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Your GitHub Name edited this page May 19, 2026 · 5 revisions

Architecture

PromptPilot is built around one idea:

A small language model can serve as the harness brain around a larger coding agent.

The SLM is not the coder. It is the control layer.

It decides:

  • What is the user asking?
  • Is the prompt clear enough?
  • Is this safe to rewrite, or should we pass it through unchanged?
  • Should the request be answered directly?
  • What facts must be preserved when the coding agent is invoked?

A separate post-tool subsystem decides:

  • Should noisy bash tool output be compressed before it re-enters the agent's context?

The large coding agent remains responsible for:

  • Code understanding
  • Implementation
  • Debugging
  • Test repair
  • Architectural reasoning
Developer prompt
        ↓
PromptPilot
        ↓
SLM harness
  - intent
  - ambiguity
  - risk
  - preservation
        ↓
Route (one of four)
  - clarify
  - answer
  - passthrough
  - act    (with constraint-preserving rewrite -> downstream_prompt)
        ↓
Codex / Claude-style coding agent
        ↓
Tool output (bash) ── PostToolUse hook ──> regex compressor ──> back to agent
        ↓
Telemetry / replay / audit

Runtime layers

PromptPilot separates workflow control from coding execution:

  1. Input capture receives the developer prompt and session memory.
  2. SLM harness classifies intent, extracts constraints, evaluates ambiguity, and estimates transformation risk.
  3. Route selection chooses one of four routes: clarify, answer, passthrough, or act (which carries a constraint-preserving rewrite in downstream_prompt).
  4. Frontier coding agent receives either the raw prompt (passthrough) or the SLM's downstream_prompt (act).
  5. Tool-output compression (separate subsystem) runs as a PostToolUse hook on the agent's bash output, shrinking pytest/grep/git/installer noise before it re-enters the agent's context. Regex/heuristic-based, not SLM-driven.
  6. Telemetry and replay record routing, token impact, compression events, and session handoff data.

This keeps the SLM central but bounded: it controls the workflow around the coding agent without replacing the model that reasons about and edits code.

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