Plugins
5 plugins@a5c-ai
Tasks
Route questions to domain experts instead of asking the current user
2 skills · plugin
curated
Task Execution Workflow
Load a plan, execute tasks with verification, and track progress via issues.
10 skills · plugin
@fradser
Pi
Bridges to pi (dev/pi), a minimal terminal coding harness. Delegates coding tasks to the pi CLI for execution with full file and git context.
3 skills · plugin
@samyakjhaveri
Business Process
Business process skills (process-optimizer, sop-writer, workflow-mapper, weekly-review). Useful for operational documentation, SOP generation, and workflow analysis. NOT for: software engineering tasks — these target organizational processes, not code.
4 skills · plugin
@samyakjhaveri
Helpers
Utility skills (decision-matrix, navigate, model-route, prompt-improver, grill-research, align-prompt). Useful for specialized one-off tasks like structured decisions, adversarial research grilling, or aligning a draft prompt to an Opus model. NOT for: daily development workflow — these are situational tools, not always-on skills.
4 skills · plugin
Results for “tasks”
150 skills
micsapp spec-kit-skill
GitHub Spec-Kit integration for constitution-based spec-driven development. 7-phase workflow (constitution, specify, clarify, plan, tasks, analyze, implement). Use when working with spec-kit CLI, .specify/ directories, or creating specifications with constitution-driven development. Triggered by "spec-kit", "speckit", "constitution", "specify", references to .specify/ directory, or spec-kit commands.
3 · bundle
chen-yu-hao scvi-tools
This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.
5 · bundle
schattenspiegel structlog-python
Use for writing, configuring, integrating, reviewing, debugging, or testing Python structured logging with structlog. Trigger for bound loggers, event dictionaries, processor chains, JSON or console rendering, standard-library logging integration, contextvars, request correlation, exception rendering, and structlog test capture. Do not use for stdlib-logging-only, Loguru-only, metrics-only, tracing-only, or collector configuration tasks that do not use structlog.
0 · bundle
modbender self-improvement
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
12 · bundle
solizardking magicblock
MagicBlock Ephemeral Rollups development patterns for Solana. Covers delegation/undelegation flows, dual-connection architecture (base layer + ER), cranks for scheduled tasks, VRF for verifiable randomness, magic actions for atomic ER-commit + base-layer follow-ups, private payments API (deposits, transfers, withdrawals, swaps, and challenge/login auth flow), commit sponsorship and fee vault wiring, lamports top-up for delegated accounts, and TypeScript/Anchor integration. Use for high-performance gaming, real-time apps, private transfers and swaps, and fast transaction throughput on Solana.
0 · bundle
chen-yu-hao pennylane
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
5 · bundle