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Found 13,655 Skills
Trace agent execution by collecting spans and building a trace tree for a task
Apply a simple code transform via agent-booster's WASM engine — sub-millisecond, deterministic, $0 (no LLM call). Companion to cost-booster-route.
Local-first AI design tool that turns coding agents into design engines with 31 skills, 129 design systems, and multi-format export
Master 20 SEO & GEO skills for Claude Code agents - keyword research, content optimization, technical audits, rank tracking with CORE-EEAT and CITE frameworks
Create, audit, or consolidate agent skills following the Agent Skills open standard (agentskills.io). Interviews the user relentlessly about intent, scope, and edge cases before drafting. Covers SKILL.md structure, frontmatter, progressive disclosure, description optimization, script bundling, sub-command architecture, setup gates, context systems, and review. Use when the user wants to create a skill, write a skill, build a new skill, make a skill, draft a SKILL.md, or mentions "skill-maker". Also use when asked to review a skill, audit a SKILL.md, check why a skill never triggers, improve an existing skill, or fix a skill. Also use when asked to package expertise, workflows, or domain knowledge into a reusable skill. Also use when asked to consolidate skills, merge skills, combine skills, reduce skill count, or refactor multiple skills into one.
Run multiple AI coding agent sessions in parallel using git worktrees — each agent isolated in its own worktree, working on a separate branch. Use this skill whenever the user wants to: run two or more AI agents simultaneously on different features or bugs, set up isolated agent workspaces in the same repo, push parallel branches to GitHub and open/update PRs, coordinate between concurrent agent sessions, or clean up after merging. Triggers on: "parallel agents", "multiple agent sessions", "git worktree", "run agents in parallel", "work on two things at once", "isolated agent workspace", "spin up another agent", or any request involving simultaneous AI-assisted development streams.
Manus-style context engineering for Agent Teams. Coordinate multiple Claude Code instances with shared planning files. Use when complex tasks need parallel work (code review, debugging, feature development). Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1.
Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness — currently Code Mode, which collapses many tool calls into one sandboxed Python execution. Use when the user mentions pydantic-ai-harness, CodeMode, Monty, code mode, or tool sandboxing, when they want an agent to run agent-written Python, or when a Pydantic AI agent would benefit from orchestrating multiple tool calls in a single sandboxed script.
Install a per-turn canary signal (e.g. starting every reply with the user's name and a turn counter) so silent context degradation becomes visible the moment it happens, and run a recovery protocol when the canary trips. Use when the user mentions a "canary", "context canary", or "canary check", asks to detect context rot / compaction / drift, says "you stopped using my name" or "did you lose context", asks "how degraded is your context", or wants an early-warning system for long agent sessions.
Break down a one-sentence idea into a task plan that an AI agent can execute independently. Use this when the user says: "Help me write a goal for the agent", "Help me break down this goal in detail", "Write a task brief for the agent", "Write a goal prompt", "Let the agent run this project on its own", "Split the work among multiple agents for parallel execution". First conduct actual tests in the codebase, conduct online research if necessary, then ask a maximum of 5 questions in one go, and produce a task plan of ≤4000 characters that can be directly pasted into /goal to run, including actual test data, whitelist boundaries, anti-cheating acceptance criteria, and resumable progress. Automatically distinguish between execution-type and exploration-type (research/selection/solution-finding) tasks.
Extracts learnings from execution trajectories at the end of a Mantis loop. Use to parse agent conversations, extract successes, failures, and false assumptions, and append them to workspace/learnings.jsonl. Don't use for analyzing source code or writing patches.
Use this skill when the user wants to check AI agent logs, automation execution logs, org-level usage stats, AI credit consumption, or export automation job history. Covers 11 MCP tools.