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osp-answer-generator-agent

Open ScholarPeer: Implementation of DeepMind's ScholarPeer Protocol for AI Peer-Review. Use in Claude, Codex, Antigravity, Pi, etc.

Install / Use

npx skills add amirkiarafiei/open-scholar-peer --skill osp-answer-generator-agent

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

70/100

Supported Platforms

Universal

Our assessment of osp-answer-generator-agent

osp-answer-generator-agent scores 70/100 on our quality scale, 3802nd of 4,650 Development & Engineering skills we index.

Its SKILL.md is 5.5 KB long, split into 6 sections with 1 code example: a solid amount of guidance for an agent.

It has no GitHub stars yet, so there is no community track record; judge it on its content.

Substance
26/30
Structure
15/20
Description
15/15
Adoption
0/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 15 days ago, so osp-answer-generator-agent is actively maintained.
  • Our last check on 2026-10-18 found the source still online.
  • No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
  • Its trust signals score 80/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

osp-answer-generator-agent compared with similar skills

All 4 of these similar skills score higher than osp-answer-generator-agent; compare them before choosing.

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osp-answer-generator-agent (this skill)by amirkiarafiei70015d agoSKILL.md
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Frequently asked questions

How do I install osp-answer-generator-agent?
Run npx skills add amirkiarafiei/open-scholar-peer --skill osp-answer-generator-agent. The install tabs above show the steps for each supported agent.
Which AI agents does osp-answer-generator-agent work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is osp-answer-generator-agent safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 80/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is osp-answer-generator-agent still maintained?
The repository was last updated 15 days ago, so osp-answer-generator-agent is actively maintained.

name: osp-answer-generator-agent description: > Open ScholarPeer Answer Generator Agent — invoked as a subagent by the Query Agent (or as a self-reflection turn on the few tools without subagents). Receives a single probing question plus a minimal context bundle, performs verification against retrieved literature and domain narrative, and returns (answer, citations, discrepancy flag). Stateless across questions — context is supplied fresh each invocation.

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.

Output format (subagent return value or post-marker turn)

**Answer:**
<2-4 sentence answer grounded in the context bundle and any newly retrieved sources.>

**Verification:**
- Self-answer based on `01_structured_summary.md`: <one line>
- External cross-check: <what you actually checked against. **Name an identifier** — a DOI, an
  arXiv id, a PMCID, a URL, or a numbered row in `02_retrieved_literature.md` — and the section if
  you read one. If you could not check it, write exactly `not verified: <reason>`.>
- Result: <consistent | [DISCREPANCY]: <explanation>>

**Citations:**
- <Paper title or URL — what was actually used to support this answer>
- <...>

**Discrepancy flag:** <none | minor | major>

If you used self-reflection mode, format the same content inside the === Answer Generator (verifying) === ... === END Answer Generator === block.

Pitfalls

  • Do not read a tool error as "nothing found". An error record carries a reason: blocked, rate_limited, busy, timeout, unavailable, not_found, bad_request or failed. Only not_found and bad_request are about your call; the rest are the provider. Only an empty list means the search really looked and found nothing. Say which you got.
  • Do not hedge to be polite. If the paper's claim of state-of-the-art is contradicted by a newer pre-print, say so and cite it.
  • Do not invent citations. Every cited paper must come from the context bundle or a tool call you actually made.
  • Do not answer beyond the question. Each Q&A pair targets one angle; let the Query Agent generate the next angle.
  • Do not carry context across questions in subagent mode — that defeats the isolation. If you find yourself "remembering" a previous answer, you're in the wrong mode.

Related Skills

View on GitHub
GitHub Stars0
CategoryDevelopment
Updated15d ago
Forks0

Trust signals

80/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

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