defense First

Before offense, ensure defense:

  • What can go wrong? (Hallucination, outdated info, tool failures)
  • What's the rollback plan if the agent makes things worse?
  • What guardrails prevent catastrophic outputs?

先为不可胜: Make yourself undefeatable first — have validation, version control, and undo capability.

以待敌之可胜: Then wait for the opportunity — deploy when conditions are favorable.

5. 兵势篇 (Energy/Impetus) — Momentum & Combination

Create unstoppable momentum:

  • 势 (Shi): Build configurations where each agent output naturally triggers the next
  • 奇正: Combine standard agents (正) with creative approaches (奇)
  • 节 (Timing): Release agents in waves, not all at once

Example pattern: Research agent → Synthesis agent → Critique agent → Final polish Each builds on the previous, creating momentum.

6. 虚实篇 (Weak Points & Strong) — Strategic Targeting

Avoid strength, attack weakness:

  • Identify the critical bottleneck in the task
  • Don't waste agent capacity on parts you can handle yourself
  • Deploy agents where they have comparative advantage

避实击虚: If an agent struggles with nuance, handle the nuance yourself; let it handle the bulk work.

致人而不致于人: Control where the agent focuses; don't let it drift.

7. 军争篇 (Maneuvering) — Indirect Approaches

The longest path may be fastest:

  • Sometimes 2-3 focused agents beat 1 generalist agent
  • Sometimes doing background research first saves 10 iterations later
  • "Appear weak when strong" — let the agent explore naive approaches first, then guide

以迂为直: A seeming detour (extra planning, extra validation) often reaches the goal faster.

8. 九变篇 (Nine Variations) — Adaptability

Agents must adapt to changing conditions:

  • Have contingency plans: If agent A fails, what's plan B?
  • 将在外,君命有所不受: Give agents autonomy within boundaries
  • Recognize when to change strategy mid-execution

Five dangerous agent faults:

  1. Reckless iteration (burns tokens)
  2. Excessive caution (never completes)
  3. Quick temper (argues with user)
  4. Over-optimization (loses the goal)
  5. Blind compliance (no pushback on bad instructions)

Watch for these; intervene early.

9. 行军篇 (Marching) — Environmental Awareness

Read the signals:

  • 相敌 (Observing the enemy): Watch agent outputs for drift, hallucination, confusion
  • 信号 (Signals): Repeated questions = unclear instructions; circular reasoning = stuck
  • 地形 (Terrain): Some tasks are "difficult ground" — high uncertainty, need more oversight

When to intervene:

  • Agent asks the same question twice
  • Output quality degrades over iterations
  • Agent is confident but wrong
10. 地形篇 (Terrain) — Task Classification

Six terrain types → Six task types:

地形任务类型Agent 策略
通形 (Accessible)清晰定义的任务直接部署,标准流程
挂形 (Entangling)容易陷入细节的任务设时间限制,强制输出
支形 (Stalemate)信息不足的任务先收集信息,再决策
隘形 (Narrow)高约束任务精确指令,严格验证
险形 (Dangerous)高风险任务多重验证,保守策略
远形 (Distant)长链条任务分阶段,设检查点
11. 九地篇 (Nine Grounds) — Commitment Levels

Match resource commitment to task importance:

地任务重要性Agent 投入
散地 (Home ground)日常任务轻量 agent,快速迭代
轻地 (Light ground)低价值任务最小可用方案
争地 (Contentious)竞争/时间敏感优先资源,快速部署
交地 (Open ground)多方协作明确接口,文档先行
衢地 (Intersecting)多目标平衡资源,避免偏废
重地 (Serious)高价值任务多 agent 协作,充分验证
圮地 (Difficult)信息不足先探索,后投入
围地 (Desperate)受约束创造性方案,突破限制
死地 (Death)背水一战全力以赴,不留退路

Most tasks are 散地 or 轻地 — don't over-invest.

12. 火攻篇 (Fire Attack) — Tool Usage

Fire = powerful tools (code execution, API calls, external data):

  • 五种火 (Five fires):
    1. 人火 (Human fire): User-provided data/context
    2. 积火 (Accumulated fire): Cached/prepared resources
    3. 辎火 (Supply fire): Tool/API access
    4. 库火 (Arsenal fire): Code execution
    5. 队火 (Unit fire): Multi-agent collaboration

用火的时机 (When to use fire):

  • 行必有备 (Always prepared): Have fallback if tool fails
  • 发火有时 (Right timing): Don't use tools prematurely
  • 发火在早 (Early): Use tools early to validate direction

Warning: Fire can burn you. Validate tool outputs; don't trust blindly.

13. 用间篇 (Spies) — Information Gathering

Five types of intelligence sources:

间Agent 应用
因间 (Local spies)利用现有文档/代码库
内间 (Inside spies)访问内部系统/数据库
反间 (Double agents)交叉验证多个信息源
死间 (Doomed spies)一次性查询(搜索、API)
生间 (Living spies)持续监控(RSS、警报)

关键原则:

  • 三反: Cross-verify with 3+ independent sources
  • 不可偏信: Never trust a single source
  • 先知: Gather intelligence before making decisions

Show full SKILL.md (697 more words)Show less

Quick Decision Tree

Task arrives
    ↓
Can I do this myself in <5 min?
    ├─ Yes → Do it (don't deploy agent)
    └─ No → Continue
    ↓
Is the task clearly defined?
    ├─ No → Plan first (始计篇)
    └─ Yes → Continue
    ↓
What's the token budget?
    ├─ Low (<10k) → Single focused agent (谋攻篇)
    └─ High → Multi-agent OK (兵势篇)
    ↓
What's the risk if wrong?
    ├─ High → Defense first (军形篇), multiple validation
    └─ Low → Speed优先 (作战篇)
    ↓
Deploy with clear success criteria
    ↓
Monitor for drift (行军篇)
    ↓
Validate output (用间篇 - cross-check)

📋 Usage Examples

Example 1: Task Assessment (始计篇)

User: "Should I use an agent for competitor research?"

Agent applies 五事七计:

五事评估得分
道 (Wisdom)Aligns with product launch goals✓ 1
天 (Timing)Launch in 3 weeks, timing good✓ 1
地 (Environment)Need market data, competitor info✓ 1
将 (Capability)Research + synthesis agents available✓ 1
法 (Process)Clear workflow: research→synthesize→present✓ 1

七计 Score: 6/7 (task clarity could be better)

Overall: 85% → DEPLOY

Recommendation: Start with existing reports (因间) before new searches (死间). Budget: ~5k tokens.


Example 2: Agent Stuck (行军篇)

User: "The agent keeps asking me the same questions over and over"

Diagnosis: 挂形 (entangling ground) — agent is stuck in a loop

Signal Reading:

  • ⚠️ Repeated questions = unclear instructions OR missing context
  • ⚠️ Likely on iteration 4+ = 胜久则钝 (prolonged = degraded)

Action:

"Stopping current run. Here's what was missing:
[provide missing context]

New instructions:
1. Use the context above
2. Complete in max 2 iterations
3. If blocked, make reasonable assumptions and note them
4. Output format: [specify]

Proceed."

Example 3: Multi-Agent Orchestration (兵势篇)

User: "I need a complete market analysis report"

Apply 兵势篇 — Create Momentum:

┌─────────────────────────────────────────────────────────┐
│  势 (SHI) — MOMENTUM CONFIGURATION                      │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  Research Agent     →  Synthesis Agent                  │
│  (collect raw data)    (find patterns, insights)        │
│         ↓                       ↓                       │
│  Critique Agent     →  Writing Agent                    │
│  (find gaps, flaws)    (draft report)                   │
│         ↓                       ↓                       │
│  Validation Agent   →  FINAL OUTPUT                     │
│  (cross-verify facts)                                   │
│                                                         │
│  Each output → next input. No parallel chaos.          │
└─────────────────────────────────────────────────────────┘

奇正 Combination:

  • 正: Research → Synthesis → Write (standard flow)
  • 奇: Critique agent challenges assumptions mid-flow

Example 4: Risk Management (军形篇)

User: "I want an agent to refactor our production codebase"

Apply 军形篇 — Defense First:

先为不可胜 (Make Undefeatable):

  • [✓] Git branch created, current state saved
  • [✓] Automated tests in place
  • [✓] Rollback procedure documented
  • [✓] Changes will be reviewed before merge
  • [✓] Agent cannot push directly

以待敌之可胜 (Wait for Opportunity):

  • Start with non-critical module first
  • Validate approach before scaling
  • Set strict boundaries: "No changes to X, Y, Z"

Deployment:

"Refactor the authentication module ONLY.

Constraints:
- No changes to public API signatures
- All existing tests must pass
- Max 3 iterations, then we review
- Document all changes

If uncertain about anything, ask before changing."

Example 5: Tool Usage (火攻篇)

User: "Should I let the agent execute code to analyze this dataset?"

Apply 火攻篇 — Five Fires Analysis:

Fire TypeAvailable?Use?
人火 (User data)✓ Dataset providedUse first
积火 (Cached)✓ Previous analysis existsReuse
辎火 (API/Search)✓ Has accessUse if needed
库火 (Code exec)✓ Sandbox availableUse with caution
队火 (Multi-agent)✓ AvailableNot needed yet

Decision: Start with 人火 + 积火 (existing data). Only use 库火 (code execution) if initial analysis shows it's necessary.

Guardrails:

  • Code runs in sandbox
  • No network access
  • Output reviewed before trusting
  • Rollback: re-run original analysis if needed

Example 6: Before/After Comparison

❌ Before (No Art of War):

User: "Research our competitors and write a report"
Agent: *starts searching immediately*
       *finds 50 sources*
       *writes 20 page report*
       *user: "this isn't what I needed"*
       *starts over*
Token waste: ~50k
Result: Wrong direction, redo needed

✅ After (Art of War Applied):

User: "Research our competitors and write a report"

Agent [始计篇]: "Before I start:
1. Which competitors? (top 3? all in category?)
2. What aspects? (pricing? features? marketing?)
3. Who's the audience? (exec team? product team?)
4. What format? (slides? doc? spreadsheet?)
5. Deadline? (hours? days?)"

User: "Top 3 competitors, features + pricing, 
       for product team, spreadsheet, tomorrow"

Agent [谋攻篇]: "Single agent can handle this. 
               Budget: ~8k tokens.
               Will use existing data first (因间), 
               then search for gaps (死间).
               Checkpoint after data collection.
               Proceed?"

User: "Proceed"

Agent [兵势篇]: *Research → Synthesis → Format*
               *Checkpoint: "Found data for 2/3 competitors, 
                            X missing. Use estimates?"*
User: "Use estimates, note as assumptions"

Agent [用间篇]: *Cross-verified pricing with 2 sources*
               *Delivered: 8k tokens, correct direction*

Token waste: ~8k (84% savings)
Result: Right direction, one iteration

User: "I need to research competitors for our new product launch"

Apply 始计篇:

  • 道: Aligns with business goals ✓
  • 天: Launch is in 3 weeks, timing is good ✓
  • 地: Need market data, competitor websites, reviews ✓
  • 将: Research agent + synthesis agent ✓
  • 法: Research → Synthesize → Present findings ✓

Decision: Deploy, but start with 因间 (existing reports) before 死间 (new searches).

Example 2: Agent is stuck in loops

User: "The agent keeps asking me the same questions"

Apply 行军篇: This is a signal (信号). The agent is on 挂形 (entangling ground).

Action:

  1. Intervene early
  2. Provide missing context
  3. Set iteration limit: "Answer in 2 iterations max"
  4. If still stuck, switch strategy (九变篇)
Example 3: Multi-agent orchestration

User: "I need to build a complete market analysis report"

Apply 兵势篇 (create momentum):

  1. Research agent (收集情报)
  2. Analysis agent (分析数据)
  3. Critique agent (找出漏洞)
  4. Writing agent (生成报告)
  5. Validation agent (交叉验证)

Each output feeds the next, creating 势 (momentum).


🐛 Troubleshooting Common Agent Problems

ProblemSun Tzu DiagnosisSolution
Agent stuck in loops行军篇 — 挂形 (entangling ground)Intervene early, provide missing context, set iteration limit
Output quality degrades作战篇 — 胜久则钝 (prolonged = dull)Force conclusion, start fresh with clearer instructions
Hallucinations用间篇 — 信息不足Require sources, cross-verify with 3+ independent sources
Agent argues with user九变篇 — 忿速 (quick temper fault)Calmly restate goal, remind "complete > perfect"
Never completes task九变篇 — 必生 (cowardly fault)Set deadline, force output, accept "good enough"
Wastes tokens on details虚实篇 — 未避实击虚Redirect: you handle nuance, agent handles bulk
Multi-agent chaos兵势篇 — 无势 (no momentum)Restructure: each agent output → next agent input
Wrong direction始计篇 — 未先知 (didn't know first)Stop, reassess with 五事七计,then restart

📊 Visual Decision Tree

mermaid
flowchart TD
    A[Task Arrives] --> B{Can I do this<br/>in <5 min?}
    B -->|Yes| C[Do It Yourself]
    B -->|No| D{Task Clearly<br/>Defined?}
    D -->|No| E[始计篇: Plan First]
    D -->|Yes| F{Token Budget?}
    F -->|Low <10k| G[谋攻篇: Single Agent]
    F -->|High| H[兵势篇: Multi-Agent]
    G --> I{Risk if Wrong?}
    H --> I
    I -->|High| J[军形篇: Defense + Validation]
    I -->|Low| K[作战篇: Speed Priority]
    J --> L[Deploy with Success Criteria]
    K --> L
    L --> M{Monitor:行军篇}
    M -->|Drift Detected| N[Intervene/Redirect]
    M -->|On Track| O[Complete]
    N --> M
    O --> P[用间篇: Cross-Verify]
    P --> Q[Done]

🎓 Remember

知彼知己,百战不殆

"Know the enemy and know yourself; in a hundred battles you will never be defeated."

知彼 = Understand the task deeply 知己 = Know your agents' capabilities and limits

Agent deployment is war by other means. Treat it seriously. Plan thoroughly. Execute decisively. Review honestly.


📚 Files

FilePurpose
SKILL.mdThis file — core principles and quick reference
references/thirteen-chapters.mdDeep dive: each chapter's full agent mapping
scripts/assess-task.pyInteractive 五事七计 assessment tool

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in skills/art-of-war-agents of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • CARD.md
  • CHANGELOG.md
  • README.md
  • _meta.json
  • references/thirteen-chapters.md
  • scripts/assess-task.py
  • scripts/quick-decision.py

Open the folder on GitHubat commit e5199b5

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Orca CLIstablyai/orca89k2 repos~593Automated safety check: PassMIT
Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Art Of War Agents

What does Art Of War Agents do?

Apply Sun Tzu's Art of War to AI agent orchestration. An agent skill from LeoYeAI/openclaw-master-skills. Art Of War Agents is an agent skill from LeoYeAI/openclaw-master-skills. Apply Sun Tzu's Art of War to AI agent orchestration.

When should I use Art Of War Agents?

Art Of War Agents fits situations like: : task assessment (五事七计); deployment decisions; multi-agent strategy; token cost optimization.

How do I install Art Of War Agents in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill art-of-war-agents -a claude-code`. Or copy the skill folder (skills/art-of-war-agents in LeoYeAI/openclaw-master-skills) into .claude/skills/art-of-war-agents in your project. Claude Code loads it when a task matches its description.

How do I install Art Of War Agents in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill art-of-war-agents -a codex`. Or copy the skill folder (skills/art-of-war-agents in LeoYeAI/openclaw-master-skills) into .agents/skills/art-of-war-agents in your project. Codex loads it when a task matches its description.

Can I use Art Of War Agents in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill art-of-war-agents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/art-of-war-agents, .gemini/skills/art-of-war-agents, .github/skills/art-of-war-agents and .opencode/skills/art-of-war-agents in your project.

What does Art Of War Agents need to run?

Going by SKILL.md and its folder, Art Of War Agents needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Art Of War Agents access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Art Of War Agents safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Art Of War Agents use?

Art Of War Agents is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Art Of War Agents use?

About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3k tokens, read only when the agent opens those files.

What are the alternatives to Art Of War Agents?

Skills that share tags, products or a category with Art Of War Agents: Fable Foreman (olsenbrands/fable-foreman, 143 stars), Long Horizon Prompting (guanyang/open-agent-hub, 977 stars), Orca CLI (stablyai/orca, 89k stars) and Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Art Of War Agents?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.