content-refinement-agent

content-refinement-agent is a skill for Claude Code, Codex from Aukexecutivedepartment5152/PaperOrchestra. It costs 104 tokens per session (2,512 once invoked), scanned A, original, no licence file.

A draft-improvement step in the PaperOrchestra research-paper workflow. It simulates peer review, meaning feedback similar to what other researchers might give before publication, and revises paper.tex files.

In plain words
What is it for?
It is for revising LaTeX research-paper drafts, keeping a worklog, and applying or reverting targeted changes under the workflow's rules.
Why use it?
It provides a controlled way to refine a paper while recording each iteration and allowing real reversion when a revision should be discarded.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit It is for revising LaTeX research-paper drafts, keeping a worklog, and applying or reverting targeted changes under the workflow's rules.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aukexecutivedepartment5152/paperorchestra/content-refinement-agent
Install

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.

Any agent
npx skills add Aukexecutivedepartment5152/PaperOrchestra --skill content-refinement-agent
Clone the repo
git clone --depth 1 https://github.com/Aukexecutivedepartment5152/PaperOrchestra

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for content-refinement-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/aukexecutivedepartment5152/paperorchestra/content-refinement-agent/github.svg)](https://agentmods.dev/skills/aukexecutivedepartment5152/paperorchestra/content-refinement-agent)
Your own site
<a href="https://agentmods.dev/skills/aukexecutivedepartment5152/paperorchestra/content-refinement-agent"><img src="https://agentmods.dev/badge/skills/aukexecutivedepartment5152/paperorchestra/content-refinement-agent/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for content-refinement-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/aukexecutivedepartment5152/paperorchestra/content-refinement-agent"><img src="https://agentmods.dev/badge/skills/aukexecutivedepartment5152/paperorchestra/content-refinement-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,512 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00104 $0.02512
Opus 5 $0.00052 $0.01256
Sonnet 5 $0.00021 $0.00502
Haiku 4.5 $0.00010 $0.00251

Measured 12d ago against content hash 53a45f94ec05, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

content-refinement-agent 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 12d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/apply_worklog.py, scripts/score_delta.py, scripts/snapshot.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/content-refinement-agent/SKILL.md · 256 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

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.

Changes

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.

  1. 12d ago First seen · 256 lines · 104 tokens per session scan A 53a45f94ec05

Subscribe to this mod's changes

content-refinement-agent is a skill published in the GitHub repository Aukexecutivedepartment5152/PaperOrchestra (2 stars, last pushed today), with no licence file. It adds 104 tokens to every session and 2,512 once invoked, about $0.0005 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 2026-08-31.