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Found 2,940 Skills
Canonical built-in iii trigger config and call payload shapes. Use when generating or validating HTTP, cron, queue, pubsub, state, stream, or log trigger registrations and handler input types.
This skill should be used when the user asks to "create an agent", "make an agent", "write an agent", "build a subagent", "add an agent to a plugin", "design an autonomous agent", "generate an agent file", "write a system prompt for an agent", "what frontmatter does an agent need", "create a specialized agent". Not for skills or commands — use create-skill.
Enforces vendor-neutral UTM naming conventions by validating marketing links and generating a normalized, policy-compliant output.
Nature figure preparation: resolution (300+ DPI), formats (AI/EPS/TIFF), RGB color, Helvetica/Arial fonts, lowercase panel labels, image integrity requirements.
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.**
Verify citations and references in scientific documents to detect hallucinated or invalid sources. Extracts DOIs, URLs, arXiv IDs, PubMed IDs, and ISBNs from Markdown, LaTeX, org-mode, and plain text, then validates them using API lookups and web fetches. Use this skill when: - Reviewing AI-generated content for citation accuracy - Validating references in papers, reports, or documentation - Checking if DOIs/URLs resolve to actual papers - Auditing a document for broken or fake citations
Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Use for model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken deployments.
Opinionated guide for building production TypeScript applications with Effect v4. Use when implementing Effect workflows, services, layers, schemas, configuration, schedules, caches, streams, HTTP clients, or tests.
Validate an implemented React Doctor rule before merge. Use after focused tests pass to review detector correctness, inspect open-source hits, run pull request parity, add regression coverage, prepare a changeset, write pull request copy, or address review findings.
Start implementation from Intent. Validates Intent completeness, then either delegates to TaskSwarm (if available) or executes TDD phases directly. Use when you have an Intent ready and want to start building.
Find and fix broken or insecure links across an entire site, including CMS content, to improve SEO and user experience. Audits HTTP/HTTPS issues and validates all internal and external links.
Validate skill directories against AgentSkills spec