Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/gaasher/agent-loop-skills/research-proposalnpx skills add gaasher/Agent-Loop-Skills --skill research-proposalgit clone --depth 1 https://github.com/gaasher/Agent-Loop-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gaasher/agent-loop-skills/research-proposal)<a href="https://agentmods.dev/skills/gaasher/agent-loop-skills/research-proposal"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/research-proposal.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00112 | $0.02523 |
| Opus 5 | $0.00056 | $0.01262 |
| Sonnet 5 | $0.00022 | $0.00505 |
| Haiku 4.5 | $0.00011 | $0.00252 |
Grade A, and why
research-proposal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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; emitsscholareval.json.roles/Judge.md— grades the feedback → 0-100 + ranked fixes; emitsverdict.json(decides pass).roles/Reviser.md— rewrites the proposal to address the fixes (guards the research intent).rubrics/rubric.md— the fixed grading rubric (the Judge never edits it).schemas/scholareval.schema.json,schemas/verdict.schema.json— the two validated outputs.
Spawn-or-degrade. On Claude Code, spawn ScholarEval / Reviser as real Agent subagents;
otherwise adopt each role inline. You are the orchestrator and the Judge.
When to use
Use when a written proposal exists and the user wants it pushed past a quality bar with literature-grounded critique. Default: run the full evaluate→grade→revise loop below. Escape hatch: if the user only wants the critique (no rewriting), run one ScholarEval + Judge pass and stop. Not for writing a proposal from a blank page, and not for a standalone literature survey.
Setup
Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it 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 every value,
<intent>, and the live/degraded literature tier before creating any other files.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 147 lines · 112 tokens per session scan A 76e4ab854c0b
research-proposal is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (163 stars, last pushed 2mo ago), licensed MIT. It adds 112 tokens to every session and 2,523 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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