conference-abstract-adaptor

conference-abstract-adaptor is a skill for Claude Code, Codex from aipoch/medical-research-skills. It costs 32 tokens per session (1,848 once invoked), scanned A, original, MIT.

A writing workflow for reshaping scientific abstracts to fit a conference’s word limit, section rules, and formatting requirements.

In plain words
What is it for?
Use it to prepare an existing research abstract for a specific conference submission.
Why use it?
It removes the manual work of adapting one abstract to different submission guidelines while keeping assumptions and missing inputs explicit.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to prepare an existing research abstract for a specific conference submission.

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Install with agentmods
npx agentmods add skills/aipoch/medical-research-skills/conference-abstract-adaptor
About the project

Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.

aipoch/medical-research-skills · 1,869 stars · on GitHub · aipoch.com

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 aipoch/medical-research-skills --skill conference-abstract-adaptor
Clone the repo
git clone --depth 1 https://github.com/aipoch/medical-research-skills

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 conference-abstract-adaptor

README.md
[![agentmods](https://agentmods.dev/badge/skills/aipoch/medical-research-skills/conference-abstract-adaptor/github.svg)](https://agentmods.dev/skills/aipoch/medical-research-skills/conference-abstract-adaptor)
Your own site
<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/conference-abstract-adaptor"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/conference-abstract-adaptor/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 conference-abstract-adaptor

Your own site · 80×15
<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/conference-abstract-adaptor"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/conference-abstract-adaptor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,848 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original 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 $0.00032 $0.01848
Opus 5 $0.00016 $0.00924
Sonnet 5 $0.00006 $0.00370
Haiku 4.5 $0.00003 $0.00185

Measured 9d ago against content hash 91dd893aa18a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

conference-abstract-adaptor 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 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/main.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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

scientific-skills/Academic Writing/conference-abstract-adaptor/SKILL.md · 218 lines

How it starts

The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Source: https://github.com/aipoch/medical-research-skills

Conference Abstract Adaptor

Conference-specific abstract formatting.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

  • Use this skill when the task needs Adapt abstracts to meet specific conference word limits and formats.
  • Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
  • Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Workflow

  1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
  2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
  3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
  4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
  5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Use Cases

  • Multi-conference submissions
  • Word count compliance
  • Format standardization
  • Deadline management

Parameters

Parameter Type Default Required Description
--abstract, -a string - Yes Abstract text file path
--conference, -c string - Yes Target conference (ASGCT, ASCO, SfN, AACR, ASM)
--output, -o string - No Output file path
--list-conferences, -l flag - No List supported conferences

Read the full file on GitHub · 218 lines

Files

What ships with it

4 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. 9d ago First seen · 218 lines · 32 tokens per session scan A 91dd893aa18a

Subscribe to this mod's changes

conference-abstract-adaptor is a skill published in the GitHub repository aipoch/medical-research-skills (1,869 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 1,848 once invoked, about $0.0002 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 2026-09-03.

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