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Found 469 Skills
Initialize a multi-agent swarm with anti-drift configuration
Document and type a Sentry API endpoint. Write or fix @extend_schema decorators, specify response TypedDicts, type request parameters, correct type drift between the declared schema and the runtime response, and validate the generated spec. Use when asked to "document an endpoint", "add OpenAPI docs", "add/fix @extend_schema", "type an endpoint response", "fix the response type", "fix type drift", "reuse a response type", "split an overloaded endpoint", "specify the response schema", "add a TypedDict response", "migrate a legacy api-docs path", "fix a parameter type", or "make an endpoint public" / "promote an endpoint" (promotion is one section here).
Run a complete repository-native, spec-anchored development loop inspired by DeepSeek Harness. Use for feature development, bug fixes, simplification, architecture, process, or testing work that changes behavior, contracts, structure, test strategy, durable formats, or decision rationale; when asked to write, review, continue, implement, reject, supersede, archive, or verify a spec/Agent Note/RFC/ADR; or when code, tests, current-state docs, and decision rationale must converge in one change. Also use for read-only spec-to-code drift checks. Do not create a spec for a purely mechanical local edit that changes none of those things.
Generate grounded-and-verified, engine-agnostic database documentation that reaches 100% parity with the real schema. Introspects the LIVE database as ground truth and cross-validates it against ORM models, migrations, generated types, seeds, and application queries, then proves completeness by diffing the docs back against the database. Produces ER diagrams (mermaid), per-table data dictionaries, and a machine-readable schema.json. Works with PostgreSQL, MySQL, SQL Server, and SQLite across any ORM (Prisma, TypeORM, Drizzle, Sequelize, Knex, Django, Rails) or raw SQL. Use when asked to document a database, produce an ERD or data dictionary, write db/schema docs, audit schema drift, or refresh existing DB docs.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for live container runtime analysis, mounted secrets, sidecars, namespaces, init containers, entrypoint drift, and route-to-container resolution. Use when the user asks why a live container differs from manifests, where a mounted secret is consumed, how a sidecar or init container changes runtime state, or which route resolves to which live container. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Definición del artefacto de producto: convierte una idea en BORRADOR en un contrato de producto completo — problema, requisitos con IDs estables, actores, flujos, ejemplos de aceptación, fronteras de alcance — como UN artefacto que madura in-place (nunca dos docs que driftan), con right-sizing (el doc crece con el contenido, no por plantilla) y confirmación de alcance ANTES de escribir (Declarado/Inferido/Fuera). Usar en la etapa de definición de wachi-producto, o suelta para escribir/enriquecer el contenido de una ficha funcional. Aterriza en RumIAndo: contenido versionado de la ficha + transición BORRADOR→PARA_VALIDAR. NO decide adopción de tecnología (wachi-validacion-interna) ni valida con usuarios (wachi-validacion-usuario).
Runs the deterministic build_runner codegen pass (drift_dev, freezed, json_serializable, riverpod_generator) as one pinned command with --delete-conflicting-outputs, always before flutter analyze, never hand-editing or force-committing generated *.g.dart / *.freezed.dart / *.drift.dart output, watch only in local dev. Use when regenerating codegen, fixing "missing part file" / "conflicting outputs" / undefined generated-class analyzer errors, after editing a drift table or DAO, a freezed value object, a riverpod Notifier or provider, or a json_serializable model, or after a fresh git clone, branch switch, or pull.
Diagnose whether the most important idea in an article, essay, post, memo, section, paragraph, or sentence appears where it best serves the reader. Use for BLUF checks, buried or misplaced ledes, slow or misleading openings, intro drift, competing main ideas, and requests to get to the point or find the real opening. Compare what the opening foregrounds with what the artifact actually delivers, judge whether any delay is earned, and recommend the smallest structural move. Diagnose by default; rewrite only when asked.
All-in-one skill for CLAUDE.md files. Two modes: `audit` finds drift (claimed facts no longer matching code), leaked secrets, duplicates, instruction-budget bloat, and prescriptive-vs-descriptive imbalance across all CLAUDE.md files in your projects. `improve` measures one CLAUDE.md against Anthropic's official best practices (200-line budget, removability test, emphasis tuning, 3-tier hierarchy) plus community-validated guidance, then proposes concrete rewrite diffs applied only after user approval. Default behavior auto-detects: in a project with a CLAUDE.md → improve mode; otherwise → audit all. Use when: 'audit claude.md', 'check claude md drift', 'lint CLAUDE.md', 'claude md audit', 'refresh instructions', 'improve CLAUDE.md', 'restructure CLAUDE.md', 'tune CLAUDE.md', 'apply CLAUDE.md best practices', 'is my CLAUDE.md good', 'CLAUDE.md is too long', 'rebalance CLAUDE.md', or quarterly as a hygiene check.
Use when a Scenario output must be checked and improved rather than accepted first roll: iterating a generation until it matches the brief, fixing a batch that came back off-brief, retrying failed shots methodically, wiring an automated generate, review, revise loop, or deciding whether to re-prompt, swap references, inpaint, post-process, or change model. Keywords: refine, iterate, critique, review loop, QA, self-correction, verify, acceptance criteria, retry, drift.
Make a 12-second looping milestone clip in code with Claude Code. Thousands of tiny points drift like a night sky, pull together into your real number (followers, subscribers, revenue, customers), a line draws underneath and the label settles in. Premium, not a party. Use when someone says "milestone video", "celebrate 10k followers", "animate my subscriber count", "number reveal", "particle number animation" or wants to mark a result on LinkedIn or Instagram.
Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error, debug my DAG, why did my workflow fail, DAG not showing up, MWAA import error, requirements failing, worker crashed, serverless run failed. Not applicable to authoring workflows (handled by authoring-mwaa-workflow), running or smoke-testing a workflow (handled by testing-mwaa-workflow), or CI-CD deploy failures.