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Roadmap

Your GitHub Name edited this page May 20, 2026 · 7 revisions

Roadmap

PromptPilot's roadmap holds two principles:

  1. The SLM stays the harness brain — it does not become the coder.
  2. When the SLM is uncertain, the correct action is passthrough.

Shipped

The pieces below are in main and covered by the test suite.

  • v2 control plane — JSON ExecutionSpec with route ∈ {clarify, answer, passthrough, act} plus intent, scope, target_files, risk, memory_record. v1 prose envelope kept as fallback parser. (prpt/core/spec.py, prpt/normalizers/slm_openai_v2.py)
  • Four interchangeable judgesMaxHaikuJudge, CodexCliJudge, AnthropicApiJudge, OpenAiJudge, auto-detected in priority order. (prpt/judges/judge.py)
  • Hybrid auth pattern — API-key SLM + subscription LLM, ~4,500× subsidy ratio measured on chain5. (HYBRID_MODE.md, BENCHMARKS.md)
  • Handoff / restart workflowprpt checkpoint, prpt bootstrap, prpt restart for collapsing heavy sessions to handoff.md and resuming fresh. (prpt/handoff.py)
  • Session memory with referential gate — recent turns prepended; --gate-session classifier skips history when prompt is self-contained. Default-on after N=5 chain1 retest showed +60% success / −28.7% cps for WITH-session.
  • Onboarding subcommandsprpt setup (one-time, with smoke test), prpt doctor (re-check, no install), prpt install-hook for both Claude Code and Codex.
  • Tool-output compression hook — regex-based PostToolUse compressor for pytest / grep / git diff / installer logs. (.codex/hooks/compress_tool_output.py)
  • Compliance posture documentation — side-by-side breakdown vs the OpenClaw / OpenCode pattern enforced April 2026. (COMPARISON.md)
  • Wiki publishing automationscripts/publish_wiki.sh keeps the wiki mirrored from docs/. (WIKI_WORKFLOW.md)

In progress

  • Honest measured benchmarks — chain1, chain4, chain5 numbers are landed; broader workload coverage (Django / Rails / JS repos) not yet measured.
  • Route-accuracy fixture set — labelled prompts to score clarify / answer / passthrough / act decisions, not just per-call cost.
  • Preservation recall metrics — programmatic checks for file paths, test names, commands, flags, symbols, stack frames, and explicit constraints.

Planned

  • Cross-provider / model-size A/B for the SLM harness (Haiku vs gpt-5.4-nano vs gpt-5.4-mini).
  • Surface high-risk transformations to the user before invoking the coding agent.
  • More worked passthrough and compression examples.
  • Easier telemetry inspection during replay (current prpt stats is a flat JSONL summary).

Non-goals

  • Replacing frontier coding models with the SLM.
  • Maximizing token reduction when context may be lost.
  • Letting the SLM make irreversible code changes.
  • Token handling for subscription auth (we invoke the official binary; the credential never enters our process).

See also: Benchmarks · Telemetry and Replay · Wiki Publishing

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