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smart-routing

Intelligent request routing for /toh command. Analyzes user intent, assesses confidence, surveys the runtime (2-step, per orchestration-protocol), and routes to the appropriate agent(s). Memory-first approach ensures context awareness. Triggers: /toh command, natural language req

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Smart Routing Skill

Intelligent routing engine for the /toh smart command. Routes any natural language request to the right agent(s).

🧠 Routing Pipeline

┌─────────────────────────────────────────────────────────────────┐
│                    USER REQUEST                                 │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  STEP 0: MEMORY CHECK (ALWAYS FIRST!)                          │
│  ├── Read .toh/memory/active.md                                │
│  ├── Read .toh/memory/summary.md                               │
│  ├── Read .toh/memory/decisions.md                             │
│  └── Build context understanding                               │
│                                                                 │
│  STEP 1: INTENT CLASSIFICATION                                 │
│  ├── Pattern matching (keywords, phrases)                      │
│  ├── Context inference (from memory)                           │
│  └── Scope detection (simple/complex)                          │
│                                                                 │
│  STEP 2: CONFIDENCE SCORING                                    │
│  ├── HIGH (80%+) → Direct execution                            │
│  ├── MEDIUM (50-80%) → Plan Agent first                        │
│  └── LOW (<50%) → Ask for clarification                        │
│                                                                 │
│  STEP 3: RUNTIME SURVEY (2-step — orchestration-protocol A)    │
│  ├── Identity: declared by loaded context file +               │
│  │   .toh/capabilities.json                                    │
│  └── Probe: teams env flag + version gates only                │
│                                                                 │
│  STEP 4: AGENT SELECTION & EXECUTION                           │
│  └── Route to appropriate agent(s)                             │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

📊 Intent Classification Matrix

Illustrative heuristics only — native agent-description matching makes the actual call (see /toh); do not compute or display confidence scores.

Primary Patterns → Agent Mapping

Pattern Category	Keywords (EN)	Keywords (TH)	Primary Agent	Confidence
Create UI	create, add, make, build + page/component/UI	สร้าง, เพิ่ม, ทำ + หน้า/component	UI Agent	HIGH
Add Logic	logic, state, function, hook, validation	logic, state, function, เพิ่ม logic	Dev Agent	HIGH
Fix Bug	bug, error, broken, fix, not working	bug, error, พัง, ไม่ทำงาน, แก้	Fix Agent	HIGH
Improve Design	prettier, beautiful, design, polish, style	สวย, design, ปรับ design	Design Agent	HIGH
Testing	test, check, verify	test, ทดสอบ, เช็ค	Test Agent	HIGH
Connect Backend	connect, database, Supabase, API, backend	เชื่อม, database, Supabase	Connect Agent	HIGH
Deploy	deploy, ship, production, publish	deploy, ship, ขึ้น production	Ship Agent	HIGH
LINE Platform	LINE, LIFF, LINE MINI App	LINE, LIFF	LINE Agent	HIGH
Mobile Platform	mobile, iOS, Android, PWA, Capacitor	mobile, มือถือ	Mobile Agent	HIGH
New Project	new project, start, build app, create system	project ใหม่, สร้าง app	Vibe Agent	HIGH
Planning	plan, analyze, PRD, architecture	วางแผน, วิเคราะห์	Plan Agent	HIGH
AI/Prompt	prompt, AI, chatbot, system prompt	prompt, AI, chatbot	Dev Agent + prompt-optimizer	HIGH
Continue	continue, resume, go on	ทำต่อ, ต่อ	Memory → Last Agent	MEDIUM
Complex Request	Multiple features, system, e-commerce, etc.	ระบบ + หลาย features	Plan Agent	MEDIUM
Vague Request	help, fix it, make better (without context)	ช่วยด้วย, แก้ที	Ask Clarification	LOW

🎯 Confidence Scoring Algorithm

Illustrative heuristics only — native agent-description matching makes the actual call (see /toh); do not compute or display confidence scores.

interface ConfidenceFactors {
  keywordMatch: number;      // 0-40 points
  contextClarity: number;    // 0-30 points
  memorySupport: number;     // 0-20 points
  scopeDefinition: number;   // 0-10 points
}

function calculateConfidence(request: string, memory: Memory): number {
  let score = 0;
  
  // Keyword matching (0-40 points)
  // Strong match with primary patterns = 40
  // Partial match = 20
  // No match = 0
  score += keywordMatchScore(request);
  
  // Context clarity (0-30 points)
  // Specific page/component mentioned = 30
  // General area mentioned = 15
  // No specifics = 0
  score += contextClarityScore(request);
  
  // Memory support (0-20 points)
  // Request relates to active task = 20
  // Request relates to project = 10
  // No memory context = 0
  score += memorySupportScore(request, memory);
  
  // Scope definition (0-10 points)
  // Single clear task = 10
  // Multiple related tasks = 5
  // Unclear scope = 0
  score += scopeDefinitionScore(request);
  
  return score; // 0-100
}

// Thresholds
const HIGH_CONFIDENCE = 80;    // Execute directly
const MEDIUM_CONFIDENCE = 50;  // Route to Plan Agent
// Below 50 = Ask for clarification

🖥️ Runtime Survey (2-step — never guess the IDE)

Step 1 — Identity (declared)

Your runtime identity is declared by the platform context file that loaded you (CLAUDE.md = Claude Code · .cursor/rules/*.mdc = Cursor · AGENTS.md = Codex or ZCode, whichever the **Runtime:** line inside it names · .agents/rules/toh-framework.md = Antigravity · GEMINI.md = Gemini CLI, legacy). Confirm capabilities from .toh/capabilities.json (written by the installer). No detection heuristics — the identity is stated, not inferred.

Step 2 — Runtime probe (only what install time cannot know)

Probe exactly: the CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS env flag, plus the Claude Code version gates for /goal and workflows. Nothing else.

Execution mode

Choose from the execution ladder in orchestration-protocol (Section B) — the full decision table lives there, once. Summary only:

Claude Code → ladder: teams > subagents > sequential
Cursor (2.4+) → native subagents in .cursor/agents/, one task at a time
Antigravity → file-based subagents via invoke_subagent, one task at a time
Codex / ZCode / Gemini (legacy) → sequential TOH LOOP in-session

🔄 Routing Decision Tree

Request arrives
      │
      ▼
┌─────────────────────────────────────┐
│ 1. Load Memory Context              │
└─────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────┐
│ 2. Is request "continue"/"ทำต่อ"?   │
├── YES → Read memory, resume task   │
└── NO → Continue analysis           │
      │
      ▼
┌─────────────────────────────────────┐
│ 3. Calculate Confidence Score       │
└─────────────────────────────────────┘
      │
      ├── Score >= 80 (HIGH)
      │   └─→ Select agent based on intent
      │       └─→ Execute directly
      │
      ├── Score 50-79 (MEDIUM)
      │   └─→ Route to Plan Agent
      │       └─→ Plan Agent analyzes & routes
      │
      └── Score < 50 (LOW)
          └─→ Ask clarifying question
              └─→ Wait for user response

📋 Clarification Patterns

When to Ask

Situation	Example	Action
No verb/action	"the login"	Ask: "What would you like to do with login?"
No target	"make it work"	Ask: "Which page/component should I fix?"
Multiple interpretations	"improve it"	Ask: "Design, performance, or features?"
Missing context + no memory	"fix it"	Ask: "What's broken? Describe the issue."

When NOT to Ask

Situation	Example	Action
Clear intent	"create login page"	Execute directly
Memory provides context	"continue" + active task exists	Resume from memory
Reasonable default exists	"add a button"	Add to current page context

🎨 Skill Loading by Intent

Detected Intent	Skills to Load
New Project	vibe-orchestrator, design-craft, business-context, engineer-harness
Create UI	ui-first-builder, design-craft, engineer-harness
Add Logic	dev-engineer, error-handling, engineer-harness
Fix Bug	debug-protocol, error-handling, engineer-harness
Connect Backend	backend-engineer, integrations, engineer-harness
Improve Design	design-craft, engineer-harness
AI/Chatbot	prompt-optimizer, dev-engineer, engineer-harness
Testing	test-engineer, error-handling, engineer-harness
Planning	plan-orchestrator, business-context, engineer-harness

Note: engineer-harness skill is ALWAYS loaded for proper output formatting and next-step suggestions.

💾 Memory Integration

Pre-Routing Memory Check

Before routing, ALWAYS:
1. Read .toh/memory/active.md
   - Current task context
   - In-progress work
   - Blockers
   
2. Read .toh/memory/summary.md
   - Project overview
   - Completed features
   - Tech stack used
   
3. Read .toh/memory/decisions.md
   - Past architectural decisions
   - Design choices
   - Naming conventions

Use memory to:
- Boost confidence (if request matches active work)
- Provide context (for ambiguous "it" references)
- Maintain consistency (follow established patterns)

Post-Execution Memory Save

After routing completes, ALWAYS:
1. Update .toh/memory/active.md
   - Mark completed items
   - Update current focus
   - Set next steps
   
2. Add to .toh/memory/decisions.md
   - If new decisions were made
   
3. Update .toh/memory/summary.md
   - If feature was completed

⚠️ NEVER finish without saving memory!

📌 Examples

Example 1: High Confidence → Direct

Request: "/toh สร้างหน้า dashboard"

Analysis:
- Keyword match: "สร้าง" + "หน้า" = Create UI (40 pts)
- Context clarity: "dashboard" = specific page (30 pts)
- Memory: Project has other pages (15 pts)
- Scope: Single page (10 pts)
Total: 95 pts = HIGH

Route: UI Agent (direct)

Example 2: Medium Confidence → Plan First

Request: "/toh build e-commerce"

Analysis:
- Keyword match: "build" = Create (40 pts)
- Context clarity: "e-commerce" = general concept (10 pts)
- Memory: New project (0 pts)
- Scope: Multiple features (0 pts)
Total: 50 pts = MEDIUM

Route: Plan Agent first → then execute plan

Example 3: Low Confidence → Ask

Request: "/toh fix it"

Analysis:
- Keyword match: "fix" (20 pts)
- Context clarity: "it" = unclear (0 pts)
- Memory: No recent bugs (0 pts)
- Scope: Unknown (0 pts)
Total: 20 pts = LOW

Action: Ask "What would you like me to fix? Please describe the issue."

⚠️ Critical Rules

Memory ALWAYS first - Never route without checking context
Confidence drives action - Trust the scoring system
Plan Agent is your friend - When in doubt, route to Plan
Survey, don't guess - Identity is declared; execution mode comes from orchestration-protocol's ladder
engineer-harness always loaded - Every response needs 3 sections + next steps

Smart Routing Skill v1.0.0 - Intelligent Request Routing Engine

Voir la source ↗

Métadonnées du fichier

name: smart-routing
description: >
  Intelligent request routing for /toh command. Analyzes user intent,
  assesses confidence, surveys the runtime (2-step, per orchestration-protocol),
  and routes to the appropriate agent(s). Memory-first approach ensures
  context awareness. Triggers: /toh command, natural language requests,
  ambiguous inputs.

Voir le texte original

---
name: smart-routing
description: >
  Intelligent request routing for /toh command. Analyzes user intent,
  assesses confidence, surveys the runtime (2-step, per orchestration-protocol),
  and routes to the appropriate agent(s). Memory-first approach ensures
  context awareness. Triggers: /toh command, natural language requests,
  ambiguous inputs.
---

# Smart Routing Skill

Intelligent routing engine for the `/toh` smart command. Routes any natural language request to the right agent(s).

---

## 🧠 Routing Pipeline

```
┌─────────────────────────────────────────────────────────────────┐
│                    USER REQUEST                                 │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  STEP 0: MEMORY CHECK (ALWAYS FIRST!)                          │
│  ├── Read .toh/memory/active.md                                │
│  ├── Read .toh/memory/summary.md                               │
│  ├── Read .toh/memory/decisions.md                             │
│  └── Build context understanding                               │
│                                                                 │
│  STEP 1: INTENT CLASSIFICATION                                 │
│  ├── Pattern matching (keywords, phrases)                      │
│  ├── Context inference (from memory)                           │
│  └── Scope detection (simple/complex)                          │
│                                                                 │
│  STEP 2: CONFIDENCE SCORING                                    │
│  ├── HIGH (80%+) → Direct execution                            │
│  ├── MEDIUM (50-80%) → Plan Agent first                        │
│  └── LOW (<50%) → Ask for clarification                        │
│                                                                 │
│  STEP 3: RUNTIME SURVEY (2-step — orchestration-protocol A)    │
│  ├── Identity: declared by loaded context file +               │
│  │   .toh/capabilities.json                                    │
│  └── Probe: teams env flag + version gates only                │
│                                                                 │
│  STEP 4: AGENT SELECTION & EXECUTION                           │
│  └── Route to appropriate agent(s)                             │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

---

## 📊 Intent Classification Matrix

> Illustrative heuristics only — native agent-description matching makes the actual call (see /toh); do not compute or display confidence scores.

### Primary Patterns → Agent Mapping

| Pattern Category | Keywords (EN) | Keywords (TH) | Primary Agent | Confidence |
|------------------|---------------|---------------|---------------|------------|
| **Create UI** | create, add, make, build + page/component/UI | สร้าง, เพิ่ม, ทำ + หน้า/component | UI Agent | HIGH |
| **Add Logic** | logic, state, function, hook, validation | logic, state, function, เพิ่ม logic | Dev Agent | HIGH |
| **Fix Bug** | bug, error, broken, fix, not working | bug, error, พัง, ไม่ทำงาน, แก้ | Fix Agent | HIGH |
| **Improve Design** | prettier, beautiful, design, polish, style | สวย, design, ปรับ design | Design Agent | HIGH |
| **Testing** | test, check, verify | test, ทดสอบ, เช็ค | Test Agent | HIGH |
| **Connect Backend** | connect, database, Supabase, API, backend | เชื่อม, database, Supabase | Connect Agent | HIGH |
| **Deploy** | deploy, ship, production, publish | deploy, ship, ขึ้น production | Ship Agent | HIGH |
| **LINE Platform** | LINE, LIFF, LINE MINI App | LINE, LIFF | LINE Agent | HIGH |
| **Mobile Platform** | mobile, iOS, Android, PWA, Capacitor | mobile, มือถือ | Mobile Agent | HIGH |
| **New Project** | new project, start, build app, create system | project ใหม่, สร้าง app | Vibe Agent | HIGH |
| **Planning** | plan, analyze, PRD, architecture | วางแผน, วิเคราะห์ | Plan Agent | HIGH |
| **AI/Prompt** | prompt, AI, chatbot, system prompt | prompt, AI, chatbot | Dev Agent + prompt-optimizer | HIGH |
| **Continue** | continue, resume, go on | ทำต่อ, ต่อ | Memory → Last Agent | MEDIUM |
| **Complex Request** | Multiple features, system, e-commerce, etc. | ระบบ + หลาย features | Plan Agent | MEDIUM |
| **Vague Request** | help, fix it, make better (without context) | ช่วยด้วย, แก้ที | Ask Clarification | LOW |

---

## 🎯 Confidence Scoring Algorithm

> Illustrative heuristics only — native agent-description matching makes the actual call (see /toh); do not compute or display confidence scores.

```typescript
interface ConfidenceFactors {
  keywordMatch: number;      // 0-40 points
  contextClarity: number;    // 0-30 points
  memorySupport: number;     // 0-20 points
  scopeDefinition: number;   // 0-10 points
}

function calculateConfidence(request: string, memory: Memory): number {
  let score = 0;
  
  // Keyword matching (0-40 points)
  // Strong match with primary patterns = 40
  // Partial match = 20
  // No match = 0
  score += keywordMatchScore(request);
  
  // Context clarity (0-30 points)
  // Specific page/component mentioned = 30
  // General area mentioned = 15
  // No specifics = 0
  score += contextClarityScore(request);
  
  // Memory support (0-20 points)
  // Request relates to active task = 20
  // Request relates to project = 10
  // No memory context = 0
  score += memorySupportScore(request, memory);
  
  // Scope definition (0-10 points)
  // Single clear task = 10
  // Multiple related tasks = 5
  // Unclear scope = 0
  score += scopeDefinitionScore(request);
  
  return score; // 0-100
}

// Thresholds
const HIGH_CONFIDENCE = 80;    // Execute directly
const MEDIUM_CONFIDENCE = 50;  // Route to Plan Agent
// Below 50 = Ask for clarification
```

---

## 🖥️ Runtime Survey (2-step — never guess the IDE)

### Step 1 — Identity (declared)

Your runtime identity is **declared by the platform context file that loaded you** (`CLAUDE.md` = Claude Code · `.cursor/rules/*.mdc` = Cursor · `AGENTS.md` = Codex **or** ZCode, whichever the `**Runtime:**` line inside it names · `.agents/rules/toh-framework.md` = Antigravity · `GEMINI.md` = Gemini CLI, legacy). Confirm capabilities from `.toh/capabilities.json` (written by the installer). No detection heuristics — the identity is stated, not inferred.

### Step 2 — Runtime probe (only what install time cannot know)

Probe exactly: the `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS` env flag, plus the Claude Code version gates for `/goal` and workflows. Nothing else.

### Execution mode

Choose from the **execution ladder in `orchestration-protocol` (Section B)** — the full decision table lives there, once. Summary only:

- **Claude Code** → ladder: teams > subagents > sequential
- **Cursor (2.4+)** → native subagents in `.cursor/agents/`, one task at a time
- **Antigravity** → file-based subagents via `invoke_subagent`, one task at a time
- **Codex / ZCode / Gemini (legacy)** → sequential TOH LOOP in-session

---

## 🔄 Routing Decision Tree

```
Request arrives
      │
      ▼
┌─────────────────────────────────────┐
│ 1. Load Memory Context              │
└─────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────┐
│ 2. Is request "continue"/"ทำต่อ"?   │
├── YES → Read memory, resume task   │
└── NO → Continue analysis           │
      │
      ▼
┌─────────────────────────────────────┐
│ 3. Calculate Confidence Score       │
└─────────────────────────────────────┘
      │
      ├── Score >= 80 (HIGH)
      │   └─→ Select agent based on intent
      │       └─→ Execute directly
      │
      ├── Score 50-79 (MEDIUM)
      │   └─→ Route to Plan Agent
      │       └─→ Plan Agent analyzes & routes
      │
      └── Score < 50 (LOW)
          └─→ Ask clarifying question
              └─→ Wait for user response
```

---

## 📋 Clarification Patterns

### When to Ask

| Situation | Example | Action |
|-----------|---------|--------|
| No verb/action | "the login" | Ask: "What would you like to do with login?" |
| No target | "make it work" | Ask: "Which page/component should I fix?" |
| Multiple interpretations | "improve it" | Ask: "Design, performance, or features?" |
| Missing context + no memory | "fix it" | Ask: "What's broken? Describe the issue." |

### When NOT to Ask

| Situation | Example | Action |
|-----------|---------|--------|
| Clear intent | "create login page" | Execute directly |
| Memory provides context | "continue" + active task exists | Resume from memory |
| Reasonable default exists | "add a button" | Add to current page context |

---

## 🎨 Skill Loading by Intent

| Detected Intent | Skills to Load |
|-----------------|----------------|
| New Project | vibe-orchestrator, design-craft, business-context, engineer-harness |
| Create UI | ui-first-builder, design-craft, engineer-harness |
| Add Logic | dev-engineer, error-handling, engineer-harness |
| Fix Bug | debug-protocol, error-handling, engineer-harness |
| Connect Backend | backend-engineer, integrations, engineer-harness |
| Improve Design | design-craft, engineer-harness |
| AI/Chatbot | prompt-optimizer, dev-engineer, engineer-harness |
| Testing | test-engineer, error-handling, engineer-harness |
| Planning | plan-orchestrator, business-context, engineer-harness |

**Note:** `engineer-harness` skill is ALWAYS loaded for proper output formatting and next-step suggestions.

---

## 💾 Memory Integration

### Pre-Routing Memory Check

```markdown
Before routing, ALWAYS:
1. Read .toh/memory/active.md
   - Current task context
   - In-progress work
   - Blockers
   
2. Read .toh/memory/summary.md
   - Project overview
   - Completed features
   - Tech stack used
   
3. Read .toh/memory/decisions.md
   - Past architectural decisions
   - Design choices
   - Naming conventions

Use memory to:
- Boost confidence (if request matches active work)
- Provide context (for ambiguous "it" references)
- Maintain consistency (follow established patterns)
```

### Post-Execution Memory Save

```markdown
After routing completes, ALWAYS:
1. Update .toh/memory/active.md
   - Mark completed items
   - Update current focus
   - Set next steps
   
2. Add to .toh/memory/decisions.md
   - If new decisions were made
   
3. Update .toh/memory/summary.md
   - If feature was completed

⚠️ NEVER finish without saving memory!
```

---

## 📌 Examples

### Example 1: High Confidence → Direct

```
Request: "/toh สร้างหน้า dashboard"

Analysis:
- Keyword match: "สร้าง" + "หน้า" = Create UI (40 pts)
- Context clarity: "dashboard" = specific page (30 pts)
- Memory: Project has other pages (15 pts)
- Scope: Single page (10 pts)
Total: 95 pts = HIGH

Route: UI Agent (direct)
```

### Example 2: Medium Confidence → Plan First

```
Request: "/toh build e-commerce"

Analysis:
- Keyword match: "build" = Create (40 pts)
- Context clarity: "e-commerce" = general concept (10 pts)
- Memory: New project (0 pts)
- Scope: Multiple features (0 pts)
Total: 50 pts = MEDIUM

Route: Plan Agent first → then execute plan
```

### Example 3: Low Confidence → Ask

```
Request: "/toh fix it"

Analysis:
- Keyword match: "fix" (20 pts)
- Context clarity: "it" = unclear (0 pts)
- Memory: No recent bugs (0 pts)
- Scope: Unknown (0 pts)
Total: 20 pts = LOW

Action: Ask "What would you like me to fix? Please describe the issue."
```

---

## ⚠️ Critical Rules

1. **Memory ALWAYS first** - Never route without checking context
2. **Confidence drives action** - Trust the scoring system
3. **Plan Agent is your friend** - When in doubt, route to Plan
4. **Survey, don't guess** - Identity is declared; execution mode comes from orchestration-protocol's ladder
5. **engineer-harness always loaded** - Every response needs 3 sections + next steps

---

*Smart Routing Skill v1.0.0 - Intelligent Request Routing Engine*

Examiner la source

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Licence: MIT
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Source du skill enregistrée

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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The SKILL.md contains a contradiction: it describes a detailed confidence scoring algorithm but then states 'do not compute or display confidence scores' and refers to native agent-description matching. This could confuse implementers.
The skill depends on external files (.toh/memory/*.md, .toh/capabilities.json) and an 'orchestration-protocol' that are not fully specified in the SKILL.md, which may hinder portability.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 95 GitHub stars
Stars/forks activity: 95 stars, 19 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
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Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

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Dépôt source: wasintoh/toh-framework
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 2 sept. 2026
Registre mis à jour: 7 sept. 2026
Chemin des instructions: src/skills/smart-routing/SKILL.md @ 07e95d088315

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Qualité

63/100

Prometteur

Confiance

53/100

Do not auto-install

Audit

70/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
The SKILL.md contains a contradiction: it describes a detailed confidence scoring algorithm but then states 'do not compute or display confidence scores' and refers to native agent-description matching. This could confuse implementers.
The skill depends on external files (.toh/memory/*.md, .toh/capabilities.json) and an 'orchestration-protocol' that are not fully specified in the SKILL.md, which may hinder portability.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 95 GitHub stars
Stars/forks activity: 95 stars, 19 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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Plus de détails

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      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
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      "Audit: 70/100 Needs review",
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}

Pour le créateur

Source de la fiche

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: wasintoh
Source: wasintoh/toh-framework
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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