PAL via MCPorter

Quick start

• See which tools are available (from the bundled generated CLI help):
  • bash "<path-to-skill>/scripts/pal"
• Print copy/pastable examples for every tool currently exposed by your PAL server:
  • bash "<path-to-skill>/scripts/pal" examples
• Run a fast self-test (validates bundled CLI + PAL server startup; no live model call):
  • bash "<path-to-skill>/scripts/pal" selftest
• Run self-test including live model calls (costs tokens; requires an API key):
  • bash "<path-to-skill>/scripts/pal" selftest --live
• Call tools:
  • bash "<path-to-skill>/scripts/pal" -o json version
  • bash "<path-to-skill>/scripts/pal" -o json listmodels
  • bash "<path-to-skill>/scripts/pal" -o markdown apilookup --prompt "OpenAI Responses API streaming"
  • bash "<path-to-skill>/scripts/pal" -o markdown chat --prompt "ping" --working-directory-absolute-path "$PWD" --model auto

Workflow

1. Read the embedded tool list.
   • bash "<path-to-skill>/scripts/pal"
2. Use examples to find minimal calls / argument shapes (recommended).
   • bash "<path-to-skill>/scripts/pal" examples
3. Call the tool via the bundled CLI.
   • Prefer --raw '{...}' for complex payloads and optional parameters.
   • Global flags (-o/--output, -t/--timeout) must come before the tool name.
4. Treat stdout as the tool result.
   • Use -o markdown for readability or -o json when you want structured output.

Does this match pal-mcp-server?

Mostly. This skill runs PAL via uvx … pal-mcp-server and bundles a CLI generated from PAL’s tool schemas via mcporter generate-cli. If upstream adds/removes tools, the bundled CLI may need to be regenerated.

• Check which PAL version you’re running: bash "<path-to-skill>/scripts/pal" -o json version
• Source-of-truth tool surface (live): bash "<path-to-skill>/scripts/pal" examples

Notes (from upstream + schemas)

• Tool surface can be reduced with DISABLED_TOOLS (helpful for clients that ingest tool schemas). Upstream often recommends: analyze,refactor,testgen,secaudit,docgen,tracer to save context.
• Upstream PAL supports multi-turn threads via continuation_id, but this skill runs PAL in ad-hoc STDIO mode (fresh server per invocation), so continuations generally won’t resume across separate pal commands.
• Prefer passing files via absolute_file_paths (and images via images) rather than pasting large blobs into prompts.

Selftest knobs

• PAL_SELFTEST_CLI_NAME: which CLI clink should call in selftest --live (auto-detects codex, claude, gemini if present in PATH)
• PAL_SELFTEST_MODEL: optionally override the model used during selftest --live (when supported by the tool)
• PAL_SELFTEST_THINKING_MODE: defaults to minimal

Configuration

This skill uses ad-hoc stdio mode (no mcporter.json required) and ships a bundled CLI at scripts/pal-cli.js.

Set these env vars when needed:

• OPENAI_API_KEY (required for OpenAI-backed PAL usage; alternatives include GEMINI_API_KEY, OPENROUTER_API_KEY, etc.)
• PAL_DEFAULT_MODEL (default: auto; forwarded as DEFAULT_MODEL when DEFAULT_MODEL is unset)
• PAL_MCP_FROM (default: git+https://github.com/BeehiveInnovations/pal-mcp-server.git)
• MCPORTER_VERSION (default pinned in "<path-to-skill>/scripts/pal")
• PAL_MCPORTER_TIMEOUT_MS (default: 120000)
• DISABLED_TOOLS (PAL setting; comma-separated tool names to hide/disable in the server; unset/empty enables all)

Notes

• bash "<path-to-skill>/scripts/pal" is the quick “what’s available?” view; use examples when you need parameter shapes.
• STDIO mode inherits your shell environment automatically; avoid passing secrets via --env KEY=value unless you have to.
• If PAL startup is slow (first uvx run), increase timeouts with PAL_MCPORTER_TIMEOUT_MS or -t <ms>.