Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Generate a believable, lived-in ALIVE world from a free-text persona description (custom path) or a deterministic sandbox preset. Routes the create/list/activate/deactivate/delete/status surface and orchestrates the 5-stage subagent generation pipeline.
Load an ALIVE walnut -- read kernel files, show current state, surface one observation, ask what to work on
Merge multiple sessions into one working context and detect conflicts between parallel sessions. Dispatches subagent swarm to read files touched, extract log history, and resolve contradictory state. Use when resuming after days away or when parallel sessions may have written conflicting decisions. For browsing or reviving individual sessions, use alive:session-history instead.
Present pending stash items grouped by destination walnut for approval
Use to orchestrate a team-based ATDD workflow — six phases (spec writing, spec review, pipeline generation, implementation, refine, verify & harden) each handled by a fresh agent so no role erodes across a long-running feature. Triggers — "build a feature with a team", "use ATDD with agents", "create an ATDD team", "orchestrate agents for ATDD", "coordinate agents for feature development", "add ATDD roles to my team", "add spec-writer and reviewer to the team".
Analyze agent-user interaction transcripts to identify context network maintenance needs and guidance improvements. Use after significant agent interactions or to improve context networks.
Self-evaluate the current agent on LongDS-Bench (zjunlp/DataMind): the long-horizon, multi-turn agentic data-analysis benchmark. Use this when the user asks to run, score, or benchmark an agent on LongDS / LongDS-Bench / DataMind longds, or to measure multi-turn data-analysis ability. This does NOT use DSGym's Docker runtime — the agent running this skill IS the agent under test: it reads a locally-prepared dataset, performs the multi-turn analysis with its own tools, and is scored by the official LLM-judge rule. The ~19.5 GB dataset must be downloaded and prepared by the operator beforehand (see `$SKILL_DIR/../README`); this skill does not download it. Heavyweight and long-running; run in the background if supported and confirm scope first.
Persistent local memory for AI agents. Save, recall, and search project decisions as local JSON. Zero cloud, zero infrastructure.
Refresh RepoPrompt tool guidance when the CLI/MCP surface changes. Tracks RepoPrompt CE (`rpce-cli`, the maintained target) across versions, and can diff the frozen Classic CLI (`rp-cli`) against CE. Uses `~/.pi/agent/skills/repoprompt-tool-guidance-refresh/scripts/track-rp-version.sh` to capture/diff `--help` and `-l` (tool definitions) under `~/.pi/agent/skills/repoprompt-tool-guidance-refresh/rp-tool-defs/`.
Extract reusable knowledge from the current session into a persistent skill. Use when you discover something non-obvious, create a workaround, or develop a multi-step workflow that future sessions would benefit from.