Greenfield plugin for Grok Build

Skills (24)

• analysis-pipeline — Reverse engineering - multi-source product intelligence analysis with provenance tracking. Master methodology for all analysis agents.
• provenance-methodology — Cross-cutting provenance discipline. Citation format, confidence levels, source hierarchy, session capture. Loaded by every analysis agent.
• doc-research — Public documentation extraction methodology. Search sequence (3 tiers), behavioral claim extraction rules, output structure, termination criteria, and gap analysis. Loaded by the analyzer agent for documentation research.
• ecosystem-analysis — Layer 1 skill for SDK and ecosystem analysis. SDK discovery strategy, behavioral extraction methodology, integration test mining, output formats. Loaded by the analyzer agent for SDK and ecosystem work.
• community-intelligence — Layer 1 skill for community intelligence gathering. Search channels, extraction methodology, consensus analysis, version-aware behavioral changes, structural contamination guard. Loaded by the analyzer agent for community intelligence gathe
• runtime-observation — Cross-cutting skill for runtime observation methodology. Five-phase observation discipline, container interaction patterns, observation record format, claim extraction, environment variation. Loaded by the analyzer agent for runtime observa
• container-execution — Infrastructure skill for containerized target execution. Runtime detection, container lifecycle, security restrictions, interaction patterns.
• source-analysis — Layer 1 skill for source code analysis — decompose any codebase into analyzable units, extract behavioral claims with provenance. Supports three target shapes (source tree, bundle, decompiled binary), with per-language grep patterns and ana
• multi-source-synthesis — Layer 2 synthesis methodology. Feature discovery, architecture reverse engineering, API extraction, cross-source synthesis with conflict resolution, module mapping. Transforms raw Layer 1 intelligence into structured synthesis documents. Lo
• behavioral-spec-writing — Layer 3 deep documentation methodology. Per-module behavioral specifications, external and behavioral integration contracts, behavior documentation, end-to-end user journey analysis. Transforms Layer 2 synthesis into implementable behaviora
• binary-analysis — Layer 1 methodology for extracting behavioral intelligence from compiled binaries, bytecode archives, managed assemblies, and bundled applications. Covers artifact identification, string extraction strategy, decompilation workflows, provena
• validation-methodology — Cross-cutting validation discipline. Acceptance criteria format, definition of done checklists, quality gate criteria, verification methods. Loaded by every analysis agent.
• spec-sanitization — Sanitizes analysis specs to remove implementation contamination while preserving provenance metadata. Run in SEPARATE SESSION after analysis, before implementation.
• second-pass-review — Independent audit of sanitized specs in workspace/output/. Three parallel LLM-based reviewer roles check structural leakage, content contamination, and behavioral completeness. Run AFTER Layer 5 sanitization, BEFORE implementation handoff.
• fidelity-validation — Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision. Run AFTER sanitization and AFTER contamination audit passes.
• source-completeness — Validates that all user-facing surfaces discovered in source code are captured in the behavioral specs. Catches coverage gaps where the analysis pipeline missed features, tools, env vars, CLI flags, or other behavioral interfaces. Run AFTER
• autonomous-discovery — Layer 1 intelligence source discovery - auto-detect available sources, search for public information, negotiate with user, produce inventory manifest
• git-archaeology — Layer 1 skill for mining git history for behavioral intelligence. Repository overview, commit message mining, PR/MR description extraction, blame-based maintenance heat maps, issue tracker cross-referencing. Loaded by the analyzer agent dur
• test-suite-analysis — Layer 1 skill for extracting behavioral intelligence from test suites. Framework detection, test code reading strategy, test execution strategy, behavioral claim extraction with Given/When/Then mapping, e2e vs unit value classification. Loa
• visual-exploration — Layer 1 skill for browser automation and visual UX documentation. Initial reconnaissance, navigation mapping, interaction documentation, end-to-end flow capture, edge case exploration, screenshot methodology. Loaded by the analyzer agent du
• contract-detection — Layer 1 skill for parsing machine-readable API contracts. OpenAPI/Swagger, GraphQL, Protobuf/gRPC, and JSON Schema detection, extraction, and behavioral claim generation. Loaded by the analyzer agent during Layer 1.
• incremental-analysis — Detect existing workspace, diff current sources against high water mark metadata, classify specs as unchanged/stale/orphaned/new, re-analyze only what changed. Activates automatically when /analyze finds an existing workspace.
• analyze — Reverse engineer a codebase into the output behavioral specs with provenance
• sanitize — Sanitization - Provenance-aware sanitization of raw specs for independent separation