architectural-forensics
Master protocol for deconstructing agent frameworks to inform derivative system architecture. Use when (1) analyzing an agent framework's codebase comprehensively, (2) comparing multiple frameworks to select best practices, (3) designing a new agent system based on prior art, (4) documenting architectural decisions with evidence, or (5) conducting technical due diligence on AI agent implementations. This skill orchestrates sub-skills for data substrate, execution engine, cognitive architecture, and synthesis phases.
npx skills add majiayu000/claude-skill-registry --skill architectural-forensics-dowwie-agent-framework-stud --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Architectural Forensics Protocol Deconstruct agent frameworks to inform derivative system architecture. ## Mission Distinguish between **software engineering decisions** (how it runs) and **cognitive architecture decisions** (how it thinks) to extract reusable patterns for new systems. ## Quick Start ```bash # 1. Map the codebase (uses codebase-mapping skill's script) python .claude/skills/codebase-mapping/scripts/map_codebase.py /path/to/framework --output codebase-map.json # 2. Run analysis via the command /analyze-frameworks ``` ## Protocol Phases ### Phase 1: Engineering Chassis Analyze the software substrate. See `references/phase1-engineering.md` for detailed guidance. | Analysis | Focus Files | Output | |----------|-------------|--------| | Data Substrate | types.py, schema.py, state.py | Typing strategy, mutation patterns | | Execution Engine | runner.py, executor.py, agent.py | Async model, control flow topology | | Component Model | base_*.py, interfaces.py | Abstraction depth, DI patterns | | Resilience | executor.py, try/except blocks | Error propagation, sandboxing | ### Phase 2: Cognitive Architecture Extract agent "business logic". See `references/phase2-cognitive.
- Mission
- Quick Start
- Protocol Phases
- Phase 1: Engineering Chassis
- Phase 2: Cognitive Architecture
- Phase 3: Synthesis
- Execution Workflow
- Quick Analysis (Single Framework)
- Output Directory Structure
- State Management & Resumption
- Agent Orchestration
- Agent Roles
- Key Innovation: Cluster-Based Reading
- Sub-Skill Reference
python .claude/skills/codebase-mapping/scripts/map_codebase.py /path/to/framework --output codebase-map.json
What does the architectural-forensics skill do?
Master protocol for deconstructing agent frameworks to inform derivative system architecture. Use when (1) analyzing an agent framework's codebase comprehensively, (2) comparing multiple frameworks to select best practices, (3) designing a new agent system based on prior art, (4) documenting architectural decisions with evidence, or (5) conducting technical due diligence on AI agent implementations. This skill orchestrates sub-skills for data substrate, execution engine, cognitive architecture, and synthesis phases.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill architectural-forensics-dowwie-agent-framework-stud --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.