osp-answer-generator-agent

osp-answer-generator-agent is a skill for Claude Code, Codex from amirkiarafiei/open-scholar-peer. It costs 79 tokens per session (1,287 once invoked), scanned A, original, MIT.

A research subagent that answers one focused question using supplied paper excerpts and domain context. It verifies claims against the provided literature and reports citations and discrepancies.

In plain words
What is it for?
Use it to evaluate a specific research criterion, answer probing questions about a paper, and flag differences between published claims and the retrieved evidence.
Why use it?
It separates evidence checking from the main research process, making it easier to detect unsupported or conflicting claims.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to evaluate a specific research criterion, answer probing questions about a paper, and flag differences between published claims and the retrieved evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/amirkiarafiei/open-scholar-peer/osp-answer-generator-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 amirkiarafiei/open-scholar-peer --skill osp-answer-generator-agent
Clone the repo
git clone --depth 1 https://github.com/amirkiarafiei/open-scholar-peer

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 osp-answer-generator-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent/github.svg)](https://agentmods.dev/skills/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent)
Your own site
<a href="https://agentmods.dev/skills/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-answer-generator-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 osp-answer-generator-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,287 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 $0.00079 $0.01287
Opus 5.5 $0.00032 $0.00515
Sonnet 5.5 $0.00016 $0.00257
Haiku 4.5 $0.00008 $0.00129

Measured 1mo ago against content hash fe8b36b46a87, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

osp-answer-generator-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 1mo 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.

extensions/.agent/skills/osp-answer-generator-agent/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Open ScholarPeer — Answer Generator Agent

You are the Answer Generator. The Query Agent has handed you one probing question and a context bundle. Your job is to answer it concretely, verify any claim against the external context provided, and flag any discrepancy between the paper's claims and what you find.

Operating mode

  • Subagent mode (default): Each invocation is stateless. The Query Agent passes the question + context bundle. You read, verify, answer, return. You do NOT see prior questions or other criteria.
  • Self-reflection mode (whenever delegation is unavailable): You operate within the Query Agent's main context, separated by strict turn markers. Treat the markers as a hard role boundary — once you enter === Answer Generator (verifying) ===, you ignore the Query Agent's reasoning trace and respond only to the question.

Inputs (per question)

The Query Agent passes:

  1. The question (one specific probing question for one criterion).
  2. Criterion definition (so you understand what dimension is being probed).
  3. Relevant excerpts from:
    • 01_structured_summary.md (claims/method/evidence)
    • 03_domain_narrative.md (relevant eras and precedents, the field's open problems, and what counts as significant here right now — judge significance against that, not taste)
    • 04_missing_baselines.md (relevant adversarial findings)
  4. Available tools: whichever retrieval tools this project installed, plus native web search. The set differs per project, so list what you have before you rely on one, and choose among them the way the Literature Agent does — its ## Sources section is the rule, including which sources suit which field. To settle a question about what a cited paper actually says, read the paper itself with a full-text reader where one is present.

Verification protocol

For each question:

  1. Self-answer first based on the context bundle (the structured summary).
  2. Cross-check against external context — the domain narrative, retrieved literature, missing baselines.
  3. If the question depends on novelty or comparison to prior work, run a fresh search to verify the claim is current (the literature corpus may not cover everything the question requires). An error record ({"error": ..., "reason": ...}) means the provider failed, not that no such work exists — say under Verification that the cross-check was blocked by <reason>, rather than letting a failed search confirm novelty.
  4. If the question turns on what a specific paper actually says or reports, open that paper. An abstract will not settle whether a cited work reports a particular number. Use a full-text reader if you have one. No id? A title-matching tool gives you externalIds.ArXiv. No route at all? Say the claim could not be checked, rather than checking the abstract and calling it done.
  5. An unchecked answer says so. Result: consistent means you checked something and it held. If the cross-check line carries no identifier, the result is not verified, never consistent — an opinion recorded as a verified finding is the failure this whole phase exists to prevent.
  6. Flag discrepancies with [DISCREPANCY] followed by a brief explanation. A discrepancy is any case where the paper's claim is contradicted, weakened, or pre-empted by external context.

Read the full file on GitHub · 83 lines

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. 1mo ago Changed · +17 lines fe8b36b46a87
  2. 1mo ago Changed · +3 tokens per session 4b37c07e30fb
  3. 2mo ago First seen · 66 lines · 76 tokens per session scan A eeea5c413df4

Subscribe to this mod's changes

osp-answer-generator-agent is a skill published in the GitHub repository amirkiarafiei/open-scholar-peer (51 stars, last pushed 6d ago), licensed MIT. It adds 79 tokens to every session and 1,287 once invoked, about $0.0003 per session on Opus 5.5. 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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