Files
AI_template/agents/README.md
olekhondera db5ba04fb9 feat: expand agents (10), skills (20), and hooks (11) with profile system
Agents:
- Add YAML frontmatter (model, tools) to all 7 existing agents
- New agents: planner (opus), build-error-resolver (sonnet), loop-operator (sonnet)

Skills:
- search-first: research before building (Adopt/Extend/Compose/Build)
- verification-loop: full quality gate pipeline (Build→TypeCheck→Lint→Test→Security→Diff)
- strategic-compact: when and how to run /compact effectively
- autonomous-loops: 6 patterns for autonomous agent workflows
- continuous-learning: extract session learnings into instincts

Hooks:
- Profile system (minimal/standard/strict) via run-with-profile.sh
- config-protection: block linter/formatter config edits (standard)
- suggest-compact: remind about /compact every ~50 tool calls (standard)
- auto-tmux-dev: suggest tmux for dev servers (standard)
- session-save/session-load: persist and restore session context (Stop/SessionStart)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-24 20:16:20 +02:00

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Markdown

# Agent Profiles
This directory contains specialized AI agent profiles. Each profile defines a role, principles, constraints, and workflow for a specific domain.
## Available Agents
| Agent | File | Model | Use When |
| -------------------- | ------------------------- | ------ | -------------------------------------------------------- |
| Planner | `planner.md` | opus | Breaking down tasks, planning implementations, risk assessment |
| Frontend Architect | `frontend-architect.md` | opus | UI components, performance, accessibility, React/Next.js |
| Backend Architect | `backend-architect.md` | opus | System design, databases, APIs, scalability |
| Security Auditor | `security-auditor.md` | opus | Security review, vulnerability assessment, auth flows |
| Code Reviewer | `code-reviewer.md` | sonnet | Code quality, PR review, best practices |
| Test Engineer | `test-engineer.md` | sonnet | Test strategy, automation, CI/CD, coverage |
| Prompt Engineer | `prompt-engineer.md` | sonnet | LLM prompts, agent instructions, prompt optimization |
| Documentation Expert | `documentation-expert.md` | sonnet | Technical writing, user/admin guides, docs maintenance |
| Build Error Resolver | `build-error-resolver.md` | sonnet | Fix build/type/lint errors with minimal changes |
| Loop Operator | `loop-operator.md` | sonnet | Monitor autonomous loops, detect stalls, escalate |
## Model Selection
- **opus** — Deep reasoning tasks: planning, architecture, security review. Slower but more thorough.
- **sonnet** — Implementation tasks: code review, testing, writing, fixing. Faster turnaround.
## Tool Restrictions
Each agent declares a `tools` array in its frontmatter, following the principle of least privilege:
- **Read-only agents** (planner, architects): Read, Glob, Grep — they advise, not implement
- **Implementation agents** (test-engineer, build-error-resolver): Read, Glob, Grep, Edit, Write, Bash
- **Review agents** (code-reviewer): Read, Glob, Grep, Bash (for git commands)
## Agent Selection
See `RULES.md` sections 4-5 for the selection protocol and multi-agent coordination.
## Using context7 (Shared Guidelines)
All agents use context7 to access up-to-date documentation. Training data may be outdated — always verify through context7 before making recommendations.
### When to Use
**Always query context7 before:**
- Recommending specific library/framework versions
- Suggesting API patterns or method signatures
- Advising on security configurations or CVEs
- Checking for deprecated features or breaking changes
- Verifying browser support or compatibility matrices
### How to Use
1. **Resolve library ID**: Use `resolve-library-id` to find the correct context7 library identifier
2. **Query documentation**: Use `query-docs` with the resolved ID and a specific topic
### Example
```
User asks about React Server Components
1. resolve-library-id: "react" → get library ID
2. query-docs: topic="Server Components patterns"
3. Base recommendations on returned documentation, not training data
```
### What to Verify
| Category | Verify |
| ------------- | ---------------------------------------------------------- |
| Versions | LTS versions, deprecation timelines, migration guides |
| APIs | Current method signatures, new features, removed APIs |
| Security | CVE advisories, security best practices, auth patterns |
| Performance | Current optimization techniques, benchmarks, configuration |
| Compatibility | Version compatibility matrices, breaking changes |
### Critical Rule
When context7 documentation contradicts training knowledge, **trust context7**. Technologies evolve rapidly — training data may reference deprecated patterns or outdated versions.
## Adding a New Agent
1. Create a new `.md` file in this directory
2. Use consistent frontmatter: `name`, `model`, `tools`, and `description`
- `model`: `opus` for reasoning-heavy tasks, `sonnet` for implementation
- `tools`: minimal set needed (principle of least privilege)
3. Follow the structure: Role → Core Principles → Constraints → Workflow → Responsibilities → Output Format → Pre-Response Checklist
4. Reference this README for context7 usage instead of duplicating the section
5. Update `DOCS.md` and `README.md` to list the new agent