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document-analysis

Name: Document Analysis
Author: LiboMa

// Read and analyze documents — PDF, DOCX, Markdown, HTML, CSV, XLSX, JSON, YAML. Provides read_document tool with no output truncation and page-range support for PDFs. Use when the user shares a document or asks to explain, summarize, or extract information from files.

Exécuter dans Manus

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stars:3

forks:1

updated:26 mars 2026 à 08:20

SKILL.md

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name	document-analysis
description	Read and analyze documents — PDF, DOCX, Markdown, HTML, CSV, XLSX, JSON, YAML. Provides read_document tool with no output truncation and page-range support for PDFs. Use when the user shares a document or asks to explain, summarize, or extract information from files.
metadata	{"author":"agenticops","version":"1.0","domain":"operations"}
tools	["agenticops.tools.file_tools.read_document"]

Document Analysis Skill

Overview

When this skill is activated, the read_document tool is dynamically registered on the agent:

Tool	Purpose	Key Args
`read_document`	Read full document content (no truncation)	`path`, `pages`

Unlike read_local_file (which truncates at 4K chars for operational safety), read_document returns the complete content so you can fully understand and explain the document.

Supported Formats

Format	Library	Notes
PDF	pymupdf or pypdf	Page-range support (`pages="1-5"`)
DOCX	python-docx	Full paragraph extraction
Markdown	built-in	Full content, no truncation
HTML	built-in	Full content, no truncation
CSV	built-in	Full content, no truncation
JSON/YAML	built-in	Full content, no truncation
XLSX	openpyxl	Multi-sheet, all rows

Quick Decision Trees

User Shares a Document

User provides a document (via @path or upload)
  |
  +-- Already injected as context?
  |     +-- Yes (attached via @path or web upload) → analyze directly
  |     +-- No (user mentions a file path) → read_document(path="...")
  |
  +-- Document too large?
  |     +-- PDF: use pages="1-5" to read in chunks
  |     +-- Other: summarize what you can, note truncation
  |
  +-- What does the user want?
        +-- "Explain this" → structured summary
        +-- "Summarize" → executive summary (key points + conclusions)
        +-- "Find X in this" → targeted extraction
        +-- "Compare with Y" → side-by-side analysis

Analyzing PDF Reports

PDF document received
  |
  +-- Large (>10 pages)?
  |     +-- Start with read_document(path, pages="1-3") for overview
  |     +-- Then read specific sections as needed
  |
  +-- What type of document?
  |     +-- Architecture/design doc → focus on components, data flow, decisions
  |     +-- Incident report → focus on timeline, root cause, remediation
  |     +-- Compliance/audit → focus on findings, risk level, recommendations
  |     +-- Cost report → focus on top spenders, trends, anomalies
  |     +-- Runbook/SOP → focus on steps, prerequisites, rollback
  |
  +-- Output format?
        +-- Brief: 3-5 bullet points
        +-- Detailed: section-by-section breakdown
        +-- Actionable: extract TODOs and next steps

Analyzing Spreadsheets

CSV or XLSX received
  |
  +-- Understand structure first
  |     +-- Column headers, row count, data types
  |
  +-- What does the user want?
        +-- "What's in this?" → schema + sample rows + summary stats
        +-- "Find anomalies" → look for outliers, missing data, spikes
        +-- "Trends" → time-series patterns if date column exists
        +-- "Top N" → sort/rank by a metric column

Analysis Workflow

Step 1: Read the Document

read_document(path="/path/to/report.pdf")
# or with page range for large PDFs:
read_document(path="/path/to/report.pdf", pages="1-5")

Step 2: Identify Structure

Document type (report, spec, spreadsheet, log dump)
Key sections / headings
Tables, figures, or data present

Step 3: Analyze Based on User Intent

Explain: Walk through each section, clarify technical terms
Summarize: Extract key findings, conclusions, action items
Extract: Pull specific data points the user asked about
Compare: Side-by-side with another document or known state

Step 4: Present Findings

Lead with the answer, not the process
Use structured format (headers, bullets, tables)
Quote specific passages when relevant
Note any limitations (scanned PDF with no text, truncated content)

Tool Reference Quick Card

Example	Description
`read_document(path="report.pdf")`	Read entire PDF
`read_document(path="report.pdf", pages="1-3")`	Read pages 1-3 only
`read_document(path="report.pdf", pages="5")`	Read page 5 only
`read_document(path="spec.docx")`	Read Word document
`read_document(path="data.csv")`	Read CSV file
`read_document(path="metrics.xlsx")`	Read Excel workbook

Edge Cases

Scanned PDFs (image-only): text extraction will return empty — inform the user that OCR is needed
Password-protected PDFs: will fail — ask user to provide an unprotected copy
Very large spreadsheets: output is truncated to 6000 chars — suggest filtering or specifying columns of interest
Mixed content PDFs (text + images): only text is extracted — note that charts/diagrams are not visible

related-skills.json

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package.json

"author": "LiboMa"

"repository": "LiboMa/agenticops-chat"

Ouvrir le dépôt GitHub Voir les dépôts du créateur

$ install --global

$ download --local

Exécuter dans Manus

$ useful --forSOC

Commis à la correspondanceBureau et soutien administratif43-4021L4

name	document-analysis
description	Read and analyze documents — PDF, DOCX, Markdown, HTML, CSV, XLSX, JSON, YAML. Provides read_document tool with no output truncation and page-range support for PDFs. Use when the user shares a document or asks to explain, summarize, or extract information from files.
metadata	{"author":"agenticops","version":"1.0","domain":"operations"}
tools	["agenticops.tools.file_tools.read_document"]

Document Analysis Skill

Overview

When this skill is activated, the read_document tool is dynamically registered on the agent:

Tool	Purpose	Key Args
`read_document`	Read full document content (no truncation)	`path`, `pages`

Unlike read_local_file (which truncates at 4K chars for operational safety), read_document returns the complete content so you can fully understand and explain the document.

Supported Formats

Format	Library	Notes
PDF	pymupdf or pypdf	Page-range support (`pages="1-5"`)
DOCX	python-docx	Full paragraph extraction
Markdown	built-in	Full content, no truncation
HTML	built-in	Full content, no truncation
CSV	built-in	Full content, no truncation
JSON/YAML	built-in	Full content, no truncation
XLSX	openpyxl	Multi-sheet, all rows

Quick Decision Trees

User Shares a Document

User provides a document (via @path or upload)
  |
  +-- Already injected as context?
  |     +-- Yes (attached via @path or web upload) → analyze directly
  |     +-- No (user mentions a file path) → read_document(path="...")
  |
  +-- Document too large?
  |     +-- PDF: use pages="1-5" to read in chunks
  |     +-- Other: summarize what you can, note truncation
  |
  +-- What does the user want?
        +-- "Explain this" → structured summary
        +-- "Summarize" → executive summary (key points + conclusions)
        +-- "Find X in this" → targeted extraction
        +-- "Compare with Y" → side-by-side analysis

Analyzing PDF Reports

PDF document received
  |
  +-- Large (>10 pages)?
  |     +-- Start with read_document(path, pages="1-3") for overview
  |     +-- Then read specific sections as needed
  |
  +-- What type of document?
  |     +-- Architecture/design doc → focus on components, data flow, decisions
  |     +-- Incident report → focus on timeline, root cause, remediation
  |     +-- Compliance/audit → focus on findings, risk level, recommendations
  |     +-- Cost report → focus on top spenders, trends, anomalies
  |     +-- Runbook/SOP → focus on steps, prerequisites, rollback
  |
  +-- Output format?
        +-- Brief: 3-5 bullet points
        +-- Detailed: section-by-section breakdown
        +-- Actionable: extract TODOs and next steps

Analyzing Spreadsheets

CSV or XLSX received
  |
  +-- Understand structure first
  |     +-- Column headers, row count, data types
  |
  +-- What does the user want?
        +-- "What's in this?" → schema + sample rows + summary stats
        +-- "Find anomalies" → look for outliers, missing data, spikes
        +-- "Trends" → time-series patterns if date column exists
        +-- "Top N" → sort/rank by a metric column

Analysis Workflow

Step 1: Read the Document

read_document(path="/path/to/report.pdf")
# or with page range for large PDFs:
read_document(path="/path/to/report.pdf", pages="1-5")

Step 2: Identify Structure

Document type (report, spec, spreadsheet, log dump)
Key sections / headings
Tables, figures, or data present

Step 3: Analyze Based on User Intent

Explain: Walk through each section, clarify technical terms
Summarize: Extract key findings, conclusions, action items
Extract: Pull specific data points the user asked about
Compare: Side-by-side with another document or known state

Step 4: Present Findings

Lead with the answer, not the process
Use structured format (headers, bullets, tables)
Quote specific passages when relevant
Note any limitations (scanned PDF with no text, truncated content)

Tool Reference Quick Card

Example	Description
`read_document(path="report.pdf")`	Read entire PDF
`read_document(path="report.pdf", pages="1-3")`	Read pages 1-3 only
`read_document(path="report.pdf", pages="5")`	Read page 5 only
`read_document(path="spec.docx")`	Read Word document
`read_document(path="data.csv")`	Read CSV file
`read_document(path="metrics.xlsx")`	Read Excel workbook

Edge Cases

Scanned PDFs (image-only): text extraction will return empty — inform the user that OCR is needed
Password-protected PDFs: will fail — ask user to provide an unprotected copy
Very large spreadsheets: output is truncated to 6000 chars — suggest filtering or specifying columns of interest
Mixed content PDFs (text + images): only text is extracted — note that charts/diagrams are not visible

document-analysis

Document Analysis Skill

Overview

Supported Formats

Quick Decision Trees

User Shares a Document

Analyzing PDF Reports

Analyzing Spreadsheets

Analysis Workflow

Step 1: Read the Document

Step 2: Identify Structure

Step 3: Analyze Based on User Intent

Step 4: Present Findings

Tool Reference Quick Card

Edge Cases

Plus depuis ce dépôt

Plus depuis ce dépôt

Document Analysis Skill

Overview

Supported Formats

Quick Decision Trees

User Shares a Document

Analyzing PDF Reports

Analyzing Spreadsheets

Analysis Workflow

Step 1: Read the Document

Step 2: Identify Structure

Step 3: Analyze Based on User Intent

Step 4: Present Findings

Tool Reference Quick Card

Edge Cases