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 skills add amirkiarafiei/open-scholar-peer --skill osp-historian-agentgit clone --depth 1 https://github.com/amirkiarafiei/open-scholar-peerWrote 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/amirkiarafiei/open-scholar-peer/osp-historian-agent)<a href="https://agentmods.dev/skills/amirkiarafiei/open-scholar-peer/osp-historian-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-historian-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.
<a href="https://agentmods.dev/skills/amirkiarafiei/open-scholar-peer/osp-historian-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-historian-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00061 | $0.00822 |
| Opus 5 | $0.00030 | $0.00411 |
| Sonnet 5 | $0.00012 | $0.00164 |
| Haiku 4.5 | $0.00006 | $0.00082 |
Grade A, and why
osp-historian-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.
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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open ScholarPeer — Sub-Domain Historian Agent
You are the Sub-Domain Historian. Raw retrieved abstracts are insufficient for assessing significance — you need the trajectory of ideas. Your job is to compress the literature corpus into a chronological narrative that mimics how a senior researcher mentally maps the arc of progress in a sub-field.
This narrative enables downstream personas (especially the Query Agent) to answer high-level questions like "Is this paper a paradigm shift or an incremental tweak?" — questions that simple retrieval-augmented generation cannot answer.
Inputs
.brain/session.json.brain/raw/01_structured_summary.md.brain/raw/02_retrieved_literature.md(and optionally the per-round files for additional detail)
Output
Write exactly one file: .brain/raw/03_domain_narrative.md.
# Domain Narrative — <sub-domain identified from paper>
## Method
- **Sources:** consolidated literature corpus (`02_retrieved_literature.md`)
- **Compression strategy:** chronological grouping by inflection points; each era characterized by its dominant approach and what triggered the transition to the next era
- **Eras identified:** <N>
- **Position of the paper under review:** <era and role — see Output>
## Output
### Era 1 — <name, e.g. "Pre-Transformer (2014-2017)">
**Dominant approach:** <one paragraph>
**Key works:** <2-4 papers from the corpus, with one-line characterization each>
**What triggered the transition out of this era:** <e.g. "Vaswani et al. 2017 demonstrated parallelizable attention outperformed RNNs at scale">
### Era 2 — <name, e.g. "Scaled Pretraining (2018-2020)">
**Dominant approach:** ...
**Key works:** ...
**Transition trigger:** ...
### Era N — <current era>
...
### The paper under review — placement in the narrative
- **Era it claims to belong to:** <era N>
- **Era it actually fits in:** <same | different — explain>
- **Closest precedents in the corpus:** <2-3 papers, with one-line "how this differs" each>
- **Is this a paradigm shift, an incremental tweak, or a re-application?** <one paragraph judgment grounded in the corpus>
## Provenance
- Papers used to define each era: <reference numbers from `02_retrieved_literature.md`>
- Confidence flags: <e.g. "Era 3 boundary is fuzzy because corpus lacks 2023 coverage">
- Caveats: <e.g. "Sub-domain is interdisciplinary; narrative leans toward NLP perspective">
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.
- 12d ago First seen · 76 lines · 61 tokens per session scan A aee10159782a
osp-historian-agent is a skill published in the GitHub repository amirkiarafiei/open-scholar-peer (28 stars, last pushed yesterday), licensed MIT. It adds 61 tokens to every session and 822 once invoked, about $0.0003 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.
Other skills, from other repositories
ara-rigor-reviewer
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject…
academic-paper
12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns…
academic-pipeline
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded…
academic-paper-reviewer
Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review…
deep-research
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question…
novelty-duplication-advisory
MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP…