Walks the Anthropic Skill best-practices checklist plus the RCS Layer-3 documentation contract for authoring a new skill. Covers gerund-form naming, third-person frontmatter description, the 11 required H2 sections in order, the ≤ 500-line body cap with reference/ bundles, one-level-deep reference linking, and the eval-first discipline (3 scenarios written before the body). Use when the user wants to write a new skill — for any host (Claude Code, Copilot CLI, Gemini CLI, Anthropic API plugin) — and needs the authoring rules plus a skeleton to fill in.
Converts existing decision-tree expertise (test-selection trees, triage flowcharts, dispatch rules, troubleshooting trees) into a deterministic walk-the-tree skill where each predicate is a numbered step, each leaf is a named action, and the skill commits to checking predicates in order rather than letting the model shortcut to a leaf based on the user's framing. Use when the user has a written or implicit decision tree (or DAG) whose predicates are objectively checkable and whose leaves are deterministic, and wants the discipline encoded as a skill. Refuses to encode open-judgment domains as trees and refuses to silently drop cycles.
Audits a trained classifier or scoring model for disparate treatment across protected attributes (race, sex, age, disability, religion, national origin, sexual orientation, or any user-supplied subgroup) using equal-opportunity, demographic-parity, calibration-within-group, and intersectional cuts. Use when the model decides outcomes for people (hiring, lending, healthcare triage, content moderation, criminal justice scoring), when a downstream user asks whether the model is "fair", when a regulator requires a disparate-impact analysis, or before deployment of any consequential ML system. Refuses to audit without a documented protected-attribute list and refuses to declare a model "fair" from a single metric.
Audits an MCP (Model Context Protocol) server against six security checks — license, source review, network egress, version pin, secret handling, and tool subset — before the server is registered into the Claude Code tool pool. Use whenever the user proposes adding an MCP server to ~/.claude/mcp.json, a project .mcp.json, or a plugin's mcpServers config. Produces a per-check verdict, a blocking-issues list, and a final integrate / integrate-with-constraints / reject recommendation.
Walks a threat-modeling pass over an autonomous agent or multi-agent system (tool-using agent, planner-executor loop, multi-step workflow, multi-agent collaboration) using a user-supplied catalog (MAESTRO, MITRE ATLAS, OWASP Agentic Security Initiative, or a custom catalog). Extends LLM-app threat modeling with agent-specific boundaries — planner ↔ executor loop, memory ↔ next-turn context, tool-result ↔ next prompt, agent ↔ agent — and adds agentic-specific concerns (excessive agency, goal hijacking, memory poisoning, runaway loops, multi-agent collusion). Methodology only — no bundled catalog; the user provides one. Use when the user is designing, reviewing, or shipping an agent system that plans, calls tools, persists state across turns, or coordinates with other agents.
Walks a STRIDE-style threat-modeling pass over an LLM application (chatbot, RAG system, single-turn completion API, content-generation pipeline) using a user-supplied catalog (OWASP LLM Top 10, MITRE ATLAS, MAESTRO, or a custom catalog). Inventories components and trust boundaries, maps each catalog item to one or more STRIDE categories at each boundary, and produces an auditable threat register with likelihood, impact, and mitigation per finding. Use when the user is designing, reviewing, or shipping an LLM-powered application and needs a structured threat model before deployment. The skill is methodology only — it never ships a bundled threat catalog; the user provides one.
Triages SAST/DAST/SCA vulnerability findings exported as SARIF (Static Analysis Results Interchange Format). Walks the SARIF in -> dedupe-across-tools -> EPSS enrichment -> blast-radius scoring -> suppression-with-rationale pipeline, then emits a PR-ready comment ranked by exploitability and reachability. Use when the user has a SARIF file or a multi-tool finding dump (Semgrep + CodeQL + Snyk + Trivy), when a security scanner produced hundreds of findings and the user needs to prioritize, when finding-fatigue threatens to bury a real critical, or when preparing a security PR comment from raw scanner output.
Runs a multi-subagent adversarial premortem on a spec, plan, design, paper, or codebase by orchestrating six independent perspectives (Red Teamer, Data Scientist, ML Engineer, Security Architect, MLOps/SRE, Governance/Risk) as parallel subagents across up to five rounds. Each perspective reads the artifacts in its own context and returns evidence-anchored findings with calibrated confidence bands; the orchestrator cross-attacks, adjudicates posteriors, applies a drop rule, and emits a prioritized remediation plan. Use for high-stakes AI, ML, data-science, agentic, MLOps, or security-sensitive artifacts where one reviewer's depth is not enough and the cost of being wrong is high. For a single-session multi-round premortem without subagents, use adversarial-premortem-single.