research-proposal
Use when the user has a research proposal (problem + proposed methodology + planned experiments) and wants it iteratively strengthened until it clears a passing grade. ScholarEval grades the proposal against the literature (Soundness + Contribution), a Judge scores that feedback 0-100 on a fixed rubric, and a Reviser rewrites the proposal to fix the worst points without diluting the research question; loops until the grade passes or the budget is hit. Not for generating a proposal from scratch, and not for running a literature survey on its own.
npx skills add gaasher/Agent-Loop-Skills --skill research-proposal --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Research Proposal Loop The artifact is a **research proposal**; the feedback signal is **ScholarEval** (literature-grounded Soundness + Contribution) turned into a **0-100 grade** by a **Judge** against the fixed `rubric.md`. Each iteration **evaluates → grades → revises** until the grade clears `<pass_threshold>` or the budget runs out. **North star.** Clearing the threshold is the stopping condition, not the goal. The goal is the strongest, most novel, genuinely publishable version the proposal can *honestly* become — every revision should ask "does this make the work more significant and more novel?", not just "does this patch a flaw?". This is **grounded ambition**: the lift comes from better-justified methods, a sharper-but-defensible novelty claim, and stronger baselines, all backed by real retrieved evidence. Overclaiming *lowers* the grade (evidence gate + Contribution axis); it never raises it. The cast (all in this folder): - `roles/ScholarEval.md` — the two-module literature-grounded evaluator; emits `scholareval.json`. - `roles/Judge.md` — grades the feedback → 0-100 + ranked fixes; emits `verdict.json` (decides pass). - `roles/Reviser.md` — rewrites the proposal to a
- When to use
- Setup
- The loop
- Ledger
- Constraints
- Stops
What does the research-proposal skill do?
Use when the user has a research proposal (problem + proposed methodology + planned experiments) and wants it iteratively strengthened until it clears a passing grade. ScholarEval grades the proposal against the literature (Soundness + Contribution), a Judge scores that feedback 0-100 on a fixed rubric, and a Reviser rewrites the proposal to fix the worst points without diluting the research question; loops until the grade passes or the budget is hit. Not for generating a proposal from scratch, and not for running a literature survey on its own.
How do I install it?
Run `npx skills add gaasher/Agent-Loop-Skills --skill research-proposal --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gaasher/Agent-Loop-Skills, a repository with 146 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.