TraceMap AI converts PRDs, specs, acceptance criteria, test cases, and multi-repo code changes into a unified requirement-test-code traceability matrix.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tracemap-ai
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install tracemap-ai using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is TraceMap AI?
TraceMap AI is a specialized testing and verification tool designed to align product requirements with implementation and test suites. It systematically analyzes PRDs, specifications, and code repositories to create an end-to-end traceability matrix. By connecting product intentions directly to code artifacts and testing evidence, it ensures that software releases meet high quality bars.
This capability makes it an invaluable addition to the ecosystem of Openclaw Skills, enabling developers and QA specialists to automate validation for frontend applications, backend microservices, configurations, and complex AI/LLM workflows.
TraceMap AI Use Cases
- PRD & Spec Validation: Verify if product requirements or technical specifications are fully covered by code and corresponding test suites.
- Pre-Release Test Review: Audit test cases before executing tests, development cycles, or final release acceptance.
- Multi-Repo Change Analysis: Inspect cross-repository changes spanning frontend, backend, configuration, and data pipelines to maintain system coherence.
- Compliance & QA Sign-Off: Generate verifiable traceability evidence to streamline QA sign-off and release governance protocols.
- Gap & Defect Detection: Uncover missing implementation fragments, weak acceptance criteria, and critical business-impacting bugs.
How TraceMap AI Works
- Input Collection: Aggregates all necessary reference files, including PRDs, specs, existing test cases, Git Diffs, prompt configurations, and overall release scope.
- Requirement Extraction: Parses dense documents to extract atomic, highly testable acceptance criteria points.
- Implementation Mapping: Maps each requirement to specific source code files, configurations, microservices, prompts, and data flows.
- Test Alignment: Correlates requirements with test fixtures, test cases, and expected execution evidence.
- Coverage Classification: Categorizes requirements into covered, partially covered, uncovered, redundant, or blocked states.
- Defect Analysis: Identifies system bugs and ranks them by severity, complete with reproduction paths and suggested owners.
- Report Generation: Outputs a comprehensive markdown report showcasing the completed matrix, gap summaries, and acceptance advice.
TraceMap AI Setup
To leverage this capability within Openclaw Skills, configure your agent workflow and execute the validation process using the command line interface.
# Initialize the tracing skill within your workspace
openclaw skill install tracemap-ai
# Run a traceability matrix generation over your PRD and repository
openclaw run tracemap-ai --prd ./docs/PRD.md --repo ./src
TraceMap AI Data Schema & Taxonomy
The skill manages its validation playbook references and constructs structural matrices for reporting. Data taxonomy is managed as follows:
Document Architecture
| File / Component | Location / Context | Usage |
|---|---|---|
| Traceability Playbook | references/traceability-validation-playbook.md |
Provides core templates, mapping dimensions, and specific coverage categories. |
| Output Report | Custom Output Directory | Standardized Markdown report detailing release recommendations and lists of open defects. |
Core Matrix Standard
- Requirement Mapping: Relates Requirement IDs directly to designated Code Locations and Test Cases.
- AI Workflow Taxonomy: Specifically tags AI, Model, or Agent Links alongside traditional validation points to record fallbacks and safety constraints.
TraceMap AI Advanced Features
- Multi-Repo Tracing: Evaluates system-wide changes crossing independent codebases simultaneously.
- AI Workflow Validation Matrix: Offers deep-dive testing support tailored for RAG pipelines, LLM prompt engineering, and agentic AI behaviors.
- Automated Fallback & Observability Checks: Validates safety nets, fallback mechanisms, and logging visibility inside modern AI applications.
- Strict Implementation Separation: Distinctly isolates missing test architecture from completely missing feature implementation code to simplify developer debugging.
SKILL.md
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