document-material-ingestion

document-material-ingestion is a skill for Claude Code, Codex from BingHanOfUESTC/open_agent_team. It costs 0 tokens per session (336 once invoked), scanned A, original, MIT.

A workflow for extracting usable facts, arguments, tables, and source locations from documents supplied for a PowerPoint project. It supports files such as Word documents, spreadsheets, PDFs, presentations, text files, and images.

In plain words
What is it for?
Use it to prepare evidence tables and slide-ready material from mixed-format project files.
Why use it?
It saves time when source material is spread across different file types and helps keep claims tied to their original locations.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to prepare evidence tables and slide-ready material from mixed-format project files.

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Install with agentmods
npx agentmods add skills/binghanofuestc/open_agent_team/document-material-ingestion
Install

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.

Any agent
npx skills add BingHanOfUESTC/open_agent_team --skill document-material-ingestion
Clone the repo
git clone --depth 1 https://github.com/BingHanOfUESTC/open_agent_team

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for document-material-ingestion

README.md
[![agentmods](https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/document-material-ingestion.svg)](https://agentmods.dev/skills/binghanofuestc/open_agent_team/document-material-ingestion)
Your own site
<a href="https://agentmods.dev/skills/binghanofuestc/open_agent_team/document-material-ingestion"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/document-material-ingestion.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 336 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.00336
Opus 5 $0.00000 $0.00168
Sonnet 5 $0.00000 $0.00067
Haiku 4.5 $0.00000 $0.00034

Measured 4d ago against content hash c1f3a59d568d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

document-material-ingestion 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 4d 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.

teams/ppt_writer_team/skills/document-material-ingestion/SKILL.md · 62 lines

What it actually says

Document Material Ingestion

Use this skill when the team must turn Boss-provided mixed-format documents into PPT-ready facts, arguments, data tables, and source traces.

Inputs

Boss brief
File paths
Target audience
PPT goal
Required slides or topics

Supported Strategy

doc/docx:
  Extract heading structure, paragraphs, tables, executive conclusions, definitions.

xls/xlsx/csv:
  Extract workbook/sheet names, table ranges, fields, time periods, metrics, units, missing values.

pdf:
  Extract page-level text, headings, tables if possible, figure captions, referenced numbers.

ppt/pptx:
  If material deck: extract slide titles, body, notes, charts, tables.
  If template deck: route to template_decoder_agent and do not use content as source claims.

image files:
  Inventory jpg/jpeg/png/webp/bmp/tiff assets with path, apparent topic, dimensions if available and suggested slide use.

txt/md:
  Extract headings, lists, tables, quoted blocks, code blocks, links.

Evidence Table Schema

| id | claim_or_data | source_file | location | confidence | suggested_slide_use |

Rules

Keep source position whenever possible.
Preserve units, dates, denominator, geography, sample size and assumptions.
Separate facts from interpretation.
Mark unreadable files explicitly.
Never invent data to fill missing tables.

Output Files

materials/material_inventory.md
materials/evidence_table.md
materials/data_tables.md
materials/images/image_manifest.json when image assets are prepared
Changes

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.

  1. 4d ago First seen · 62 lines · 0 tokens per session scan A c1f3a59d568d

Subscribe to this mod's changes

document-material-ingestion is a skill published in the GitHub repository BingHanOfUESTC/open_agent_team (109 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 336 tokens. 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.