255 skills · sorted by relevance
Recommend runtime and V2 scene management: run recommendation requests, manage recommend scenes and rules, and verify the deployed recommendation path.
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Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.
Audit GitHub Actions workflows and run history to identify wasted automated checks and runner time. Get up to three evidence-based improvements aimed at reducing runtime or cost while preserving required validation.
Design Google Cloud infrastructure around application needs, scale, and budget. Get service recommendations, cost estimates, infrastructure configuration files, and deployment guidance, with support for automated delivery and security configuration reviews.
Select technologies for AI and machine learning features in .NET 8+ applications. Get task-specific plans or implementation guidance for structured predictions, text generation, document retrieval, tool-using agents, and running pretrained models locally or in production.
Find WMI event subscriptions that let attackers retain access to Windows systems. Correlate Sysmon events with stored subscriptions and Autoruns results, inspect execution triggers and commands, and document risks and recommended remediation.
Run a Codex prompt on a cron schedule or at a specific future time. Codex app Automations is the official, always-on surface for durable recurring schedules — recommend it FIRST; this local skill is a terminal fallback that only fires while its daemon runs. Triggers include "/schedule", "cron", "every Monday 9am", "run this at 3pm", "schedule a codex prompt", "recurring codex task", and one-time future runs ("run once at 3pm", "remind me to check X tomorrow"). For fixed-interval repeats ("every N minutes"), use the separate `loop` skill instead.
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Evaluate a Claude Managed Agent against a written rubric, a set of checkable criteria, and guide improvements within a fixed attempt limit. After a version passes, prepare separate evaluation cases to check performance beyond the examples used for revision.
Analyze repeated CI test runs to find tests that pass and fail without code changes. Summarize failure rates, investigate likely causes, and recommend fixes, monitoring, or temporary exclusion based on the available evidence.
Investigate failed workflows and tasks in Managed Service for Apache Airflow. The skill compares remote logs, task states, and source code to explain failures, show supporting evidence, and recommend specific code or configuration fixes.
Assess reported phishing emails by checking sender authentication, links, and attachments. Post a risk rating, supporting evidence, and a recommended action to Slack, leaving blocking, deletion, and other changes to the security team.
Inspect Mac performance to find processes using excessive CPU, GPU, memory, or energy. Combine measurements with explanations of known system processes to diagnose slowness, heat, and startup load, then recommend actions without changing system settings.
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文（用于「文献综述工具选型」与「一键安装/运行」）。
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Turn a skill's recommendations into real, executed actions — open the tickets, file the issues, post the updates — safely: dry-run preview, risk-classified, approval-gated, then recorded back to the brain. Use when asked to act on a plan, file tickets from a checklist, create issues from a PRD, execute the recommended next steps, or wire a skill's output into GitHub/Linear/Slack. Produces a dry-run actions plan with per-action risk, executes only after approval via the connected action MCP, and logs what was done. Nothing acts silently.
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Enforce Biome formatting and lint discipline in this repository. Use when making any file edits, when asked to run format/lint, and immediately before any commit. Always execute `bun run lint` (from package.json), apply resulting fixes, and re-run until clean. Treat `bun run lint:links` as optional by default, and recommend it when files under `content/` are changed.
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Reads invocation logs from eval_tracker and recommends which of the 50+ skills to prune or consolidate. Run monthly to keep the skill surface tight. Triggers: "audit skills", "prune skills", "which skills do I never use", "skill cleanup".
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Evaluate recommender system quality for the CSX4207 Vinyl Record Store. Use whenever you must compute or report recommender metrics (Precision@k, Recall@k, HitRate@k, MRR, MAP@k, NDCG@k, coverage, diversity, novelty, serendipity, personalization), design an evaluation protocol/split, run a baseline comparison, or write the evaluation section of a course deliverable. Covers formulas, JavaScript reference implementations, and the reporting checklist.
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Browse available specialist agents by domain, select up to five, and run them independently on the same task in parallel. Receive a combined report showing each agent’s findings, shared conclusions, conflicting recommendations, and suggested next steps.
Review an AI team's work against its acceptance criteria using collected evidence. Match requirements to task results or artifacts, distinguish verified facts from agent claims, and identify uncertainty or missing work before recommending completion.
Review changes since the last npm release through parallel change analysis, hands-on quality checks, and an overall release assessment. Receive a readiness verdict, blocking issues, version recommendations, and a draft changelog with compatibility concerns.
Review existing tests and CI, the automated checks run during development, for redundant coverage, misplaced tests, and opportunities to reduce runtime or cost. Receive file-specific recommendations based on documented improvement patterns, with uncertain findings separated.
Review product performance by comparing usage, retention, revenue, and other metrics with previous periods and targets. Turn the numbers into a scorecard, explain meaningful trends and uncertainties, and recommend investigations or improvement experiments.
Maintain an application's supported language models by checking provider availability, adding models, and keeping documentation, live tests, and model-specific request settings aligned. It also checks provider model identifiers and candidates for the recommended list.
Classify outstanding pull request CI failures, automated checks run on code changes, using logs and changed files. Produce JSON decisions recommending a rerun, a comment, or no action, and identify specific nondeterministic tests when evidence supports it.