Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/skills/ssmurfgg04-gif/context-m/pdf)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/pdf"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/pdf/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/ssmurfgg04-gif/context-m/pdf"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/pdf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00145 | $0.13695 |
| Opus 5 | $0.00072 | $0.06848 |
| Sonnet 5 | $0.00029 | $0.02739 |
| Haiku 4.5 | $0.00015 | $0.01370 |
Grade A, and why
pdf 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 7d 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 — 880 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF - Document Production Workbench
Script Path Setup (MANDATORY before any script call)
All paths are relative to $PDF_SKILL_DIR. Resolve it once before calling any script:
PDF_SKILL_DIR="<skill_directory>" # ← parent directory of this SKILL.md
For Python imports (when generation code needs to import skill modules):
import sys, os
PDF_SKILL_DIR = "<skill_directory>"
_scripts = os.path.join(PDF_SKILL_DIR, "scripts")
if _scripts not in sys.path:
sys.path.insert(0, _scripts)
Triage
Determine task weight to control how much context to load:
| Weight | Triggers | What to Load |
|---|---|---|
| Light | Format conversion, form fill, text extract, merge/split, simple certificate | SKILL.md + briefs/process.md only |
| Standard | Multi-page report, poster, academic paper, resume, reformat - any document with design decisions | SKILL.md + configs/fonts.md + matched brief + ALL files referenced by the brief (typesetting, configs, etc.) |
⚠️ Pre-Routing Checks (run BEFORE matching brief)
- Emoji Check - Scan user content for intentional emoji (decorative 📊🎯🔥, not OS-level emoji input). If found → force Creative pipeline (Fixed-Canvas or Flow depending on document type) regardless of original routing. ReportLab renders emoji as □ squares; LaTeX drops them entirely.
- CJK Check - Chinese/Japanese/Korean content needs font coverage. Report brief must register CJK fonts - probe first with
ls /usr/share/fonts/truetype/chinese/(Linux) or check$FONT_DIR(macOS) to confirm which fonts exist, then register accordingly (prefer NotoSerifSC > Noto Sans SC; never hardcode a font name without verifying it exists). Creative Fixed-Canvas and Creative Flow briefs must load Google Fonts Noto Sans SC withfont-display: swap; Academic brief must use\usepackage{ctex}. - Size Check - Non-standard page sizes (not A4/Letter/A3) → prefer Creative brief (Playwright handles any dimension). ReportLab can do custom sizes but pagination is manual.
- Character Safety Check - Before writing any content string, scan for Japanese kana (の、が、は etc.), unusual Unicode symbols, or non-CJK characters that may corrupt during encoding transit ( Especially when code is written via heredoc/base64/LLM output). Replace with plain Chinese equivalents:
の→之/的/缔,々→omit or write full character. If content must preserve Japanese, use only standard CJK Unified Ideographs (U+4E00-U+9FFF) and common kana; avoid rare/private-use codepoints.
What ships with it
37 files 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.
- briefs/academic.md 46 KB
- briefs/creative-fixed-canvas.md 48 KB
- briefs/creative-flow.md 10 KB
- briefs/creative.md 46 KB
- briefs/poster.md 33 KB
- briefs/process.md 14 KB
- briefs/report.md 75 KB
- briefs/resume.md 10 KB
- configs/components.md 5.1 KB
- configs/fonts.md 4.2 KB
- configs/visual_framework.md 13 KB
- env_setup/env_check.sh 1.7 KB runs code
- env_setup/font_list.txt 4.7 KB
- env_setup/setup_mac_linux.sh 11 KB runs code
- env_setup/setup_windows.ps1 11 KB runs code
- env_setup/setup.md 7.0 KB
- LICENSE.txt 745 B
- references/resume-academic.tex 4.7 KB
- references/resume-altacv.tex 6.0 KB
- scripts/cover_validate.js 22 KB runs code
- scripts/design_engine.py 121 KB runs code
- scripts/html2pdf-next.js 34 KB runs code
- scripts/html2poster.js 8.7 KB runs code
- scripts/pdf_qa.py 42 KB runs code
- scripts/pdf.py 105 KB runs code
- scripts/poster_validate.py 61 KB runs code
- scripts/setup.sh 11 KB runs code
- scripts/toc_validate.py 77 KB runs code
- typesetting/charts.md 12 KB
- typesetting/cover-backgrounds.md 16 KB
- typesetting/cover.md 46 KB
- typesetting/fill-engine.md 20 KB
- typesetting/geometry.md 5.5 KB
- typesetting/overflow.md 22 KB
- typesetting/pagination.md 13 KB
- typesetting/palette.md 9.1 KB
- typesetting/typography.md 1.0 KB
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.
- 7d ago First seen · 880 lines · 145 tokens per session scan A 914d58c329fc
pdf is a skill published in the GitHub repository ssmurfgg04-gif/context-m (1 stars, last pushed yesterday), licensed Apache-2.0. It adds 145 tokens to every session and 13,695 once invoked, about $0.0007 per session on Opus 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.
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pptx
A guide for turning a paper, outline, or structured text into a PowerPoint presentation. It covers an 8–12-slide structure, concise slide points, and generation of a .pptx file.
pdf-reader
Extract text from PDF files. Use when reading, parsing, or analyzing PDFs.
ov-add-paper
Load when the user asks to add, import, compile, or ingest a research paper/PDF into OpenViking, especially when they mention ov-add-paper, ARA, claims, evidence, figures, tables, or paper-to-OV knowledge resources.
ocr-document
Extract text from PDFs, images, and scanned documents. Uses pymupdf (local) or optional cloud OCR APIs.
liteparse
Parse documents into LLM-ready content entirely on the local machine — PDF / DOCX / XLSX / PPTX / images → Markdown, structured JSON (with bounding boxes), or page screenshots, via the lit CLI. No cloud, no LLM, works offline. Use whenever the user attaches or points to a document that must be read before reasoning…