Claude Skills by prime-radiant-inc

github.com/prime-radiant-inc

31 skillsA× 29B× 20 installs22 views

Analysis PipelineAReverse engineering - multi-source product intelligence analysis with provenance tracking. Master methodology for all analysis agents.researchtypescriptgoVotes: 0GitHub stars: 269
Autonomous DiscoveryBLayer 1 intelligence source discovery - auto-detect available sources, search for public information, negotiate with user, produce inventory manifestdocumentationjavascripttypescriptVotes: 0GitHub stars: 269
Behavioral Spec WritingALayer 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 behavioral specifications. Loaded by the analyzer agent during Layer 3.developmentjavascripttypescriptVotes: 0GitHub stars: 269
Binary AnalysisALayer 1 methodology for extracting behavioral intelligence from compiled binaries, bytecode archives, managed assemblies, and bundled applications. Covers artifact identification, string extraction strategy, decompilation workflows, provenance requirements, and handoff to source code mode.ai-agentspythonrustVotes: 0GitHub stars: 269
Community IntelligenceALayer 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 gathering.documentationgonodeVotes: 0GitHub stars: 269
Container ExecutionBInfrastructure skill for containerized target execution. Runtime detection, container lifecycle, security restrictions, interaction patterns.devopspythonrustVotes: 0GitHub stars: 269
Skills DirectorySkills Directory Look up any of 103,619 scanned skills — grade, score, and every finding. Free.Check a skill
Contract DetectionALayer 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.documentationgojavaVotes: 0GitHub stars: 269
Doc ResearchAPublic 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.documentationjavascriptgoVotes: 0GitHub stars: 269
Ecosystem AnalysisALayer 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.developmentjavascripttypescriptVotes: 0GitHub stars: 269
Fidelity ValidationACross-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.ai-agentsgoVotes: 0GitHub stars: 269
Git ArchaeologyALayer 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 during Layer 1.documentationgobashVotes: 0GitHub stars: 269
Incremental AnalysisADetect 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.ai-agentsbashgitVotes: 0GitHub stars: 269
Multi Source SynthesisALayer 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. Loaded by the analyzer agent during Layer 2.documentationgobashVotes: 0GitHub stars: 269
Provenance MethodologyACross-cutting provenance discipline. Citation format, confidence levels, source hierarchy, session capture. Loaded by every analysis agent.documentationgogitVotes: 0GitHub stars: 269
Runtime ObservationACross-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 observation roles.ai-agentsgoshellVotes: 0GitHub stars: 269
Second Pass ReviewAIndependent 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.developmentjavascripttypescriptVotes: 0GitHub stars: 269
Source AnalysisALayer 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 analysis templates.developmentjavascripttypescriptVotes: 0GitHub stars: 269
Source CompletenessAValidates 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 Layer 3 specs are written, BEFORE sanitization.documentationtypescriptpythonVotes: 0GitHub stars: 269
Spec SanitizationASanitizes analysis specs to remove implementation contamination while preserving provenance metadata. Run in SEPARATE SESSION after analysis, before implementation.developmenttypescriptpythonVotes: 0GitHub stars: 269
Test Suite AnalysisALayer 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. Loaded by the analyzer agent during Layer 1.testingjavascripttypescriptVotes: 0GitHub stars: 269
Validation MethodologyACross-cutting validation discipline. Acceptance criteria format, definition of done checklists, quality gate criteria, verification methods. Loaded by every analysis agent.ai-agentsrustgoVotes: 0GitHub stars: 269
Visual ExplorationALayer 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 during Layer 1.documentationjavascriptgoVotes: 0GitHub stars: 269
Auditing ProgressAUse when an iteration has just finished and you need to verify behavior evidence quality in three tiers — deep evidence for current stories, impacted behavior for touched scenarios, and sentinel corpus for high-value regression detection.ai-agentsVotes: 0GitHub stars: 177
Extracting RequirementsAUse when starting an iterative-development run on human spec collateral — reads the spec, produces per-epic requirement files with proof obligations and behavior scenario cards with stable IDs.ai-agentspythonbashVotes: 0GitHub stars: 177
Implementing TasksAUse when executing a batch of TDD-sized tasks inside a running-an-iteration call — dispatches an implementer subagent per task following red-green-refactor discipline and returns per-task completion status.ai-agentsVotes: 0GitHub stars: 177
Iterative DevelopmentAUse when implementing a project with a large, comprehensive, or ambiguous spec — extracts requirements with proof obligations, defines a walking skeleton with its first journey scenario, then loops through audited sprints that continuously build a behavior evidence corpus. Completion means passing evidence, not just finished stories.ai-agentsgogitVotes: 0GitHub stars: 177
Running An IterationAUse when executing the next pending iteration from an iterative-development roadmap — picks the iteration, decomposes into code and evidence tasks, runs sentinel corpus baseline, dispatches implementing-tasks, runs impacted + sentinel scenarios, and updates artifacts.ai-agentspythonrustVotes: 0GitHub stars: 177
Scoping The Simplest CoreAUse when turning extracted requirements into a roadmap — selects the walking skeleton iteration with its first journey scenario, orders remaining work into follow-on iterations, and applies story splitting when ACs have different dependency profiles.ai-agentspythonbashVotes: 0GitHub stars: 177
Multi Model Adversarial ReviewAUse for high-stakes review where you want multiple model providers reviewing the same artifact and critiquing each other. Shells out to installed coding-agent CLIs (claude, codex, gemini, pi, opencode by default; amp and droid available opt-in) to run parallel reviews, then runs a cross-critique grid where each reviewer evaluates the others' findings to catch hallucinations and severity inflation, then synthesizes a final deduplicated report. Triggers on "MMAR review", "multi-model review", "...developmentgoshellVotes: 0GitHub stars: 17
Parallel Adversarial ReviewAUse when reviewing a diff, commit, branch, or implementation against a spec — dispatches two same-model reviewer subagents in parallel under a competitive scoring frame, then aggregates findings. Triggers on "review this", "PAR review", "adversarial review", or any evaluative gate (scope review, spec compliance, code quality, audit).developmentgogitVotes: 0GitHub stars: 17
Github TriageAUse when triaging GitHub issues or pull requests - categorizing, labeling, organizing open items, validating if issues are still relevant, and reviewing/merging PRs. Handles taxonomy discovery, label creation, bulk tagging, issue staleness checks, and full PR review + merge workflows.code-qualitypythonrustVotes: 0GitHub stars: 4