Pynchy dev

Skill crypdick/pynchy/.claude/skills/pynchy-dev

OpenClaw alternative with strict container isolation for security measures. Connects to WhatsApp, has memory, scheduled jobs, and runs directly on Anthropic's Agents SDK

Install

npx -y skills add crypdick/pynchy --skill pynchy-dev

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One thing to look at

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What its author says it does

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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.

SKILL.md

4.4 KB, as published. Nobody here has run it

Pynchy Development

Run commands directly—don't tell the user to run them.

Am I on pynchy?

Check hostname and compare it with the deployment-specific live host from local memory, environment, or the operator. If you are on that host, access services at localhost. Otherwise, reach Pynchy over SSH or Tailscale using that configured host.

Commands

uv run pynchy            # Run the app
uv run pytest tests/     # Run tests
uv run ruff check --fix src/  # Lint + autofix
uv run ruff format src/       # Format
uvx prek run --all-files  # Run all repository hooks
./src/pynchy/agent/build.sh     # Rebuild agent container

Isolated feature runtimes

Use new-feature from the control checkout when a feature needs an isolated Pynchy runtime. See the feature-runtime workflow for create, merge, restart, and teardown commands. Do not use raw git worktree commands for managed features. Install or verify its host dependencies with ./scripts/install_new_feature_dependencies.py.

Documentation Lookup

When you need documentation for a library or framework, use the context7 MCP server to get up-to-date docs. Don't rely on training data for API details that may have changed.

Testing Philosophy

Write tests that validate actual business logic, not just line coverage. See references/testing-philosophy.md for what makes a good test vs. coverage theater.

Known Issues

Transcript branching is closed by construction, not to be reopened casually — native Teams tools (TeamCreate/TeamDelete/SendMessage) are not allow-listed in either core, so nothing can branch the leader's session transcript. Task sidechains remain allowed (they write off the main chain and resume safely). Reintroducing Teams requires per-teammate session isolation first — tracked in backlog/0-proposed/reintroduce-teams-session-isolation.md.

Debugging Agent Behavior

Prefer querying SQLite over docker logs — docker logs truncate output, but the DB stores full content and captures agent internals (thinking, tool calls, system prompts).

Database: data/messages.db. If not on the Pynchy host, prefix commands with ssh "$PYNCHY_HOST" after setting PYNCHY_HOST to the deployment-specific hostname.

# Last 20 messages in a channel
sqlite3 data/messages.db "
  SELECT timestamp, sender_name, message_type, substr(content, 1, 120)
  FROM messages WHERE chat_jid = '<JID>'
  ORDER BY timestamp DESC LIMIT 20;
"

# Last 20 tool calls globally
sqlite3 data/messages.db "
  SELECT timestamp, chat_jid, json_extract(payload, '$.tool_name') AS tool
  FROM events
  WHERE event_type = 'agent_trace'
    AND json_extract(payload, '$.trace_type') = 'tool_use'
  ORDER BY timestamp DESC LIMIT 20;
"

Full query cookbook (cross-table traces, thinking, activity timelines): references/sqlite-queries.md
Session transcript branching: references/session-transcript.md
Pytest hangs (100% pass, never exits): references/pytest-hang-diagnostics.md

OpenAI Shell Tool Pitfall

If the OpenAI backend shows /bin/sh: Syntax error: word unexpected (expecting ")") for shell tool calls, the shell executor is likely receiving a ShellCommandRequest(...) object and trying to run its repr. Ensure _make_shell_executor in src/pynchy/agent/agent_runner/src/agent_runner/cores/openai.py extracts command from object/mapping shapes (including parsing repr when needed).

If OpenAI tool calls show up with empty tool_input in events, the tool_call_item.raw_item usually carries the data. Common raw_item.type values: shell_call (uses action.commands), local_shell_call (uses action.command list), apply_patch_call (uses operation), and function_call/mcp_call (uses JSON arguments). Parse those fields before falling back to generic mappings.