research-proposal

Iteratively grades and revises research proposals against literature-grounded evidence until a passing score.

Updated Sep 9, 2026
One-click install
npx skills add https://github.com/xlinh2301/EditCTC --skill research-proposal-xlinh2301
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-proposal
Source: https://github.com/xlinh2301/EditCTC/tree/main/.agents/skills/research-proposal
Command: npx skills add https://github.com/xlinh2301/EditCTC --skill research-proposal-xlinh2301

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Research proposals often contain unsupported methods, overstated novelty claims, or gaps that reviewers will catch. This Skill runs an iterative evaluate-grade-revise loop that grounds every critique in real retrieved literature, turning a draft proposal into a defensible, well-supported document. ## Core Features & Use Cases - Literature-Grounded Evaluation: A ScholarEval role assesses Soundness (per method) and Contribution (per dimension) using real paper snippets retrieved via a literature-search toolchain, never fabricated citations. - Fixed-Rubric Grading: A Judge converts each evaluation into a 0-100 grade using a frozen rubric with weighted axes (soundness, contribution, evidence quality) and hard gates that block passing on fatal flaws. - Guarded Iterative Revision: A Reviser applies one focused fix batch per iteration while protecting the frozen research intent, looping until the grade passes, the budget runs out, or progress plateaus. - Use Case: A researcher with a draft proposal on a computational drug-discovery pipeline runs the loop; over three iterations the grade climbs from 58 to 82 as contradicted methods gain mitigations and novelty claims are sharpened against named prior work. ## Quick Start Use the research-proposal skill to evaluate and iteratively strengthen my proposal in proposal.md until it reaches a grade of 75.

Frequently Asked Questions about research-proposal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I improve a research proposal with AI feedback?▼

Provide your draft proposal and let the loop run ScholarEval for literature-grounded critique, a Judge for a 0-100 grade against a fixed rubric, and a Reviser that applies ranked fixes. The loop repeats until the grade passes your threshold or the iteration budget is exhausted.

How does the proposal grading rubric work?▼

The rubric scores three axes from 0 to 5: soundness, contribution, and evidence quality, then aggregates them into a 0-100 grade with frozen weights (0.45, 0.35, 0.20 by default). Hard gates force a fail if soundness is zero, evidence quality is one or below, or a fatal ungrounded contradiction exists.

Can the loop write a research proposal from scratch?▼

No. The skill requires an existing proposal containing a problem statement, proposed methodology, and planned experiments. It also does not run a standalone literature survey; retrieval exists only to ground the evaluation and revisions.

Does the literature search require an API key?▼

No key is required for the core Semantic Scholar and arXiv search, snippet, cite, and fulltext commands. A free S2_API_KEY makes snippet and citation-graph retrieval more reliable, and the loop degrades to WebSearch and WebFetch if the literature tools are unavailable.

How does the loop prevent fabricated citations?▼

Every evidence entry must contain a verbatim snippet from a real retrieval performed that iteration, and the Judge applies an evidence gate that discounts ungrounded points before scoring. Claims without real citations are marked ungrounded rather than given invented references.

When does the revision loop stop?▼

The loop stops when the verdict passes the grade threshold, when the iteration budget is reached, or when the grade has not improved for the configured patience count. On budget or plateau stops it reports the best-grade iteration, not necessarily the last.