Guide to analyze multiple documents (PDF, DOCX) against user-defined columns and produce a structured Excel output with citations. Use when the user wants to: (1) Extract specific information from multiple documents into a table, (2) Compare clauses or provisions across contracts, (3) Create a document review matrix with source citations. Triggers on: 'tabular review', 'document matrix', 'extract from documents', 'compare across documents', 'review multiple contracts'.
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 structured data from multiple documents into an Excel matrix with citations.
Use AskUserQuestion to collect:
Example column definitions:
- Parties: Names of all parties to the agreement
- Effective Date: When the agreement becomes effective
- Term: Duration of the agreement
- Governing Law: Jurisdiction for disputes
Use Glob to find all documents:
Glob(pattern: "**/*.pdf", path: "<folder>")
Glob(pattern: "**/*.docx", path: "<folder>")
Launch background agents to process documents concurrently. Each agent:
Launch agents:
Task(
prompt: "<agent_prompt>",
subagent_type: "general-purpose",
run_in_background: true
)
Agent prompt template:
You are processing documents for a tabular review.
DOCUMENTS TO PROCESS:
<list of document paths>
COLUMNS TO EXTRACT:
<column definitions>
For each document:
1. Read the document using the pdf skill (for .pdf) or docx skill (for .docx)
2. Extract the requested information for each column
3. Note the page number (PDF) or section (DOCX) where you found the information
4. Include a brief quote (30-50 chars) showing the source text
Return your results as JSON:
{
"results": [
{
"document": "<filename>",
"path": "<absolute_path>",
"extractions": [
{
"column": "<column_name>",
"value": "<extracted_value>",
"page": <page_number>,
"quote": "<brief_context_quote>"
}
]
}
]
}
If you cannot find information for a column, set value to "Not found" and explain in the quote field.
Distribution strategy:
Wait for all background agents to complete:
TaskOutput(task_id: "<agent_id>", block: true)
Aggregate all results into a single array of document extractions.
Invoke the xlsx skill to create the output file:
Create an Excel workbook at <output_path>:
SHEET 1: "Document Review"
- Header row: Document | <Column1> | <Column2> | ...
- Data rows: One row per document
For each extraction cell:
- Cell value: The extracted text
- Cell hyperlink: file://<document_path>#page=<N> (for PDFs)
- Cell comment: "Page <N>: '<quote>'"
SHEET 2: "Summary"
- Total documents: <count>
- Documents processed: <count>
- Extraction date: <today>
Extraction result format:
{
"document": "Contract_ABC.pdf",
"path": "/path/to/Contract_ABC.pdf",
"extractions": [
{
"column": "Parties",
"value": "Acme Corp and Beta Inc",
"page": 1,
"quote": "entered into between Acme Corp and Beta Inc"
},
{
"column": "Effective Date",
"value": "January 15, 2025",
"page": 1,
"quote": "effective as of January 15, 2025"
}
]
}
Cell with citation:
file:///path/to/Contract_ABC.pdf#page=1Page 1: "entered into between Acme Corp and Beta Inc"Color coding (optional):
| Scenario | Action |
|---|---|
| Document unreadable | Log error, mark row as failed, continue |
| Column not found | Set value to "Not found", explain in comment |
| Agent timeout | Collect partial results, note incomplete |
| Missing skill | Prompt user to install required skill |
User: I want to do a tabular review of my contracts
Claude: [Uses AskUserQuestion]
- What folder contains your documents?
- What should I name the output Excel file?
- What columns do you want to extract?
User: ~/Contracts, review.xlsx, Parties/Date/Term/Governing Law
Claude: [Discovers 15 documents via Glob]
Claude: [Launches 5 background agents, 3 docs each]
Claude: [Collects results via TaskOutput]
Claude: [Creates review.xlsx via xlsx skill]
Output: review.xlsx with 15 rows, 4 columns, hyperlinks and citations
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Creates an Agent Authority Charter for enterprise or regulated AI agents before deployment. Use this Skill when a user needs to define what an AI agent is allowed to do, who delegated authority to it, what actions are permitted or prohibited, when human approval is required, what evidence must be preserved, and how the agent can be suspended, revoked, or escalated.
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。
EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.
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。
This skill builds a structured audit trail of an AI-assisted task: what the tool was asked to do, what materials it was given, what it produced, how the output was verified, and what was ultimately relied upon. It documents the workflow for supervision and later review without ruling on privilege or disclosure, so the record stays accurate rather than self-serving.
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。
Use this skill when the user wants an AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk review of an internal AI use case, an AI product feature, an LLM workflow, or a third-party AI vendor. The skill asks intake and clarifying questions first when facts or evidence are missing, identifies required documentation and missing evidence, maps the use case to AI governance frameworks and applicable legal domains, and produces a preliminary or final governance review with scorecards, findings, owners, remediation actions, and follow-up questions.
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。
Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable. Produces a multi-page website (default), a single-page interactive site, a Microsoft Word document, a PowerPoint deck, or a LaTeX/PDF. Use whenever a user has a stress-test result, ambiguity audit, or "where will this be litigated" scenarios and wants to publish, present, or share them. Triggers on "publish the audit," "make a report from this," "turn this into a website / deck / brief / Word doc," "make a Netlify site," "build slides of these ambiguities," "render the scenarios," "package this stress test," "produce a LaTeX version," and similar. Pairs with the ambiguity-stress-test skill (the underlying analysis) but works on any structured or semi-structured ambiguity analysis. Visual design is opinionated but the user can override colors, fonts, and brand label. Most formats need Python and a writable filesystem; single-file HTML works where the host has neither.
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。
Adversarially stress-tests a legal text — a contract, statute, regulation, or judicial opinion — for interpretive ambiguity: it finds the seams where the people governed by it will later disagree about what it means and turns each into a concrete dispute scenario with both sides' arguments, the likely outcome, and a fix. Use it whenever someone wants to pressure-test, red-team, audit, or find weak spots, loopholes, gaps, ambiguities, or drafting problems in a legal document; whenever a drafter wants to tighten a contract, statute, regulation, or opinion before it issues; whenever a litigator wants to mine an opinion or contract for arguments; or whenever someone hands over a legal text and asks where it will be fought over or for issue-spotting. Trigger it for contract review, statutory-ambiguity analysis, judicial-opinion scope analysis, and drafting QA — even if the user never says "stress-test" or "ambiguity."
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。