pynchy
pynchy contient 9 skills collectées depuis crypdick, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
Use when managing the pynchy service on the server — deploying changes, observing logs, checking service status, restarting the service, setting up GitHub auth, rebuilding the agent container, or running commands on the live Pynchy host. Also use when interacting with the LiteLLM proxy — investigating failed requests, model routing errors, spend tracking, health checks, API gateway diagnostics, or modifying the LiteLLM configuration. Also use when the user mentions the LiteLLM UI, dashboard, proxy errors, or model availability.
Use when creating, scaffolding, or updating a pynchy plugin, including channels, MCP servers, skills, agent cores, workspace specs, and container runtime plugins. Also use when users ask how to register plugins via config.toml, add entry points, or validate plugin hook wiring.
Drive a host desktop through Pynchy's policy-mediated provider plugins when browser automation is inappropriate.
How to use browser tools for web navigation and interaction
Refresh expired Slack browser tokens (xoxc/xoxd) using persistent browser sessions. Use when Slack MCP tools fail with authentication errors.
Post tweets, like, reply, retweet, and quote on X (Twitter) using browser automation. Use when the user asks you to interact with X/Twitter.
When running multi-line Python code or code with quotes, apostrophes, or f-strings via Bash, always use heredoc syntax instead of python -c to avoid shell quoting issues.
Use when writing or reviewing pynchy documentation, deciding where to document things, updating the docs, checking doc consistency, or fixing broken links. Covers information architecture, writing philosophy, tree-shaped navigation, doc-code coupling, no hard-coded usernames, extensibility framing for pluggable subsystems, and when to add code comments.
Use when running pynchy locally — running the app, tests, linting, formatting, prek hooks, or rebuilding the agent container. Also use when determining whether you're on the live Pynchy host or a local machine, and for debugging agent behavior-- session transcript branching, inspecting message history and agent traces in SQLite, pytest hangs, or diagnosing known codebase issues.