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Found 996 Skills
Use to select, configure, deploy, verify, debug, or tear down a VSS profile (base, search, lvs, warehouse, edge). Not for standalone microservices — use the vss-deploy-* skill.
Use when an operator asks what changed recently, what was left hanging, what can move now, "gm", "where are we", or when a session-derived update needs to become visible to the whole Workbench. Recall recent memory, verify live repo/issue/automation state, surface drift first, and produce a short evidence-labeled action menu.
Look up current research information using parallel-cli search (primary, fast web search), the Parallel Chat API (deep research), or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information. Note: query text is transmitted to api.parallel.ai (PARALLEL_API_KEY) and, for academic searches, to openrouter.ai (OPENROUTER_API_KEY).
Use when implementing or repairing Univer package-local project behavior with assertions.ts, Facade Migration Packs, univer sac apply, univer sac verify, or verify-report loops.
Verify in-text citations, references, author identities, year disambiguation, DOIs, and specified formatting rules; does not assess whether sources support claims. Use when the user asks for "check citation format", "verify in-text citations and references", "check authors with the same surname", "conduct a citation audit", or requests the rw-citation-audit workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Verify whether the analysis units, replication levels, statistical methods, and result reports in the research are consistent, and do not treat report review as re-analysis. Use when the user asks for "check statistical reports", "verify n and replicate experiments", "review statistical methods and results", or requests the rw-statistics-audit workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Drives a disciplined explore → plan → implement → verify loop for changing an AI agent's behavior with confidence — whether fixing a reported failure or introducing a new requirement, business rule, or policy. Grounds the diagnosis in MLflow traces, codifies the desired behavior as a regression test suite (`mlflow.genai.evaluate` assertions in `@mlflow.test` pytest tests), and iterates the agent — not the test — until green, resisting quick system-prompt patches when the real fix is upstream (missing tool, retrieval source, or capability). Use whenever the user wants to fix or change how an agent behaves — e.g. "fix this issue in my agent", "this answer is wrong", "the agent is hallucinating", "improve my agent based on this trace", "make the agent do X instead of Y", "I want the agent to lead with/prioritize/recommend X", "new business rule: the agent should X", "always/never do X", "change the agent's default behavior" — or shares a trace they want addressed.
Use when polling deployment health after a merge to verify the deployed SHA matches and apply a status label.
Validate and configure the local Salesforce development environment. Runs a prerequisite scan showing 🔴/🟡/🟢 status for all required tools (Salesforce CLI, Code Analyzer plugin, Node.js, NPM, Git, Salesforce MCP, Source Tracking) and offers to install or update missing/outdated items. TRIGGER when the user runs /salesforce-development:platform-environment-validate, asks to 'check my setup', 'validate tools', 'verify prerequisites', 'am I set up correctly', or reports that a tool is missing or not working. DO NOT TRIGGER for: org authentication issues (use /salesforce-development:login), deployment problems (use platform-metadata-deploy), or general status checks (use /salesforce-development:status).
Guidelines for Gemini API development. Used to pre-define architecture in the planning phase and verify code patterns in the coding/debugging phase.
Research-first, evidence-first implementation planning in seven gated phases: frame the task, inventory the real codebase, verify ground truth from vendor source or official docs, adversarially audit existing code, analyze gaps, commit to design decisions, and write a phased plan document with timeline, risks, and verification. Enforces "no claim without evidence, no design before research". Use when the user asks to plan, design, architect, audit, or review before implementing a feature, refactor, migration, or system, or mentions deep-plan, /deep-plan, or Plan mode.
LLM-based deep iterative search and reasoning service. Specializes in handling complex problems, automatically decomposing queries, conducting multi-round iterative retrieval, evaluating and verifying information, and finally generating comprehensive and structured deep analysis reports.