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Found 211 Skills
Use Twill Cloud Coding Agent to manage Twill's public v1 API workflows. Create/list/update tasks, stream and cancel jobs, manage scheduled tasks, list repositories, and export Claude teleport sessions.
Stay current with how OpenCode, OpenAI Codex, and Claude Code implement extensibility features (skills, slash commands, subagents, custom prompts). Use when comparing implementations across AI coding assistants, researching how a specific tool implements a feature, or syncing knowledge about agent extensibility patterns. Triggers include questions like "how does X implement skills?", "compare slash commands across tools", "what's the latest on Claude Code sub-agents?", or requests to understand agent extensibility approaches.
Makes SDK, API or protocol documentation something a coding agent can integrate from unattended - crawler access, machine-readable entry points (llms.txt, markdown endpoints, OpenAPI and JSON Schema files), self-contained copy-paste-safe pages, and a measured first-attempt agent success rate. Use whenever the user mentions agent-readable or AI-ready docs, agent experience or AX, llms.txt or llms-full.txt, docs for coding agents, agents inventing API calls that do not exist, or asks whether an agent could wire up their SDK from the docs alone - even if they never say "agent". Not for AI-search citation, nor a repo's own contributor-facing agent instructions. For human docs architecture use samber/developer-relations-skills@developer-docs-structure-audit.
Harvest coding-agent session transcripts already on disk (Claude Code, Codex, OpenCode, Cursor, Pi) and extract durable knowledge — topics, people, facts, events, quotes — into whatever persistent memory the agent can reach. Cursor-tracked, budgeted, read-only on sources. Use when asked to collect/import/mine session history into memory, build memory from past sessions, or as a scheduled task. Composes with memory-gardener, which tends what this skill plants.
build a repo-local skill and install a matching iterated coding-agent GitHub Actions workflow, prompt, memory file, and reference templates
Paired benchmark orchestration for comparing coding-agent performance with recursive-mode off and on. Use when the user wants to benchmark recursive-mode, compare recursive vs non-recursive execution on the same project, generate disposable benchmark repos, capture timing/build-test logs, or write a benchmark report.
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
Cheat sheet + workflow for launching interactive coding-agent CLIs (Claude Code, Gemini CLI, Codex CLI, Cursor CLI, and pi itself) via the interactive_shell overlay or headless dispatch. Use for TUI agents and long-running processes that need supervision, fire-and-forget delegation, or headless background execution. Regular bash commands should use the bash tool instead.
Rigorously evaluate an Agent Skill end-to-end across ANY coding-agent CLI — verify its scripts emit the documented numbers (deterministic checks), test whether its description triggers on the right prompts, and measure whether an agent following the SKILL.md beats a no-skill baseline (with/without pass-rate delta, mean ± stddev, benchmarked). Use whenever you need to test, benchmark, validate, grade, or quantify a skill's quality, check if a skill "actually works," compare two skill versions, optimize a skill's triggering, or set up an eval suite — even if the user just says "is this skill any good," "does my skill work," or "benchmark this skill." Drives Claude Code, OpenAI Codex, Antigravity (agy), Cursor, GitHub Copilot, Amp, opencode, or Grok in headless mode.
Report coding-agent progress, questions, decisions, blockers, tests, PRs, human replies, inbox instructions, and handoffs to the Agora coordination server. Use for every coding task when AGORA_URL is set, especially at session start, before risky or shared edits, when blocked, when asking for human or agent input, after running tests or verification, when opening or updating PRs, when polling for human replies or instructions, and before the final response.
Use when mining coding-agent session history, archived transcripts, memories, or repeated local work to discover recurring workflows that should become new Agent Skills.
Watch for the 11 known AI-coding-agent failure modes (fabrication, scope_creep, security_vulnerability, etc.) — consult this skill before edits, dependency adds, completion claims, or anything that could trip a known supervision concern. Quote the snake_case failure-mode ids verbatim when flagging risks.