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Found 172 Skills
Pragmatic qualitative analysis for interview data in sociology research. Guides you through systematic coding, interpretation, and synthesis with quality checkpoints. Supports theory-informed (Track A) or data-first (Track B) approaches.
Use this when you have a written implementation plan to execute in a separate session with review checkpoints.
Amend a published CLI from one of two input sources: (1) dogfood mode mines the active Claude Code session transcript for friction (missing flags, hand- rolled API payloads, silent-null returns); (2) direct-input mode accepts user-supplied asks (rename a command, add commands or feeds, fix a named bug, optionally sniff the source site for new endpoints). Confirms scope with the user, plans + executes the fix autonomously, scrubs PII, and opens a PR against mvanhorn/printing-press-library. Two user-in-loop checkpoints: scope after capture, PR draft before open. Trigger phrases: "amend the CLI", "submit a patch", "fix what I just dogfooded", "open a PR for this CLI", "patch this CLI", "add features to my CLI", "rename this command", "add these feeds to <cli>", "sniff for new APIs in <cli>", "amend with these ideas", "use printing-press-amend", "run printing-press-amend".
Load automatically when user asks to learn Medusa development (e.g., "teach me how to build with medusa", "guide me through medusa", "I want to learn medusa"). Interactive guided tutorial where Claude acts as a coding bootcamp instructor, teaching step-by-step with checkpoints and verification.
Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.
Manages session state and context handoffs for multi-session projects using the Session Handoff Protocol. Creates and maintains SESSION.md to track phase progress, git checkpoints, and next actions across context clears. Integrates with project-planning skill to convert IMPLEMENTATION_PHASES.md into trackable session state. Use when starting new projects after planning, resuming work after context clear, or managing complex multi-phase implementations. Keywords: session management, SESSION.md, session handoff protocol, context handoff, multi-session projects, phase tracking, git checkpoints, session state tracking, resume work, context clear, phase progress tracking, implementation phases, verification stage, debugging stage, next action tracking, work continuity, session recovery, context management, phased implementation tracking
Expert blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.
Locate the latest checkpoint for a run id and provide a follow-up play/eval command. Use when asked to find a checkpoint.
Use this when you have a written implementation plan and execute it in separate sessions with review checkpoints
This skill should be used for multi-session autonomous agent work requiring progress checkpointing, failure recovery, and task dependency management. Triggers on '/harness' command, or when a task involves many subtasks needing progress persistence, sleep/resume cycles across context windows, recovery from mid-task failures with partial state, or distributed work across multiple agent sessions. Synthesized from Anthropic and OpenAI engineering practices for long-running agents.
Use when a migration is already known to stay on the LangGraph orchestration side, including stages, routing, checkpoints, interrupts, persistence, streaming, and subgraph boundaries.
Eino orchestration with Graph, Chain, and Workflow. Use when a user needs to build multi-step pipelines, compose components into executable graphs, handle streaming between nodes, use branching or parallel execution, manage state with checkpoints, or understand the Runnable abstraction. Covers Graph (directed graph with cycles), Chain (linear sequential), and Workflow (DAG with field mapping).