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Found 40 Skills
Multi-agent pipeline orchestrator that plans and dispatches parallel development tasks to worktree agents. Reads project context, configures task directories with PRDs and jsonl context files, and launches isolated coding agents. Use when multiple independent features need parallel development, orchestrating worktree agents, or managing multi-agent coding pipelines.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
When the user wants to find blog keywords, do keyword research for SEO, or build a keyword list for content. Use when the user mentions "keyword research," "blog keywords," "find keywords," "what should I blog about," "keyword ideas," "long-tail keywords," "striking distance keywords," "keyword gap," "content gap analysis," "competitor keywords," "keyword difficulty," "search volume," "topic clusters," "pillar content keywords," "keyword list," or "what are people searching for." Outputs a ranked JSONL keyword list for downstream content creation. For writing content strategy, see content-strategy. For SEO audits, see seo-audit. For AI search optimization, see ai-seo.
Guides use of ProjectDiscovery Katana for web crawling and spidering in security testing and recon workflows. Covers installation, standard vs headless mode, scope and rate limits, JSONL output, and piping from httpx or URL lists. Use when the user mentions Katana, projectdiscovery/katana, web crawling, spidering, endpoint discovery, attack surface mapping, or chaining crawlers in automation pipelines.
Summarize Codex token usage from local Codex Desktop or CLI session JSONL logs. Use when the user asks to count, audit, total, compare, or report Codex/OpenAI token usage for a period such as today, this week, last month, a calendar month, a rolling 30-day window, peak week, peak day, input/output/cached/reasoning breakdown, or net token usage.
Use when debugging a Nemo Gym run or reward profiling job. Covers rollout collection failures, empty or partial JSONL outputs, stale materialized inputs, verifier/schema errors, Ray or Slurm issues, vLLM readiness, judge failures, tool/sandbox failures, cache problems, and throughput bottlenecks.
Analyze claude-trace JSONL files for session health, patterns, and actionable insights. Use when debugging session issues, understanding token usage, or identifying failure patterns.
Mine Gmail history into a local flat-file knowledge base (~/.cortex/). Use when asked to "run the cortex", "mine emails", "cortex run", "cortex dry run", "set up the cortex", "cortex from DATE", or "mine my inbox". Extracts contacts, clients, communications and knowledge facts into portable JSONL/JSON files. Requires gws CLI and ANTHROPIC_API_KEY.
Search and query your Knowledge Cortex (~/.cortex/). Use when asked to "cortex stats", "cortex search", "cortex client", "cortex contacts", "cortex export", "cortex prune", "search my knowledge base", or "what do I know about COMPANY". Queries portable JSONL/JSON files for contacts, clients, communications, and facts.
Generate a self-contained HTML viewer for any Claude Code session, including agent team sessions with full inter-agent DM timelines. Use whenever the user asks to "view a session", "visualize a conversation", "show me what happened in session X", "generate a session viewer", "replay a session", or references viewing/inspecting Claude Code JSONL logs. Also use when the user provides a session ID and wants to see the conversation.
Use when diagnosing unexpected behavior, failed workflows, bugs, browser or Node.js runtime issues, logs, traces, or when preparing a root-cause hypothesis. 诊断异常、定位 bug、判断修复方向时使用：先建立证据表，区分运行时事实和代码推断，避免多层猜测；证据不足时添加 copy-friendly 浏览器日志或本地 Node.js JSONL 日志。
Eight-axis judgment code review for the current diff — Correctness, Simplification, Tests, Documentation, Style, Intent, Design/API, Performance (+ Coherence on metadata changes). Five-phase pipeline scope → deterministic tool battery (npx/uvx-preferred, zero-install for the JS + Python majority) → 8 parallel LLM axis reviewers → Haiku validators on sub-80 findings (verbatim rubric, ≥80 threshold) → synthesis with no-silent-drop + Conventional Comments JSONL. Every report closes with "What I did NOT check" (security → /security-review, runtime perf, flaky detection). Opt-in flags `--verify-build`, `--mutation-test`, `--reconcile`, `--apply-safe`. Public-skill posture — zero auto-install, graceful skip on missing native tools.