Research Question

Research Question Loop

A sharpen → score → revise loop for the framing stage of research. The artifact is a small set of research questions; the feedback signal is how many clear the bar — each scored 0-5 on five fixed axes (Specific, Answerable, Novel, Feasible, Significant). You start from a vague topic, draft candidates, score each against the rubric (with a light novelty check against the literature), and rewrite the weakest axis of the promising ones until enough are strong.

A good research question is the hard part of research: too broad and it cannot be answered; too narrow and it does not matter; already settled and there is no point. The goal is a few excellent questions, not many mediocre ones — this loop drives toward the narrow band that is answerable, novel, and worth answering.

Scope & limitations

This loop produces and refines questions, grounded by a light novelty check (a few searches), not a full survey — for an exhaustive map use the literature-survey loop, and to turn a question into testable predictions use the hypothesis-generation loop. The novelty check needs web or literature access; without it (novelty_check: none), novelty is the loop's best judgment and must be labeled unverified.

When to use

Use when the user has a topic, area, or rough curiosity and wants it turned into concrete questions worth pursuing. Default: run the full draft→score→revise loop below. Escape hatch: if the user only wants candidates rated (no rewriting), score the set once and report the rubric breakdown. Not for grading a finished proposal, and not for generating hypotheses or experimental designs.

Setup

Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is available) infer a likely value for each binding and present it as the recommended option; on other hosts ask each as a quoted plain-text prompt. Then write loop.run.yaml (format: examples/run.example.yaml) and confirm the values before creating any other files.

Novelty toolchain (only for novelty_check: lit). Paper search goes through the sibling literature-search skill (<lit> = <lit_skill_dir>/tools/lit_search.py, with <lit_py> = python3 and <lit_skill_dir> its installed location, e.g. ~/.claude/skills/literature-search/); the relevant moves are <lit> search "<q>" (is a direct answer already published?) and <lit> snippet "<q>" (pinpoint the answering passage). Confirm <lit> --help works at setup; if the skill is absent, tell the user and either install it (copy the repo's loops/literature-search folder into ~/.claude/skills/) or degrade to web (host WebSearch/WebFetch) or none. Record the resolved choice in <novelty_check> so re-runs are non-interactive.

The loop

The rubric (a fresh Grader scores each question 0-5 per axis — see grading below):

Grading — spawn a fresh Grader per iteration (spawn-or-degrade). Each iteration, spawn a freshly instantiated Grader subagent — separate from whoever drafted or revised the questions, so the score is independent and not self-graded — and give it each candidate plus its novelty evidence. It returns the five raw 0-5 per-axis points (no weights). On Claude Code spawn it as a real Agent; otherwise adopt the Grader role inline in a clean pass. The orchestrator sums to a raw score out of 25, then converts to the 0-100 score used everywhere:

total = 100 × raw / 25 (e.g. raw 20/25 → total 80).

A question is strong when total ≥ <pass_threshold> and no axis scored 1 (a single fatal axis sinks it regardless of total).

Copy this checklist and tick items off:

• Iteration 0 — restate <topic> and what is interesting about it; draft 3-5 candidate questions spanning different angles (mechanism, comparison, condition/boundary, application). Record nothing as strong yet.
• Gather novelty evidence per candidate via <novelty_check> (<lit> search/snippet, or WebSearch, or skip).
• Score — spawn a fresh Grader with each candidate + its evidence; it returns raw 0-5 per axis; convert raw/25 → total/100.
• Diagnose each promising question's lowest axis — the one thing keeping it from strong.
• Revise that one axis (one focused move per question); drop a fatally-flawed question revision cannot save; add a fresh candidate if short.
• Append a ledger row; stop when <n_questions> clear the bar, or at <budget>.