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.
Best use case
architectural-forensics is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "architectural-forensics" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/architectural-forensics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How architectural-forensics Compares
| Feature / Agent | architectural-forensics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this 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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.md` for detailed guidance.
| Analysis | Focus Files | Output |
|----------|-------------|--------|
| Control Loop | agent.py, loop.py | Reasoning pattern, step function |
| Memory | memory.py, context.py | Context assembly, eviction policies |
| Tool Interface | tool.py, functions.py | Schema generation, error feedback |
| Harness-Model Protocol | llm.py, adapters/, stream.py | Wire format, tool call encoding, agentic primitives |
| Multi-Agent | orchestrator.py, router.py | Coordination model, state sharing |
### Phase 3: Synthesis
Generate actionable outputs:
1. **Best-of-Breed Matrix** → Framework comparison table
2. **Anti-Pattern Catalog** → "Do Not Repeat" list
3. **Reference Architecture** → New framework specification
## Execution Workflow
```
┌─────────────────────────────────────────────────────────┐
│ For Each Framework │
├─────────────────────────────────────────────────────────┤
│ 1. codebase-mapping │
│ ↓ │
│ 2. Phase 1 Analysis (parallel) │
│ ├── data-substrate-analysis │
│ ├── execution-engine-analysis │
│ ├── component-model-analysis │
│ └── resilience-analysis │
│ ↓ │
│ 3. Phase 2 Analysis (parallel) │
│ ├── control-loop-extraction │
│ ├── memory-orchestration │
│ ├── tool-interface-analysis │
│ ├── harness-model-protocol │
│ └── multi-agent-analysis (if applicable) │
└─────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ Synthesis │
├─────────────────────────────────────────────────────────┤
│ 4. comparative-matrix │
│ 5. antipattern-catalog │
│ 6. architecture-synthesis │
└─────────────────────────────────────────────────────────┘
```
## Quick Analysis (Single Framework)
For rapid assessment, run the minimal path:
```
codebase-mapping → execution-engine-analysis → control-loop-extraction → tool-interface-analysis
```
## Output Directory Structure
```
forensics-output/ # Working/intermediate files
├── .state/
│ ├── manifest.json
│ └── {framework}.state.json
└── frameworks/
└── {framework}/
├── codebase-map.json
├── phase1/*.md
└── phase2/*.md
reports/ # Final deliverables
├── frameworks/
│ └── {framework}.md # Framework summary
└── synthesis/
├── comparison-matrix.md
├── antipatterns.md
├── reference-architecture.md
└── executive-summary.md
```
## State Management & Resumption
The protocol is designed to be stateful and resumable.
- **Idempotency**: The Orchestrator tracks progress in `manifest.json` and will skip frameworks marked as `completed`.
- **Clean Slate Resumption**: If a run is interrupted, frameworks marked as `in_progress` are considered "stale". Use `python scripts/state_manager.py reset-running` to move them back to `pending` and delete their partial output directories, ensuring a clean restart for those items.
## Agent Orchestration
This skill uses a **4-tier hierarchy** of specialized agents for context efficiency:
```
Orchestrator
│
└── Framework Agents (parallel, one per framework)
│
└── Skill Agents (parallel, one per skill) [COORDINATORS]
│
└── Reader Agents (parallel, one per file cluster) [EXTRACTORS]
│
└── Synthesis Agent (cross-framework synthesis)
```
### Agent Roles
| Agent | Context Budget | Reads | Produces |
|-------|---------------|-------|----------|
| **Orchestrator** | ~10K | State files | Coordination decisions |
| **Framework Agent** | ~50K | Skill outputs | Framework summary report |
| **Skill Agent** | ~25K | Cluster extracts | Skill analysis report |
| **Reader Agent** | ~20K | 1-5 source files | JSON extract (~2K) |
| **Synthesis Agent** | ~40K | All framework reports | Comparison matrix, architecture spec |
### Key Innovation: Cluster-Based Reading
Reader Agents read **file clusters** (1-5 related files) rather than individual files:
- Clusters are grouped by relationship: hierarchy, module cohort, type+usage, interface+impl
- Cross-file patterns (inheritance, imports, shared state) are captured in the extract
- This enables understanding architectural patterns that span multiple files
See:
- `references/orchestrator-agent.md` — Top-level coordination
- `references/framework-agent.md` — Per-framework analysis coordination
- `references/skill-agent.md` — Skill coordination and cluster assignment
- `references/reader-agent.md` — File cluster extraction
- `references/synthesis-agent.md` — Cross-framework synthesis
## Sub-Skill Reference
| Skill | Purpose | Key Outputs |
|-------|---------|-------------|
| `codebase-mapping` | Repository structure | File tree, dependencies, entry points |
| `data-substrate-analysis` | Type system | Typing strategy, serialization |
| `execution-engine-analysis` | Control flow | Async model, event architecture |
| `component-model-analysis` | Extensibility | Abstraction patterns, DI |
| `resilience-analysis` | Error handling | Error propagation, sandboxing |
| `control-loop-extraction` | Reasoning loop | Pattern classification, step function |
| `memory-orchestration` | Context management | Assembly, eviction, tiers |
| `tool-interface-analysis` | Tool system | Schema gen, error feedback |
| `harness-model-protocol` | LLM interface layer | Wire format, encoding, agentic primitives |
| `multi-agent-analysis` | Coordination | Handoffs, state sharing |
| `comparative-matrix` | Comparison | Decision tables |
| `antipattern-catalog` | Tech debt | Do-not-repeat list |
| `architecture-synthesis` | New design | Reference spec |Related Skills
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