Initial commit

This commit is contained in:
Zhongwei Li
2025-11-30 08:43:48 +08:00
commit cf118c4923
27 changed files with 10878 additions and 0 deletions

View File

@@ -0,0 +1,86 @@
---
name: wolf-principles
description: Wolf's 10 core principles for agent behavior and system design (condensed index)
version: 1.1.0
triggers:
- "wolf principles"
- "core principles"
- "system guidelines"
---
# Wolf Principles Index
**Quick reference for Wolf's 10 core principles.** For detailed implementation guidance, examples, and conflict resolution, use the Skill tool to load `wolf-principles` SKILL.md.
## The 10 Core Principles
1. **Artifact-First Development**
All work produces durable, verifiable artifacts (PRs, ADRs, journals), not ephemeral conversations.
2. **Role Isolation and Separation of Concerns**
Each agent role has clearly defined responsibilities with minimal overlap and strict boundaries.
3. **Research-Before-Code**
All implementation must be preceded by structured research and evidence-based recommendations.
4. **Advisory-First Enforcement**
New policies are tested in advisory mode (shadow) before becoming hard gates.
5. **Evidence-Based Decision Making**
All decisions must be supported by concrete evidence and measurable outcomes.
6. **Self-Improving Systems**
The system continuously learns from operations and evolves based on evidence.
7. **Multi-Provider Resilience**
Operate reliably across multiple AI providers with graceful fallback.
8. **GitHub-Native Integration**
Leverage GitHub primitives (Apps, Actions, Issues) to minimize custom infrastructure.
9. **Incremental Value Delivery**
Work broken into small increments (2-8h) that are independently valuable and deployable.
10. **Transparent Governance**
All decisions, processes, and constraints are openly documented and auditable.
---
## When Principles Conflict
**Priority Order:**
1. Security and Safety (Principles 2, 7)
2. Evidence and Quality (Principles 3, 5, 6)
3. Operational Efficiency (Principles 1, 8, 9)
4. Governance and Compliance (Principles 4, 10)
---
## Quick Application Guide
- **Making decisions?** → Use Principles 3, 5 (Research-First, Evidence-Based)
- **Designing architecture?** → Use Principles 1, 2, 7 (Artifacts, Isolation, Resilience)
- **Implementing features?** → Use Principles 3, 9 (Research-First, Incremental)
- **Enforcing policies?** → Use Principles 4, 10 (Advisory-First, Transparent)
- **Building automation?** → Use Principles 6, 8 (Self-Improving, GitHub-Native)
---
## Next Steps
**REQUIRED**: Load detailed guidance when needed
- Use Skill tool to load `wolf-principles` for:
- Detailed implementation guidance
- Example applications for each principle
- Conflict resolution patterns
- Integration with other Wolf skills
**Sequential Skill Chain:**
1.**Principles** (you are here)
2. → Load `wolf-archetypes` to determine work type
3. → Load `wolf-governance` to understand quality gates
4. → Load `wolf-roles` for role-specific guidance
---
*This is a condensed index (~300 tokens). For full content (~2,700 tokens), load SKILL.md.*

View File

@@ -0,0 +1,341 @@
---
name: wolf-principles
description: Wolf's 10 core principles for agent behavior and system design
version: 1.2.0
triggers:
- "wolf principles"
- "core principles"
- "system guidelines"
- "agent behavior"
- "decision making"
---
# Wolf Principles Skill
This skill provides access to Wolf's 10 core principles that guide the design, implementation, and operation of the Wolf Agents multi-agent system. These principles have been refined over 50+ phases of real-world development.
## When to Use This Skill
- **ALWAYS** before making architectural decisions
- When justifying design choices or trade-offs
- During planning and implementation of new features
- When resolving conflicts between competing priorities
- For onboarding new team members or agents
## The 10 Core Principles
### 1. Artifact-First Development
**Principle**: All work produces durable, verifiable artifacts rather than ephemeral conversations.
**Implementation**:
- Pull Requests (PRs) are the primary unit of work and evidence
- Every change must be committed, reviewed, and merged
- Conversations and decisions are captured in issues, ADRs, and journals
- No work is considered complete without a merged artifact
**Example Application**:
```
Instead of: "I fixed the bug, it works now"
Do this: Create PR with fix, tests, and documentation of root cause
```
### 2. Role Isolation and Separation of Concerns
**Principle**: Each agent role has clearly defined responsibilities with minimal overlap and strict boundaries.
**Implementation**:
- Individual GitHub Apps per role with minimal required permissions
- Agents cannot merge their own implementations
- Clear ownership matrices and authority boundaries
- Role cards define exact scope, non-goals, and collaboration patterns
**Example Application**:
```
PM Agent: Defines requirements and acceptance criteria
Coder Agent: Implements solution meeting criteria
Reviewer Agent: Validates implementation quality
QA Agent: Verifies functionality and tests
```
### 3. Research-Before-Code
**Principle**: All implementation work must be preceded by structured research and evidence-based recommendations. This applies at **TWO levels**:
1. **Level 1 - Architectural Research** (research-agent, 2-8 hours): "Should we use this approach?"
2. **Level 2 - Documentation Lookup** (coder-agent, 2-5 minutes): "How do I use this library's current API?"
**Implementation**:
**Level 1 - Architectural Research:**
- Mandatory Research Agent analysis before any coding begins
- Structured research comments with evidence, findings, and advised solutions
- `research` label as a blocking gate for implementation
- Implementation must align with or justify deviations from research
- Time scale: 2-8 hours for feasibility, approach, and architecture decisions
**Level 2 - Documentation Lookup:**
- Use WebSearch/WebFetch for official API documentation before using libraries
- Verify syntax/patterns against authoritative sources (not model memory)
- Check for breaking changes, new features, and current best practices
- Look up version-specific documentation matching your project
- Time scale: 2-5 minutes per library (prevents "cold start" coding from memory)
**Why Two Levels:**
- **Level 1** addresses *unknown unknowns* (architectural risks, feasibility)
- **Level 2** addresses *known unknowns* (current API syntax, recent changes)
- Both prevent wasted implementation time from outdated assumptions
**Example Application**:
```
Task: Add authentication to API
Level 1 - Architectural Research (research-agent, 4 hours):
- Analyze existing auth patterns, security requirements, compliance needs
- Compare JWT vs OAuth2 vs Passport.js approaches
- Evaluate security implications, scalability, maintenance burden
- Deliver recommendation: "Use Passport.js with JWT strategy"
→ Output: ADR documenting decision and rationale
Level 2 - Documentation Lookup (coder-agent, 3 minutes):
- WebSearch "passport.js jwt strategy official documentation 2025"
- WebFetch https://www.passportjs.org/packages/passport-jwt/
- Verify: Current version is 4.0.1, check for breaking changes from 3.x
- Review: Example code for JWT verification and token extraction
→ Output: Implementation using current, verified API patterns
Result: Implementation informed by both architectural research (Level 1)
and current documentation (Level 2), avoiding both strategic and tactical errors.
```
### 4. Advisory-First Enforcement
**Principle**: New policies and constraints are tested in advisory mode before becoming hard gates.
**Implementation**:
- Shadow-mode validation for new rules and patterns
- Gradual rollout with confidence thresholds
- Evidence collection before enforcement
- Fallback and rollback mechanisms for all gates
**Example Application**:
```
New Rule: All PRs must have 90% test coverage
Phase 1: Report coverage but don't block (2 weeks)
Phase 2: Block if <70% coverage (2 weeks)
Phase 3: Enforce 90% threshold (ongoing)
```
### 5. Evidence-Based Decision Making
**Principle**: All decisions must be supported by concrete evidence and measurable outcomes.
**Implementation**:
- Performance budgets with measurement requirements
- Security scans and validation evidence
- Test coverage and quality metrics
- Documented trade-offs with quantified impacts
**Example Application**:
```
Decision: Choose between REST and GraphQL
Evidence Required:
- Latency benchmarks for typical queries
- Bundle size impact measurements
- Developer productivity metrics
- Maintenance cost analysis
```
### 6. Self-Improving Systems
**Principle**: The system continuously learns from its operations and evolves based on evidence.
**Implementation**:
- Comprehensive journaling of problems, decisions, and learnings
- Regular retrospectives and pattern identification
- Automated metrics collection and analysis
- Feedback loops from operations back to design
**Example Application**:
```
Problem: CI failures increasing
Journal: Document failure patterns
Analysis: Identify common root causes
Improvement: Add pre-commit checks for identified patterns
Measurement: Track CI failure rate reduction
```
### 7. Multi-Provider Resilience
**Principle**: The system must operate reliably across multiple AI providers with graceful fallback.
**Implementation**:
- Provider-agnostic interfaces and abstractions
- Automated failover between providers
- Rate limit awareness and throttling
- Provider-specific optimizations without vendor lock-in
**Example Application**:
```
Primary: OpenAI GPT-4 for complex reasoning
Fallback 1: Claude for continued operation
Fallback 2: Local models for basic functionality
Circuit Breaker: Automatic switching based on availability
```
### 8. GitHub-Native Integration
**Principle**: Leverage GitHub platform primitives to minimize custom infrastructure and operational overhead.
**Implementation**:
- GitHub Apps for authentication and authorization
- GitHub Actions for automation and workflows
- Issues and PRs for coordination and communication
- GitHub API for all programmatic interactions
**Example Application**:
```
Instead of: Custom task tracking system
Use: GitHub Issues with labels and milestones
Instead of: Custom CI/CD pipeline
Use: GitHub Actions with reusable workflows
```
### 9. Incremental Value Delivery
**Principle**: All work should be broken into small, independently valuable increments.
**Implementation**:
- Target 2-8 hour work increments for AI-accelerated development
- Each PR represents complete, testable functionality
- Continuous integration and deployment patterns
- Feature flags for gradual rollout
**Example Application**:
```
Feature: User Dashboard
Increment 1: Basic layout and navigation (2h)
Increment 2: User profile widget (3h)
Increment 3: Activity feed (4h)
Increment 4: Settings panel (2h)
Each increment is fully functional and deployable
```
### 10. Transparent Governance
**Principle**: All decisions, processes, and constraints must be openly documented and auditable.
**Implementation**:
- Public documentation of all policies and procedures
- Clear audit trails for all changes
- Role-based access controls with justification
- Regular governance reviews and updates
**Example Application**:
```
Decision: Change deployment frequency
Documentation: ADR with rationale
Audit Trail: Git history of decision
Review: Monthly governance meeting
Update: Adjust based on operational metrics
```
## How to Query Principles
You can ask about specific principles or search across all principles:
### Query by Number
"What is principle 5?" → Returns Evidence-Based Decision Making
### Query by Topic
"How does Wolf handle security?" → Returns relevant principles (2, 5, 7)
### Query by Implementation
"How to make decisions?" → Returns principles 3, 5, 6
### Get All Principles
"Show all principles" → Returns complete list with summaries
## Principle Conflicts Resolution
When principles appear to conflict, use this priority order:
1. **Security and Safety** (Principles 2, 7)
2. **Evidence and Quality** (Principles 3, 5, 6)
3. **Operational Efficiency** (Principles 1, 8, 9)
4. **Governance and Compliance** (Principles 4, 10)
## Integration with Other Skills
- **wolf-archetypes**: Principles inform archetype behavior
- **wolf-roles**: Each role implements relevant principles
- **wolf-governance**: Principles guide governance rules
## Scripts Available
- `query.js` - Search principles by ID, keyword, or topic
- `apply.js` - Generate principle-based recommendations for specific scenarios
## Evolution and Updates
These principles evolve based on operational evidence. Changes require:
1. Evidence collection showing insufficiency
2. Impact analysis on existing systems
3. Community review from all roles
4. Advisory-first deployment
5. Post-implementation assessment
## Red Flags - STOP
If you catch yourself thinking:
-**"This is too simple to need principles"** - Simple decisions cascade. Even trivial choices compound over time. Query principles BEFORE proceeding.
-**"I know the right approach already"** - Evidence before opinions (Principle 5). Your intuition needs validation against principles.
-**"Principles don't apply to this work type"** - ALL work has principles. Research? Use Principle 3. Bug fix? Use Principle 1. No exceptions.
-**"I'll check principles after implementation"** - Too late. Principles guide implementation, not justify it post-hoc.
-**"This conflicts with deadline pressure"** - Principles ENABLE speed by preventing rework. Skipping principles slows you down.
-**"I'm just prototyping"** - Prototypes become production (always). Use Principle 9 (incremental value) even for experiments.
**STOP. Use Skill tool to load wolf-principles BEFORE proceeding.**
## After Using This Skill
**REQUIRED NEXT STEPS:**
```
Sequential skill chain - DO NOT skip steps
```
1. **REQUIRED NEXT SKILL**: Use **wolf-archetypes** to determine behavioral archetype
- **Why**: Principles are strategic guidance. Archetypes translate them into tactical requirements for your specific work type.
- **Gate**: Cannot proceed to implementation without archetype selection
- **Tool**: Use Skill tool to load wolf-archetypes
2. **REQUIRED NEXT SKILL**: Use **wolf-governance** to identify quality gates
- **Why**: Archetypes define priorities. Governance defines acceptance criteria and Definition of Done.
- **Gate**: Cannot claim work complete without meeting governance requirements
- **Tool**: Use Skill tool to load wolf-governance
3. **REQUIRED NEXT SKILL**: Use **wolf-roles** to understand collaboration patterns
- **Why**: Work rarely happens in isolation. Roles define who does what and how handoffs occur.
- **Gate**: Cannot proceed without understanding role boundaries
- **Tool**: Use Skill tool to load wolf-roles
**DO NOT PROCEED to implementation without completing steps 1-3.**
### Verification Checklist
Before claiming you've applied principles:
- [ ] Queried wolf-principles for relevant guidance
- [ ] Selected archetype using wolf-archetypes
- [ ] Identified quality gates using wolf-governance
- [ ] Loaded role guidance using wolf-roles
- [ ] Created artifact (PR, ADR, journal entry) documenting decisions
**Can't check all boxes? Work is incomplete. Return to this skill.**
---
*Source: docs/principles.md (lines 292-527)*
*Last Updated: 2025-11-14*
*Phase: Superpowers Skill-Chaining Enhancement v2.0.0*

View File

@@ -0,0 +1,314 @@
#!/usr/bin/env node
/**
* Wolf Principles Query Script
* Searches and filters Wolf's 10 core principles
*
* Usage:
* node query.js --id 5 # Get principle by number
* node query.js --search "security" # Search by keyword
* node query.js --topic "decision" # Find by topic
* node query.js --all # List all principles
*/
const principles = [
{
id: 1,
name: "Artifact-First Development",
description: "All work produces durable, verifiable artifacts rather than ephemeral conversations",
keywords: ["artifacts", "PRs", "documentation", "traceability", "audit"],
topics: ["development", "process", "quality"],
implementation: [
"Pull Requests are the primary unit of work",
"Every change must be committed, reviewed, and merged",
"Conversations captured in issues, ADRs, and journals",
"No work complete without merged artifact"
]
},
{
id: 2,
name: "Role Isolation and Separation of Concerns",
description: "Each agent role has clearly defined responsibilities with minimal overlap and strict boundaries",
keywords: ["roles", "isolation", "boundaries", "permissions", "security"],
topics: ["architecture", "security", "organization"],
implementation: [
"Individual GitHub Apps per role with minimal permissions",
"Agents cannot merge their own implementations",
"Clear ownership matrices and authority boundaries",
"Role cards define exact scope and non-goals"
]
},
{
id: 3,
name: "Research-Before-Code",
description: "All implementation work must be preceded by structured research and evidence-based recommendations",
keywords: ["research", "evidence", "analysis", "planning"],
topics: ["development", "quality", "decision-making"],
implementation: [
"Mandatory Research Agent analysis before coding",
"Structured research comments with evidence",
"Research label as blocking gate",
"Implementation must align with research"
]
},
{
id: 4,
name: "Advisory-First Enforcement",
description: "New policies and constraints are tested in advisory mode before becoming hard gates",
keywords: ["advisory", "gradual", "rollout", "testing", "policies"],
topics: ["governance", "change-management", "stability"],
implementation: [
"Shadow-mode validation for new rules",
"Gradual rollout with confidence thresholds",
"Evidence collection before enforcement",
"Fallback and rollback mechanisms"
]
},
{
id: 5,
name: "Evidence-Based Decision Making",
description: "All decisions must be supported by concrete evidence and measurable outcomes",
keywords: ["evidence", "metrics", "measurement", "data", "decisions"],
topics: ["decision-making", "quality", "objectivity"],
implementation: [
"Performance budgets with measurements",
"Security scans and validation evidence",
"Test coverage and quality metrics",
"Documented trade-offs with quantified impacts"
]
},
{
id: 6,
name: "Self-Improving Systems",
description: "The system continuously learns from its operations and evolves based on evidence",
keywords: ["learning", "improvement", "evolution", "feedback", "journals"],
topics: ["continuous-improvement", "learning", "adaptation"],
implementation: [
"Comprehensive journaling of problems and decisions",
"Regular retrospectives and pattern identification",
"Automated metrics collection and analysis",
"Feedback loops from operations to design"
]
},
{
id: 7,
name: "Multi-Provider Resilience",
description: "The system must operate reliably across multiple AI providers with graceful fallback",
keywords: ["resilience", "providers", "fallback", "reliability", "failover"],
topics: ["architecture", "reliability", "risk-management"],
implementation: [
"Provider-agnostic interfaces and abstractions",
"Automated failover between providers",
"Rate limit awareness and throttling",
"Provider-specific optimizations without lock-in"
]
},
{
id: 8,
name: "GitHub-Native Integration",
description: "Leverage GitHub platform primitives to minimize custom infrastructure and operational overhead",
keywords: ["GitHub", "integration", "platform", "native", "infrastructure"],
topics: ["architecture", "operations", "efficiency"],
implementation: [
"GitHub Apps for authentication",
"GitHub Actions for automation",
"Issues and PRs for coordination",
"GitHub API for programmatic interactions"
]
},
{
id: 9,
name: "Incremental Value Delivery",
description: "All work should be broken into small, independently valuable increments",
keywords: ["incremental", "small", "value", "delivery", "continuous"],
topics: ["development", "agility", "risk-management"],
implementation: [
"Target 2-8 hour work increments",
"Each PR represents complete functionality",
"Continuous integration and deployment",
"Feature flags for gradual rollout"
]
},
{
id: 10,
name: "Transparent Governance",
description: "All decisions, processes, and constraints must be openly documented and auditable",
keywords: ["transparency", "governance", "audit", "documentation", "compliance"],
topics: ["governance", "compliance", "trust"],
implementation: [
"Public documentation of all policies",
"Clear audit trails for all changes",
"Role-based access controls with justification",
"Regular governance reviews and updates"
]
}
];
/**
* Query principles by ID
*/
function queryById(id) {
const principle = principles.find(p => p.id === parseInt(id));
if (!principle) {
return { error: `No principle found with ID ${id}` };
}
return formatPrinciple(principle, true);
}
/**
* Search principles by keyword
*/
function searchByKeyword(keyword) {
const lower = keyword.toLowerCase();
const matches = principles.filter(p =>
p.name.toLowerCase().includes(lower) ||
p.description.toLowerCase().includes(lower) ||
p.keywords.some(k => k.toLowerCase().includes(lower)) ||
p.implementation.some(i => i.toLowerCase().includes(lower))
);
if (matches.length === 0) {
return { message: `No principles found matching "${keyword}"` };
}
return {
query: keyword,
count: matches.length,
principles: matches.map(p => formatPrinciple(p, false))
};
}
/**
* Find principles by topic
*/
function findByTopic(topic) {
const lower = topic.toLowerCase();
const matches = principles.filter(p =>
p.topics.some(t => t.toLowerCase().includes(lower))
);
if (matches.length === 0) {
return { message: `No principles found for topic "${topic}"` };
}
return {
topic: topic,
count: matches.length,
principles: matches.map(p => formatPrinciple(p, false))
};
}
/**
* Get all principles
*/
function getAllPrinciples() {
return {
count: principles.length,
principles: principles.map(p => formatPrinciple(p, false))
};
}
/**
* Format a principle for output
*/
function formatPrinciple(principle, detailed = false) {
const base = {
id: principle.id,
name: principle.name,
description: principle.description
};
if (detailed) {
return {
...base,
keywords: principle.keywords,
topics: principle.topics,
implementation: principle.implementation,
usage_example: getUsageExample(principle.id)
};
}
return base;
}
/**
* Get usage example for a principle
*/
function getUsageExample(id) {
const examples = {
1: "Create PR with fix, tests, and documentation instead of just reporting 'fixed'",
2: "PM defines requirements, Coder implements, Reviewer validates, QA verifies",
3: "Research existing patterns before implementing new authentication",
4: "Test new coverage rules in advisory mode before enforcing",
5: "Benchmark REST vs GraphQL with real metrics before choosing",
6: "Journal CI failures, identify patterns, implement preventive checks",
7: "Use OpenAI with Claude fallback and local model for basic ops",
8: "Use GitHub Issues instead of custom task tracking",
9: "Break dashboard into 2-4 hour increments, each deployable",
10: "Document decisions in ADRs with clear audit trails"
};
return examples[id] || "See skill documentation for examples";
}
// Parse command line arguments
function parseArgs() {
const args = process.argv.slice(2);
const options = {};
for (let i = 0; i < args.length; i++) {
if (args[i] === '--id' && args[i + 1]) {
options.id = args[i + 1];
i++;
} else if (args[i] === '--search' && args[i + 1]) {
options.search = args[i + 1];
i++;
} else if (args[i] === '--topic' && args[i + 1]) {
options.topic = args[i + 1];
i++;
} else if (args[i] === '--all') {
options.all = true;
}
}
return options;
}
// Main execution
import { fileURLToPath } from 'url';
import { dirname } from 'path';
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
// Check if running as main module
if (import.meta.url === `file://${process.argv[1]}`) {
const options = parseArgs();
let result;
if (options.id) {
result = queryById(options.id);
} else if (options.search) {
result = searchByKeyword(options.search);
} else if (options.topic) {
result = findByTopic(options.topic);
} else if (options.all) {
result = getAllPrinciples();
} else {
result = {
error: "Please specify --id, --search, --topic, or --all",
usage: "node query.js --id 5 | --search 'security' | --topic 'decision' | --all"
};
}
console.log(JSON.stringify(result, null, 2));
}
// Export for use as module
export {
queryById,
searchByKeyword,
findByTopic,
getAllPrinciples,
principles
};