gh-nice-wolf-studio-wolf-skills-marketplace-wolf-core/skills/wolf-principles/SKILL.md

name, description, version, triggers

name	description	version	triggers
wolf-principles	Wolf's 10 core principles for agent behavior and system design	1.2.0	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.

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Source: docs/principles.md (lines 292-527) Last Updated: 2025-11-14 Phase: Superpowers Skill-Chaining Enhancement v2.0.0