「collection」の検索結果

Validates Ansible playbooks using ansible-lint and the Ansible Galaxy API. Performs check-mode dry runs, validates role dependencies, and detects deprecated module usage across collections.

日本語の概要は準備中です。原文の説明を表示しています。

Generates and validates Ansible playbooks from infrastructure requirements. Uses ansible-lint for validation and queries Ansible Galaxy API for discovering certified roles and collections.

日本語の概要は準備中です。原文の説明を表示しています。

Runs Ansible diagnostic playbooks using ansible-runner and the Ansible Collections ecosystem (ansible.builtin, community.general). Captures system health, service status, and log analysis across inventory hosts.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnoses Ansible playbook failures using ansible-playbook --check --diff mode, ansible-lint, and the Ansible callback plugin API. Parses task execution results and suggests fixes for common module errors in ansible.builtin and community collections.

日本語の概要は準備中です。原文の説明を表示しています。

OS post-exploitation after a stable host foothold: quiet host recon, host-native persistence (Windows Run/tasks/services/COM/WMI and Linux SSH/cron/systemd/SUID), long-term C2 (Sliver primary, Mythic/Havoc optional), targeted collection and exfil, and engagement cleanup. Does not own AD/cloud/K8s native persistence, Ligolo pivoting, LSASS dumping, or EDR bypass. Operator selects objectives; success is a live callback, verified persistence, delivered loot, or restored host — not generating an unused implant.

日本語の概要は準備中です。原文の説明を表示しています。

Astro framework patterns and best practices for content-focused sites. Trigger: When working with .astro files, content collections, or Astro routing.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user says 'plan a campaign', 'promote this product', 'write the copy for our sale', 'what should we push this month', or 'draft the launch for this collection'. Reads what the store actually sells, drafts the campaign around it, and stages every page and product change for approval.

日本語の概要は準備中です。原文の説明を表示しています。

Rates any SKILL.md on a 0-100 scale across 10 dimensions with a SHIP / REWORK / SCRAP verdict. Evaluates trigger precision, instruction clarity, output predictability, edge case coverage, anti-hallucination guardrails, developer experience, composability, open-source readiness, wow factor, and real-world utility. No flattery — calibrated against Anthropic's own skill-creator rubric. Use this skill whenever someone says "score this skill", "rate my skill", "is this skill good", "skill review", "skill audit", "roast my SKILL.md", "grade this", "will this skill work", "evaluate my skill", "how good is this", or pastes a SKILL.md and asks for feedback. Also trigger when comparing two skills, benchmarking a skill collection, or asking "what's wrong with this skill" — even without the word "score".

日本語の概要は準備中です。原文の説明を表示しています。

Operate Milvus vector database with pymilvus Python SDK. Use when the user wants to connect to Milvus, create collections, insert vectors, perform similarity search, hybrid search, full-text search, manage indexes, partitions, databases, or RBAC via Python code.

日本語の概要は準備中です。原文の説明を表示しています。

Astro framework expert including components, content collections, and integrations

日本語の概要は準備中です。原文の説明を表示しています。

Enable, disable, and manage Claude Code skills. Use when the user asks to enable/disable/toggle/manage skills, check skill status, list skills, manage skill collections, or clean up the skills directory.

日本語の概要は準備中です。原文の説明を表示しています。

Direct a claim-driven research experiment project from post-Idea intake through method realization, prioritized evidence collection, Human Gates, and Evidence Freeze. Use for scientific planning, state transitions, queues, budgets, or recovery; not for running code or independently auditing results.

日本語の概要は準備中です。原文の説明を表示しています。

Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.

日本語の概要は準備中です。原文の説明を表示しています。

Writes and reviews Swift App Intents code that exposes app actions and data to Siri, Shortcuts, Spotlight, widgets, Control Center, and Apple Intelligence. Use when adding AppIntent, AppEntity, OpenIntent, AppShortcutsProvider, EntityQuery, Focus Filters, AssistantEntity/AssistantIntent schemas, on-screen awareness, LongRunningIntent/CancellableIntent, EntityCollection, SyncableEntity, AppIntentsTesting, or when wiring SwiftData/networked data into intents.

日本語の概要は準備中です。原文の説明を表示しています。

Preserve and conserve library and archival materials. Covers environmental controls (temperature, humidity, light), handling procedures, book repair techniques (torn pages, loose spines, foxing), acid-free storage, digitization for preservation, and disaster recovery planning. Use when establishing preservation practices for a new or existing collection, when materials show signs of deterioration, when setting up environmental controls for storage, when planning digitization to preserve fragile originals, or when creating a disaster recovery plan for a library or archive.

日本語の概要は準備中です。原文の説明を表示しています。

Grade a trading card using PSA, BGS, or CGC standards. Covers observation-first assessment (adapted from meditate's unbiased observation), centering measurement, surface analysis, edge and corner evaluation, and final grade assignment with confidence interval. Supports Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when evaluating a card before professional grading submission, pre-screening a collection for high-grade candidates, settling condition disputes between buyers and sellers, or estimating the grade-dependent value spread for a card.

日本語の概要は準備中です。原文の説明を表示しています。

Evolve SKILL.md files from agent execution traces using a three-stage pipeline: trajectory collection from observed runs, parallel multi-agent patch proposal for error and success analysis, and conflict-free consolidation of overlapping edits via prevalence-weighting. Based on the Trace2Skill methodology.

日本語の概要は準備中です。原文の説明を表示しています。

Conduct a GxP audit of computerized systems and processes. Covers audit planning, opening meetings, evidence collection, finding classification (critical/major/minor), CAPA generation, closing meetings, report writing, and follow-up verification. Use for scheduled internal audits, supplier qualification audits, pre-inspection readiness assessments, for-cause audits triggered by deviations or data integrity concerns, or periodic compliance posture reviews of validated systems.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

日本語の概要は準備中です。原文の説明を表示しています。

Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per X.

日本語の概要は準備中です。原文の説明を表示しています。

Handles NoSQL document operations and collection hierarchy exploration. Use for CRUD tasks and data retrieval. Provides flexible document manipulation and structured querying.

日本語の概要は準備中です。原文の説明を表示しています。

SurrealDB Surrealism WASM extension development. Write Rust functions, compile to WASM, deploy as database modules. Part of the surreal-skills collection.

日本語の概要は準備中です。原文の説明を表示しています。

SurrealDB MCP for AI agents: built-in server (SurrealDB 3.1+ `surreal mcp`) and standalone surrealmcp (surrealdb/surrealmcp v0.4.0). Part of the surreal-skills collection.

日本語の概要は準備中です。原文の説明を表示しています。

SurrealFS virtual filesystem for AI agents. Rust core + Python agent (Pydantic AI). Persistent file operations backed by SurrealDB. Part of the surreal-skills collection.

日本語の概要は準備中です。原文の説明を表示しています。