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Found 2,624 Skills
Join and participate in sc-chatroom group chats (the "Workroom" product). Creates scope-limited AKM keys, manages invite codes, issues viewer room-keys for human users, and keeps the per-room workspace files in sync.
Open or return Logfire project pages, live views, trace links, and Explore pages in the Codex browser without querying telemetry first. Use this skill when the user asks to "open in Logfire", "show in the live view", "open Explore", "open the UI", "show in Codex", "use the browser", "give me a link", or asks for a Logfire GUI/browser/live-view presentation of a project, time range, service, span, trace, log, or filter. If "show" or "view" wording is ambiguous, ask whether the user wants a UI view or query analysis.
Control Unreal Engine 5 editor via HTTP commands. Spawn/delete/transform actors, manage blueprints, materials, animation blueprints, and any UObject property via reflection. Use when the user asks to create, modify, or query anything in UE5 editor, or mentions UE5, Unreal, actors, blueprints, levels, materials, animation, input, or characters.
Run an ordered sequence of pm-skills against one input via the pm-workflow-orchestrator sub-agent, pausing for go/no-go and stopping on a failed or empty step. Dispatches natively on Claude Code with the pm-skills plugin (invokes @agent-pm-skills:pm-workflow-orchestrator, which delegates each step through the Skill tool); on non-Claude clients (Codex CLI, Cursor, Windsurf, Copilot, Gemini CLI) reads agents/pm-workflow-orchestrator.md and walks the loop inline after a tool-capability pre-flight. Explicit invocation only; never fires proactively. EXPERIMENTAL on all non-Claude clients and on the native path until smoke-tested; run --dry-run first.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
When the user wants to build or improve a sales bot's ability to manage sender reputation and ensure messages get delivered. Also use when the user mentions "deliverability," "spam prevention," "sender reputation," "email warmup," or "domain reputation."
General UI/UX judgment for layout, polish, visual hierarchy, spacing, typography, color, and accessibility. Use when no product-specific or Frappe-specific design system skill applies.
Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/AlloyDB Omni/MySQL/Oracle/MSSQL/CockroachDB/Yugabyte/MongoDB/GizmoSQL/Redis/Spanner/BigQuery/Azurite/MinIO tests. Not for mocked DBs.
Gary Vaynerchuk's jab-jab-jab-right-hook framework applied to a personal portfolio rotation on X and LinkedIn. Jabs = build-in-public + educational (value). Hooks = promo (the ask). Each property in the user's configured portfolio (see `~/.config/makerskills/jab-hook/properties.yaml`) gets a hook at least once every ~3 weeks; jabs fill the rest. Drafts go into the user's Typefully workspace via MCP. Modes — plan (7-day plan), pick-next (single post), audit (coverage report), draft (specific post). Triggers on "/jab-hook," "what should I post," "plan my socials," "next promo," "next jab," "next hook," "social rotation," "promote [property]," "BIP post," "audit my socials," "what haven't I posted about."
Review the accessibility, version, identifiers, restrictions, and statements of research data, code, and materials, and do not equate public availability with reusability. Use when the user asks for "check data availability", "write data availability statement", "check whether data, code, and materials are reusable", or requests the rw-research-data workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Discover, verify, and document literature based on specific judgments, and adjust search directions according to evidence. Use when the user asks for "find relevant literature", "conduct literature discovery", "supplement sources for this argument", or requests the rw-literature-discovery workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Organize research, constructs, design, samples, results, biases, conflicts, and gaps into a traceable evidence map. Use when the user asks for "create an evidence map", "organize research conflicts", "connect literature", or requests the rw-evidence-map workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.