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Found 6,688 Skills
Generate comprehensive OpenSpec specifications directly from the current project state. Use when the user wants to create or populate main specs by analyzing existing code, documentation, AGENTS.md, GitHub issues, and pull requests — without going through the change/proposal workflow. Ideal for bootstrapping specs on a project that already has working code but no specs yet, or for refreshing specs to match the current implementation.
Chinese Git Commit Skill. Analyze changes and generate Chinese conventional commit messages. Triggered when the user says "submit", "commit", "submit code", "submit changes", or "/commit-zh". Executed entirely by the main agent, no subagents used.
Context layer for data agents - builds semantic layer, wiki, and warehouse metadata to enable accurate AI-powered analytics queries
Use when operating the vigolium CLI for web vulnerability scanning, security testing, traffic ingestion, server management, AI agent-driven scanning and code review, cloud-storage management, or writing custom JavaScript extensions. Invoke for scan commands, scan-url, scan-request, run, ingest, server, agent (query/autopilot/swarm/olium/piolium/audit/session), traffic browsing, database queries, storage uploads/downloads, module management, extension scripting, export, project management, and configuration tuning.
Decide where files live in an ML experimentation project: reusable code in `src/<pkg>/`, one `# %%` script per experiment in `experiments/`, design notes + index in `journal/`, reports in `reports/`, agent-only probes in `scratch/`, narrative digest in `overview/summary.md`. Owns the layout, the file-creation rules (one file per experiment, ask before editing), and the jupytext `# %%` script convention. Never imposes `data/` — the user owns that. TRIGGER — any of: - Starting a new ML project / scaffolding a workspace. - About to create the first experiment file in a project. - About to create `src/<pkg>/data.py` / `features.py` / `pipeline.py` / `evaluate.py` for the first time. - About to write a `.ipynb` for experimentation — redirect to a `# %%` script under `experiments/`. - User asks where something should live, how to organize the project, or how to set up the workspace. - About to add a new experiment iteration — decide new file vs edit existing (ask the user). SKIP when: the file is clearly part of an already-populated module (e.g., adding a function to existing `features.py`); pure refactor inside a single existing file; pipeline declaration mechanics (`build-ml-pipeline`); evaluation mechanics (`evaluate-ml-pipeline`); skore symbol lookup (`python-api`). HOW TO USE: **first run the Detection table** below — if any signal matches, glue to existing conventions (do not rename or move folders). If no signal matches, scaffold the default layout. **Emit the Pre-flight checklist as visible text and read the Stop conditions before any file is created or edited.** Use templates in `templates/`; copy and adapt, do not rewrite from scratch.
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.
Use whenever you need an email address to receive something and then read or wait for it, especially during a signup or login flow. PROACTIVELY, and you can usually do this without asking the user first: a service is about to send a verification code, OTP, one-time code, 2FA or two-factor code, confirmation link, magic link, or password reset and you need to wait for it and read the value out; you are signing up for or registering with a service and must confirm an email address to continue; you need a fresh, managed, throwaway, or burner address instead of using a real one; you sent something and need to watch for what lands. REACTIVELY: the user asks "did it arrive?", "check the inbox", "what came in?", "wait for the email", or wants an address to catch replies, codes, receipts, or alerts. Provides a managed `*.primitive.email` address plus `primitive emails latest` and `primitive emails wait` to read and block for mail, and hosted Functions to run JavaScript on every inbound message. No SMTP, no DNS, no mail server. Use this when a third party sends mail TO you; to send your own message and wait for its reply, use the primitive-chat skill. Gets a free `*.primitive.email` address via `primitive agent start-agent-signup` if you do not already have one. Part of the Primitive CLI (Primitive, primitivedotdev, primitive.dev; the `primitive` or `prim` command).
Salesforce Data Cloud Act phase. Use this skill when the user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. TRIGGER when: user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. DO NOT TRIGGER when: the task is segment creation (use data360-segment), data retrieval/search work (use data360-query), or STDM/session tracing (use agentforce-observe).
Salesforce Data Cloud Connect phase. Use this skill when the user manages Data Cloud connections, connectors, or sets up a new source system. TRIGGER when: user manages Data Cloud connections, connectors, connector metadata, tests a connection, browses source objects or databases, or sets up a new source system. DO NOT TRIGGER when: the task is about data streams or DLOs (use data360-prepare), DMOs or identity resolution (use data360-harmonize), retrieval/search (use data360-query), or STDM telemetry (use agentforce-observe).
Develop a Base44 app remotely inside Base44's cloud sandbox using your own agent — no local checkout and no deploy/push commands. The implementation is remote: writing a resource file into the sandbox is what ships it (backend functions, entities, and agents all auto-sync from the file you write), and OAuth connectors are set up against the remote app via MCP tools or the projectless `base44 connectors` CLI. This skill is the place for learning what you can author in the sandbox, how backend functions, entities, and agents are structured, and how to connect a connector without a local filesystem. Triggers on 'develop my Base44 app remotely', 'no local files', 'cloud sandbox', 'create an entity/agent remotely', 'connect a connector remotely', 'bring my own agent', or any work editing a Base44 app inside a sandbox.
Terminal transcription of audio files and URLs with the Gladia CLI (gladia speech-to-text). Use when the user has gladia-cli installed, wants shell-based transcription, or asks an agent to transcribe audio then answer questions about the content. For audio intelligence features not available as CLI flags, use the SDK skills instead.
Decision guide for delegating to hui-style subagents. Tells the main thread WHEN to spawn `huicrew-investigator` (locate code), `huicrew-builder` (1-2 file edit), or `huicrew-reviewer` (diff review) instead of doing the work inline or using vanilla `Explore`. Subagent output is hui-compressed so the tool-result injected back into main context is ~60% smaller — main context lasts longer across long sessions. Trigger: "delegate to subagent", "use huicrew", "spawn investigator/builder/reviewer", "save context", "compressed agent output".