Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
日本語の概要は準備中です。原文の説明を表示しています。
Agents should invoke this skill for academic or technical papers, arXiv/PubMed/IEEE/ACM links, PDFs, methodology review, limitations, practical implications, or extracting findings for engineering decisions.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Extract actionable insights from academic and technical papers.
# Paper Summary: <Paper Title>
**Authors:** [Author list]
**Published:** [Journal/Conference, Date]
**Link:** [URL]
**Quality:** Peer-reviewed / Preprint / Workshop paper
## TL;DR
[1-2 sentence summary of the key contribution]
## Problem
[What problem does this paper address? Why does it matter?]
## Approach
[Methodology in plain language — what did they do?]
## Key Findings
Anchor each finding to the paper so readers can verify. Use **§** for sections, **Fig.** / **Table** when the evidence is visual or tabular.
1. **[Finding 1]:** [Description with key metrics/numbers] — *Evidence:* §[N] [section name]; [Fig. X / Table Y if applicable]
2. **[Finding 2]:** [Description] — *Evidence:* §[N] …
3. **[Finding 3]:** [Description] — *Evidence:* §[N] …
## Claim–evidence map
| # | Claim (one line) | Where in paper | Type |
|---|------------------|----------------|------|
| 1 | [Claim] | §3.2 Results, Table 2 | Empirical |
| 2 | [Claim] | §1 Introduction | Stated goal |
| 3 | [Claim] | Fig. 4 | Qualitative |
Use this table for citation-audit alignment against the project's own research notes. If the PDF has no section numbers, use **page** or **heading text** instead of §.
## Practical Implications
[What does this mean for practitioners? How can we use these findings?]
- For the current stack: [Specific applicability to Rust/TS/Python work]
- For current projects: [How this might inform current work]
## Limitations
- [Limitation 1: e.g., small sample size, specific domain]
- [Limitation 2: e.g., not replicated, theoretical only]
## Related Work
- [Paper 1] — [How it relates]
- [Paper 2] — [How it relates]
## Verdict
**Reliability:** High / Medium / Low
**Relevance to us:** High / Medium / Low
**Action:** Apply directly / Consider for future / Interesting but not actionable
Before paraphrasing implications, list atomic claims the paper makes (results, bounds, contributions). For each: section / figure / table reference (or page). Prefer primary evidence (results section) over abstract-only restatement.
The most important question: "What can we do differently because of this paper?"
| Factor | Assessment |
|---|---|
| Peer review status | Published / Preprint / Workshop |
| Replication | Replicated / Single study / Theoretical |
| Sample size | Adequate / Small / N/A |
| Methodology rigor | Strong / Moderate / Weak |
| Author credibility | Established / New / Anonymous |
| Conflicts of interest | None apparent / Funded by [X] / Vendor paper |
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概要と使いどころ
Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
日本語の概要は準備中です。原文の説明を表示しています。
Audit and improve project-rules files (AGENTS.md, CLAUDE.md, .agents/instructions, local overrides) so the agent keeps accurate project context. Use when the user asks to check, audit, review, update, improve, or fix their AGENTS.md or CLAUDE.md, mentions "project rules maintenance" or "agent context optimization", or when the codebase has changed enough that the rules file may be stale. Scans the repository for every rules file, grades each against a quality rubric, outputs a quality report, and applies targeted edits only after user approval.
日本語の概要は準備中です。原文の説明を表示しています。
Capture learnings from the current session into the project-rules file (AGENTS.md, CLAUDE.md, or local override) so future sessions benefit. Use when the user says "revise the rules", "update AGENTS.md / CLAUDE.md with what we just learned", "save this to project memory", "remember this for next time", or at the end of a productive session when valuable context has emerged that is not yet documented. This complements agents-md-improver — improver audits, while this one captures.
日本語の概要は準備中です。原文の説明を表示しています。
Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside frontend-design.
日本語の概要は準備中です。原文の説明を表示しています。
Design a feature architecture by analyzing existing codebase patterns and conventions, then provide a comprehensive implementation blueprint with specific files to create or modify, component designs, data flows, and a build sequence. Use this skill when the user asks for an architecture design, an implementation plan for a non-trivial feature, or when dispatched as a sub-task during feature-dev architecture phase.
日本語の概要は準備中です。原文の説明を表示しています。
Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".
日本語の概要は準備中です。原文の説明を表示しています。