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Found 2,779 Skills
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.
All-in-one content director that bundles FOUR format specialists — talking-to-camera, silent POV, dance, and stitch/duet — behind a single front door. Ingests the user's Instagram or TikTok handle, then in Stage 0 asks which KIND of trend they want to make (talking / POV / dance / duet, each explained), recommends a format from their profile when they're unsure, and can present a cross-format sampler menu (~10 real trend cards with links spanning all four formats) so the user picks one card. Once a format (and optionally a specific trend) is locked, it loads the matching format playbook from `formats/<format>.md` and runs that pipeline end-to-end. Triggers — "be my content director" (when the user wants to choose a format), "what kind of trend should I make", "talking vs pov vs dance vs duet", "show me trends across formats", "content director bundle", "content-director".
Salesforce Industries Common Core (OmniStudio/Vlocity) Apex callable generation and review skill with 120-point scoring. Use when creating, reviewing, or migrating Industries callable Apex implementations. TRIGGER when: user creates or reviews System.Callable classes, migrates VlocityOpenInterface or VlocityOpenInterface2, or builds Industries callable extensions used by OmniStudio, Integration Procedures, or DataRaptors. DO NOT TRIGGER when: generic Apex classes or triggers (use platform-apex-generate), building Integration Procedures (use omnistudio-integration-procedure-generate), authoring OmniScripts (use omnistudio-omniscript-generate), configuring Data Mappers (use omnistudio-datamapper-generate), or analyzing namespace/dependency issues (use omnistudio-dependencies-analyze).
Translates an image (or a set of image references — screenshots, mockups, Figma URLs, live websites) into two mirrored design-system artifacts: `docs/design.md` (YAML tokens + prose, following Google's open [design.md](https://github.com/google-labs-code/design.md) format, for the coding agent) and `docs/design.html` (a self-contained, token-driven style guide rendering every token and component live, for the human to read). Reads the imagery, asks targeted clarifying questions, derives the design tokens (colors, typography, spacing, rounded, components), and writes both files. Fully standalone — requires no other document or skill. Use when the founder says "create a design system", "design from image", "translate image to design", "create design.md", "image to design system", "extract design tokens", or shares an image with no other clear intent.
亚马逊店铺商品目录 Catalog（与 linkfox-amazon-store-auth / report / listings / pricing / orders / feeds 同系列），经 /spApi/developerProxy 调用 SP-API Catalog Items：v0 listCatalogCategories；v2022-04-01（默认）或 v2020-12-01 的 searchCatalogItems、getCatalogItem。当用户提到亚马逊目录、Catalog Items、listCatalogCategories、searchCatalogItems、getCatalogItem、按 ASIN 查目录、关键词搜商品目录、类目节点、includedData、summaries/images 时触发。
Transform research interests into definable, searchable, falsifiable, and executable research questions. Use when the user asks for "help me formulate research questions", "is this topic feasible", "turn my interest into an RQ", or requests the rw-research-question workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
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.