「query run」の検索結果

This skill should be used when the user asks to "process beta reader feedback", "alpha reader feedback", "feedback round", "synthesize reader feedback", "reader notes", "beta feedback", "reader readiness check", "review copy", "send the draft to readers", "share with readers who don't use GitHub", "triage the reader panel", or wants to collect, reconcile, and act on external reader feedback for a story project. NOT for running the simulated reader panel itself (use reader-panel), rounds with a professional editor (use editorial-review), or for checking a manuscript is ready to query or publish (use submission or publishing).

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Designs the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.

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Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.

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Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.

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Real-world Active Directory environment constraints that silently break attacks when ignored: NTLM disabled (Kerberos fallback), AES-only KDCs (RC4 blocked by GPO), LDAP signing and channel binding requiring LDAPS on 636, Kerberos clock skew (KRB_AP_ERR_SKEW), SPNs that must be FQDNs never short names or IPs (the misleading invalidCredentials), Protected Users / LAPS / gMSA / MachineAccountQuota, LDAP 1000-object paging, and VPN latency. Use this whenever a bind, TGT request, LDAP query, or SMB negotiation fails in a way that looks like bad credentials but is really hardening, or before running any AD tooling against a domain you have not fingerprinted. Standard-tooling knowledge (netexec, impacket, certipy, bloodyAD, kerbrute), no vendor-specific engine.

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Internal — for Boundless team members only. Monitor Boundless deployment pipelines (AWS CodePipeline + CodeBuild) on the ops account. Use when the user wants to track a deployment after merging a PR, check whether a commit has rolled out to staging/prod, diagnose a failed deployment, watch the status of a specific pipeline, or get prompted to approve a production rollout once staging succeeds. Do NOT use for service runtime debugging (use ops-logs-query) or for deploying dev infrastructure (use ops-infra-deploy).

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Redpanda's Agentic Data Plane: governance infrastructure for building, running, and governing AI agents and MCP servers, plus a proxying AI Gateway for LLM providers, operated via `rpk ai` and the ADP API. Use when creating or managing AI agents (managed or self-managed) via `rpk ai agent` or `AgentRegistryService`; configuring MCP servers (remote or managed catalog, code mode, auth); setting up LLM providers or querying models via `rpk ai llm`/`rpk ai model` or the AI Gateway proxy; or configuring budgets, guardrails, or Cedar access-control policies through the governance APIs. Also covers reading agent transcripts and spending insights, and wiring OAuth clients or providers to the aigw Authorization Server. For the separate rpk cloud mcp control-plane MCP server, see `/redpanda:rpk-cloud`.

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DuckDB is an in-process analytical database that runs embedded inside your application with zero external dependencies. It can query CSV, Parquet, and JSON files directly without loading them into tables first, making it ideal for local data exploration, ETL pipelines, and analytical workloads where spinning up a server is overkill.

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Builds a portable, embedding-free knowledgebase from a set of files and delivers it as a self-contained `.skill` bundle (BM25 index + bundled searcher + query protocol). Use when a user wants to turn uploaded files, a folder, or a corpus into a searchable knowledgebase they can hand to any agent — phrased as "make a knowledgebase", "build a KB skill", "package these docs for retrieval", "create a searchable bundle", or references to a `.skill` KB. The output runs anywhere with Node or Python — no model, no install, no network. Distinct from `bm25` (ephemeral in-session search) and `building-github-index` (markdown project-knowledge index).

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Ranked content search over a text corpus you point it at, using BM25 (via xhluca/bm25s). Corpus-agnostic: cloned repos, project knowledge stores, uploaded files and archives, any local directory. In-memory BM25 index per invocation, with a session-local disk cache for repeat runs against the same corpus. Use for "rank these documents", "search this corpus", "which files are most about X", "find content about Y", or any multi-word concept query against a known body of text where grep would return everything or nothing. Needs a corpus on disk. Not for searching stored memories or prior-session decisions (remembering), not for a named symbol or a file's structure (tree-sitting), and not for a literal string you could grep.

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Bioactivity and small-molecule data-side catalog. Covers ChEMBL search/lookup, MolTrack compound and batch registration, HitTriage / HitDesign / PeptiHit workflows, and Curves dose-response analysis (IC50/EC50 fitting). Open this skill when the user asks about querying ChEMBL, registering or looking up compounds in MolTrack, running a hit-triage campaign, or fitting/inspecting dose-response curves.

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Google Cloud Platform CLI - manage GCP resources. Use when working with Compute Engine, Cloud Run, GKE, Cloud Functions, Storage, BigQuery, or other GCP services.

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Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.

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Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).

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Run SQL against Snowflake — submit statements, poll async handles, fetch result partitions, cancel, and browse warehouses/databases/schemas/tables. Use this whenever the user wants to query Snowflake, ask "what tables are in this schema", check a warehouse's status, or mentions `snowflakecomputing.com`, `/api/v2/statements`, or a Snowflake account identifier (like `xy12345.us-east-1`). Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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Investigate an alert, page, or production symptom in an incident channel or a team's oncall / monitoring channel, and report findings a human can verify. Ordinary conversation and chatter are not alerts; leave those alone. Use when an alert or page lands, when error rate, latency or saturation is up, when someone asks "why is X broken", "investigate", "what changed", "root cause this", pastes a monitor, dashboard, trace or error-tracker link, or reports production trouble; also by default when an alert lands in a covered channel unasked. Feed-level asks land here too ("which alerts matter", "triage today's alerts"), plus the scheduled alert-review routine and the ticket path ("customer X can't check out"). Runs a fast first pass posted as a short interim update with a TL;DR, every finding carrying the query or link to verify it, then proposes the fix and — with connector access and confirmation — carries it out, never unattended. Afterwards `incident-postmortem` writes it up.

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Work with a Grafana instance — search and read dashboards, run datasource queries (Prometheus, Loki, PostgreSQL, etc.), inspect alert rules and silences, post annotations, and manage folders. Use this whenever the user mentions a Grafana dashboard, panel, or alert; pastes a Grafana URL; asks "what does this dashboard show", "query this metric in Grafana", "is this alert firing", "silence this alert", or wants to create/export a dashboard — even if they don't say "API". Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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选择和实现状态管理方案时使用。覆盖 React (Redux Toolkit / Zustand / Jotai / Valtio / TanStack Query) / Vue (Pinia) / Angular (NgRx / Signals) / Svelte (Stores / Runes)。融合客户端状态 + 服务端状态分离。

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Uses AWS SDK CloudWatchClient GetMetricData and CloudWatch Logs Insights StartQueryExecution to automate incident triage. Correlates alarms via DescribeAlarms with X-Ray trace segments for root cause analysis.

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Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.

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Onboarding workflow for cross-library KB query in a consulting practice. Discovers existing user-scope library registrations, prompts for additions, validates each library, helps activate the relevant ones for the current project, runs a smoke test, and reports a clear "ready" or "issues to fix" verdict. Use --verify-only to skip prompting and just validate existing setup (sub-task 23 of

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Process unhandled Slack messages as Claudicle with persistent memory. Loads user models and soul state, posts terminal-styled thinking messages, generates cognitive-step responses, updates memory based on mentalQuery decisions. Requires Session Bridge listener running.

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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.

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Log a bias-log / vipassana observation to the memex store the moment a reasoning pattern surfaces, mid-session or at close. Use when you catch a bias in your own reasoning (anchoring, confirmation, trust-the-summary), when a pace tell or verification gap appears, or when meditate/observe produces a reflection worth persisting across sessions. Primary path pipes the body to `memex add` over stdin (`--title` required) then runs the `meditate-vipassana` extractor to make it queryable; secondary path appends a numbered bullet to `docs/OBSERVATIONS.md`.

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。