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Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.
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Internal — for Boundless team members only. Query Boundless broker telemetry tables on Redshift for prod/staging operational data. Use when the user asks about broker health, request evaluations, request completions, proving times, skip rates, telemetry data, or wants to run SQL against the telemetry database on live networks. Also covers historical telemetry through 2026-04-24 stored as Parquet archives in S3 (queried via DuckDB). Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.
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Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.
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ALWAYS activate when the user's query involves Docker in any way — even if it also matches other skills. If the words docker, Dockerfile, docker-compose, compose.yml, container, or image appear in the query, this skill MUST be used. Covers: writing or editing Dockerfiles and compose files, adding services (postgres, redis, etc.) to compose, volume mounts and data persistence, docker build failures (layer caching, npm install issues), healthchecks and service startup ordering (depends_on), environment variables in containers, port mapping, container crashes and exit codes (OOM/137), non-root users, multi-stage builds, image optimization, .dockerignore, and deploying to container runtimes. Takes priority over general implementation or debugging skills when Docker infrastructure is the subject.
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Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, semantic or structural nearest-neighbor lookup, or hybrid search (vector + fulltext, multiple vector sources, or graph-derived scores). Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext-only/keyword-only search — use neo4j-cypher-skill. Does NOT compute GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.
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Database workflow specialist. Generates migration files with rollback scripts, detects breaking schema changes, and validates query parameterization.
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Query Codex banked reset credits from the local Codex Desktop login state and report available reset count plus expiration times. Use when the user asks to check Codex reset credits, reset opportunities, reset chances, reset count, rate-limit reset credits, or expiration times; also use when the user provides a codex-reset-credits-skill or codex-reset-credits runbook link and asks Codex to query the result directly.
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Query, read, create, update, and describe Salesforce records — Accounts, Contacts, Opportunities, Leads, Cases, and custom objects. Use this whenever the user wants to look up a Salesforce record, run a SOQL query, update an Opportunity, check an object's fields, or asks "what's in Salesforce" — even if they don't say "API". Also use it for any URL under *.salesforce.com / *.lightning.force.com or a mention of a Salesforce record ID or SOQL. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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Run SQL against Amazon Redshift — submit statements, poll status, page through results, and browse databases/schemas/tables. Use this whenever the user wants to query Redshift (provisioned cluster or Serverless), ask "what tables are in this schema", check a query's status, or mentions `redshift-data`, a Redshift cluster identifier / workgroup name, or a `redshift-data.{region}.amazonaws.com` endpoint. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.
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VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db / dispatch_tracker.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.
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Query the ChEMBL database web services for measured bioactivity data, compound records and calculated properties, targets, assays, mechanisms of action, drug indications and warnings. Use this skill to build curated SAR or QSAR datasets for a target, look compounds up by SMILES, InChIKey, name, or ChEMBL id, run similarity and substructure searches, and check what chemistry is already known against a protein. Also trigger when a query mentions ChEMBL ids (CHEMBL...), pChEMBL values, IC50/Ki/Kd/EC50 retrieval, assay confidence scores, or ebi.ac.uk/chembl.
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How to use the Appwrite CLI well. Covers when to use appwrite.config.json vs `appwrite client`, pull/push vs one-shot service calls, type-safe SDK and model generation, query flags, CI auth, Cloud regional endpoints, function/site variables, and traps that `--help` will not mention. Does not catalog commands — run `appwrite <command> --help` for flags. Use whenever running `appwrite`, deploying or pulling functions/sites/tables, generating application code from a table schema, initializing or linking a project, scripting Appwrite, or when the user mentions the Appwrite CLI, appwrite.config.json, `appwrite generate`, `appwrite types`, or `appwrite client`.
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Multi-backend observability CLI for querying metrics, logs, and traces across 12 backends. This skill should be used when the user runs obz commands, constructs metric/log/trace queries, configures providers, parses obz JSON output, or troubleshoots obz errors. Covers all core commands (metric query/list/info/labels/label-values/series, log search, trace search/get), global flags, time formats, output modes, config files, and exit codes.
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Run SQL queries against psql, BigQuery, or MySQL from the terminal, including natural-language-to-SQL and schema exploration. Use when analyzing data, inspecting DB state, or debugging tables. Trigger on "query the database", "SQL", "show me data from", "explore table".
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Retrieve, query, and identify relevant Google Cloud Monitoring metric descriptors for a GCP service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.
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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).
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Query a user's Gemini Notebook (formerly NotebookLM) for source-grounded answers through a logged-in browser session on their Google account. Uses the account's usage limits and writes browser state. Runs only on an explicit request: /blog notebooklm, "ask my Gemini Notebook", "ask my NotebookLM notebook", or "query my notebook library". Also used by blog-write when the user named a notebook for research. Not for generic research or note-taking.
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How to use the Appwrite CLI well. Covers when to use appwrite.config.json vs `appwrite client`, pull/push vs one-shot service calls, type-safe SDK and model generation, query flags, CI auth, Cloud regional endpoints, function/site variables, and traps that `--help` will not mention. Does not catalog commands — run `appwrite <command> --help` for flags. Use whenever running `appwrite`, deploying or pulling functions/sites/tables, generating application code from a table schema, initializing or linking a project, scripting Appwrite, or when the user mentions the Appwrite CLI, appwrite.config.json, `appwrite generate`, `appwrite types`, or `appwrite client`.
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Run natural-language data query workflows for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads reports.
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Preserve output from expensive one-shot commands for monitoring and later analysis. Use when rerunning a build, API query, CI inspection, or diagnostic would be costly and a managed background process is not the better interface.
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Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.
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Run analytics queries against any database using plain English — BigQuery (bq CLI), PostgreSQL, MySQL, SQLite, or any DB with a CLI/MCP/API. Use when you need to pull metrics, analyze data, or answer business questions without writing SQL.
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How to use the Appwrite CLI well. Covers when to use appwrite.config.json vs `appwrite client`, pull/push vs one-shot service calls, type-safe SDK and model generation, query flags, CI auth, Cloud regional endpoints, function/site variables, and traps that `--help` will not mention. Does not catalog commands — run `appwrite <command> --help` for flags. Use whenever running `appwrite`, deploying or pulling functions/sites/tables, generating application code from a table schema, initializing or linking a project, scripting Appwrite, or when the user mentions the Appwrite CLI, appwrite.config.json, `appwrite generate`, `appwrite types`, or `appwrite client`.
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