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 oaustegard/claude-skills --skill reviewing-ai-papersgit clone --depth 1 https://github.com/oaustegard/claude-skillsWrote 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/oaustegard/claude-skills/reviewing-ai-papers)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/reviewing-ai-papers"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/reviewing-ai-papers/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/oaustegard/claude-skills/reviewing-ai-papers"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/reviewing-ai-papers.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 | $0.00105 | $0.01766 |
| Opus 5.5 | $0.00042 | $0.00706 |
| Sonnet 5.5 | $0.00021 | $0.00353 |
| Haiku 4.5 | $0.00011 | $0.00177 |
Grade A, and why
reviewing-ai-papers 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 2mo 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reviewing AI Papers
When users request analysis of AI/ML technical content (papers, articles, blog posts), extract actionable insights filtered through an enterprise AI engineering lens and store valuable discoveries to memory for cross-session recall.
When NOT to use this skill
The subject matter has to be machine learning. Adjacent asks that are not:
| Situation | Use |
|---|---|
| "Does this text read as AI-written?" | declauding |
| Register or voice pass on a draft | declauding |
| Review a pull request or a diff | code-review |
| A paper outside ML | read it directly; this skill's lens will not fit |
"AI" appearing in the request is not the trigger — AI being the topic of the document is.
Contextual Priorities
Technical Architecture:
- RAG systems (semantic/lexical search, hybrid retrieval)
- Vector database optimization and embedding strategies
- Model fine-tuning for specialized scientific domains
- Knowledge distillation for secure on-premise deployment
Implementation & Operations:
- Prompt engineering and in-context learning techniques
- Security and IP protection in AI systems
- Scientific accuracy and hallucination mitigation
- AWS integration (Bedrock/SageMaker)
Enterprise & Adoption:
- Enterprise deployment in regulated environments
- Building trust with scientific/legal stakeholders
- Internal customer success strategies
- Build vs. buy decision frameworks
Analytical Standards
- Maintain objectivity: Extract factual insights without amplifying source hype
- Challenge novelty claims: Identify what practitioners already use as baselines. Distinguish "applies existing techniques" from "genuinely new methods". The procedure for this is "The ablation the paper did not run" below. Run it; do not improvise a judgement
- Separate rigor from novelty: Well-executed study of standard techniques ≠ methodological breakthrough
- Confidence transparency: Distinguish established facts, emerging trends, speculative claims
- Contextual filtering: Prioritize insights mapping to current challenges
What ships with it
1 file 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.
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.
- 2mo ago Changed · +53 lines 0127ca2c5a4e
- 2mo ago First seen · 140 lines · 105 tokens per session scan A 987643461014
reviewing-ai-papers is a skill published in the GitHub repository oaustegard/claude-skills (374 stars, last pushed today), licensed MIT. It adds 105 tokens to every session and 1,766 once invoked, about $0.0004 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 2026-09-03.
reviewing-ai-papers compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| reviewing-ai-papersoaustegard/claude-skills | Skill | 374 | today | MIT | A | 105 | Claude Code |
| langchain-developmentMindrally/skills | Skill | 305 | 1mo ago | Apache-2.0 | A | 29 | Claude Code |
| ai-productlingxling/awesome-skills-cn | Skill | 331 | 1mo ago | MIT | A | 28 | Claude Code |
| laravel-ai-sdkfusengine/agents | Skill | 34 | 1mo ago | MIT | A | 35 | Claude Code |
| aws-generative-ai-developerVincentChuWaiChow/vanguard-frontier-agentic | Skill | 24 | today | Apache-2.0 | A | 93 | Claude Code |
| ai-securityKin9Zeus/senior-engineer-skills | Skill | 3 | 20d ago | MIT | A | 225 | Claude Code |
| dspy-haystack-integrationOmidZamani/dspy-skills | Skill | 124 | 4mo ago | MIT | A | 32 | Claude Code |