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pi-clarify

Prompt clarification extension for pi coding agent.

Sample

Features

  • clarify_prompt tool - Prompts the LLM to ask clarifying questions when user input is vague
  • Vague input detection - Flags structurally empty input (blank, single-character, or pure punctuation) and lets the LLM judge ambiguity for everything else via an injected system-prompt guideline
  • /clarify toggle - Enable or disable clarification with /clarify on|off
  • ~ bypass prefix - Prefix prompts with ~ to skip clarification for one turn

Installation

pi install npm:@dkmnx/pi-clarify

Or add directly to your settings.json:

{
  "packages": ["npm:@dkmnx/pi-clarify"]
}

Usage

Automatic Clarification

When enabled, the LLM automatically detects vague prompts and asks for clarification:

  • "fix it" → "What specifically needs to be fixed?"
  • "make it better" → "What does 'better' mean in this context?"
  • "optimize this" → "Which files or functions should be optimized?"

Manual Tool Call

The LLM can explicitly call the clarify_prompt tool:

clarify_prompt({
  question: "What specific behavior needs to be fixed?",
  options: [
    "Fix the login redirect issue",
    "Fix the form validation error",
    "Fix the memory leak in the dashboard"
  ]
})

Commands

Command Description
/clarify Toggle clarification on/off
/clarify on Enable clarification
/clarify off Disable clarification

Bypass

Prefix your prompt with ~ to skip clarification for one turn:

~ fix it - just update the error message text

~ is used instead of ! because pi reserves !/!! as the built-in shell-command prefix.

How vague input is detected

Clarification is driven by the LLM, not by keyword matching. When enabled, the extension appends a CLARIFY_PROMPT guideline to the system prompt instructing the model to call clarify_prompt whenever a request is ambiguous, has unclear outcomes/scope, admits multiple valid interpretations, or is missing constraints.

The only client-side heuristic is a minimal structural guard: completely blank, single-character, or pure-punctuation input is flagged as vague so the model receives an extra reminder. Short but actionable commands like git push or npm test are not auto-flagged — the model decides based on the full conversation context.

Example patterns the LLM is told to clarify

These are examples the injected guideline tells the model to watch for (the model does the actual judgment, not the extension):

  • Ambiguous referents: "fix it", "this is broken", "the bug"
  • Unclear outcomes: "make it better", "improve the code"
  • Undefined scope: "refactor everything", "fix the tests"
  • Missing constraints: no mention of backwards compatibility, performance priorities, or approach preferences
  • Multiple valid interpretations: the request could reasonably mean 2+ different things

License

MIT