By athola
Write, test, profile, and publish Python code with async patterns, pytest, ruff, and performance tools. Agents handle code review, bottleneck analysis, and packaging to PyPI.
Analyzes Python test suites for quality, coverage, and improvement opportunities.
Analyzes Python async code for correctness, patterns, and potential issues.
Profiles Python code for performance bottlenecks using cProfile, memory_profiler, or py-spy.
Enforce strict ruff rules without per-file-ignores bypasses. Use for lint errors or code quality reviews.
Python profiling, bottleneck identification, and algorithm optimization. Use when code is slow.
Python 3.9+ expert (uv, ruff, pydantic, FastAPI). Use PROACTIVELY for Python development or optimization.
Expert Python testing agent specializing in pytest, TDD workflows, mocking strategies, and thorough test coverage.
Async Python patterns via asyncio and aiohttp for I/O-bound concurrency. Use when adding async APIs, handling concurrent I/O, or debugging async code.
Python package creation and PyPI distribution via pyproject.toml and entry points. Use when publishing a package or setting up build configuration.
Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.
Python testing patterns with pytest, fixtures, TDD, mocking, async and integration tests. Use when writing or auditing a Python test suite.
Uses power tools
Uses Bash, Write, or Edit tools
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A plugin marketplace for Claude Code. Install only the plugins you need to run git workflows, code review, spec-driven development, and autonomous agents from inside your Claude Code session.
Requires Claude Code 2.1.16+ and Python 3.9+ for hooks.
# Add the marketplace, then install the plugins you want
/plugin marketplace add athola/claude-night-market
/plugin install sanctum@claude-night-market # Git workflows
/plugin install pensive@claude-night-market # Code review
/plugin install spec-kit@claude-night-market # Spec-driven dev
Run claude --init once after installing. Prefer one command?
npx skills add athola/claude-night-market installs everything;
opkg i gh@athola/claude-night-market --plugins sanctum,pensive
installs a subset. Full options are in the
Installation Guide.
If the
Skilltool is unavailable, read skill files directly atplugins/{plugin}/skills/{skill-name}/SKILL.md.
Night Market is built around the loop you already work in. A typical feature runs end to end on a handful of commands:
/attune:mission routes you through
brainstorm, specify, plan, and execute phases.imbue enforces a failing test first,
so implementation follows the test, not the other way around./full-review runs a
multi-discipline pass; /refine-code cleans up duplication
and dead code./prepare-pr runs quality gates and leaves a
clean git state ready for a pull request./catchup rebuilds context
from recent git history after a break.The commands you reach for most:
| Task | Command |
|---|---|
| Run the project lifecycle | /attune:mission |
| Initialize a new project | /attune:arch-init |
| Review a PR | /full-review |
| Address review feedback | /fix-pr |
| Implement an issue | /do-issue |
| Prepare a pull request | /prepare-pr |
| Write a spec | /speckit-specify |
| Catch up on changes | /catchup |
| Package project knowledge as skills | /attune:skill-library |
| Clean up the codebase | /unbloat |
| Pressure-test a decision | /attune:war-room |
Full task-by-task walkthroughs are in the [Common Workflows Guide][workflows].
23 plugins in four layers. Each installs independently, and dependencies pull their shared runtime automatically.
Foundation is the base every other layer builds on:
leyline (auth, quotas, error patterns, trust verification),
sanctum (git, commits, PR prep, sessions), and imbue
(TDD enforcement, proof-of-work, scope guarding).
Utility handles cross-cutting concerns: conserve (context
and token optimization), conjure (delegation to Gemini and
Qwen), hookify (a behavioral rules engine with a security
catalog), egregore (autonomous agent orchestration),
herald (notifications), and oracle (local ML inference).
Domain is where the day-to-day work happens: pensive (code
and architecture review), attune (project lifecycle), spec-kit
(spec-driven development), parseltongue (Python), minister
(GitHub issues and DORA metrics), memory-palace (knowledge
organization), archetypes (architecture paradigms), gauntlet
(codebase learning), phantom (computer use), scribe
(documentation and slop detection), scry (recordings), tome
(research), and cartograph (codebase visualization).
Meta improves the system itself: abstract (skill authoring,
hook development, evaluation, and skill-stability tracking).
The full skill, command, and agent inventory is in the Capabilities Reference.
⚠️ Plugins run inside your Claude Code session and can read or edit your repo, run shell commands, and call external services. Review any plugin before installing it.
Three guards reduce the blast radius, but none replace your own review:
Meta-skills infrastructure for Claude Code plugin ecosystem - skill authoring, hook development, modular design patterns, and evaluation frameworks
Spec Driven Development toolkit - structured specification, planning, and implementation workflows for systematic feature development
Spatial knowledge organization using memory palace techniques - build, navigate, and maintain virtual memory structures for enhanced recall and information management. Includes PR Review Room for capturing review knowledge.
Documentation review, cleanup, generation, voice extraction, and human-quality writing enforcement with AI slop detection and SICO-based voice profiling
Multi-source research plugin — code archaeology, community discourse, academic literature, and TRIZ cross-domain analysis with domain-adaptive depth
npx claudepluginhub athola/claude-night-market --plugin parseltongueUse this agent when working with Python code that requires advanced features, performance optimization, or comprehensive refactoring. Examples: <example>Context: User needs to optimize a slow Python function that processes large datasets. user: "This function is taking too long to process our data, can you help optimize it?" assistant: "I'll use the python-expert agent to analyze and optimize your Python code with advanced techniques and performance profiling."</example> <example>Context: User wants to implement async/await patterns in their existing synchronous Python code. user: "I need to convert this synchronous code to use async/await for better performance" assistant: "Let me use the python-expert agent to refactor your code with proper async/await patterns and concurrent programming techniques."</example> <example>Context: User needs help implementing complex Python design patterns. user: "I want to implement a factory pattern with decorators for my API endpoints" assistant: "I'll use the python-expert agent to implement advanced Python patterns with decorators and proper design principles."</example>
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
Research-backed best practices for building modern, production-grade Python packages — project structure, pyproject.toml, typing, testing, CI/CD, documentation, versioning, API design, packaging, security, and developer experience
Python-specific validation, patterns, and expert agents
Python development ecosystem - uv, ruff, pytest, packaging, type checking
Opinionated Python 3.11+ engineering system. Establishes strong defaults (SOLID, typing policy, testing standards, code smell detection) and routes to specialist skills for TDD, CLI, web, data/science, and constrained environments.