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.
npx skills add Zhang-Henry/CoEvoSkills --skill evo-pdf-anonymizergit clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkillsWrote 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/zhang-henry/coevoskills/evo-pdf-anonymizer)<a href="https://agentmods.dev/skills/zhang-henry/coevoskills/evo-pdf-anonymizer"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-pdf-anonymizer/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/zhang-henry/coevoskills/evo-pdf-anonymizer"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-pdf-anonymizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00727 |
| Opus 5 | $0.00027 | $0.00364 |
| Sonnet 5 | $0.00011 | $0.00145 |
| Haiku 4.5 | $0.00005 | $0.00073 |
Grade A, and why
evo-pdf-anonymizer 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 12d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Anonymization Skill
Overview
Anonymize academic papers by precisely redacting identity-revealing strings while preserving all scientific content. Uses PyMuPDF's search_for + add_redact_annot workflow for true PDF redaction.
Workflow
- Discovery: Extract text from each PDF page-by-page. Read author blocks, affiliations, emails, acknowledgement sections, headers/footers, and arXiv watermarks. Build an explicit list of strings to redact.
- Redaction: Apply string-level redaction using PyMuPDF. Each redaction targets a specific discovered string.
- Verification: Extract text from the redacted PDF and confirm all target strings are removed, page count preserved, and metadata cleaned.
Usage
import sys, os
sys.path.insert(0, '/app/environment/skills/evo-pdf-anonymizer/scripts')
from utils import run_anonymization, extract_text, find_references_start_page
# Discover input PDFs at runtime
input_dir = '/path/to/inputs'
output_dir = '/path/to/outputs'
os.makedirs(output_dir, exist_ok=True)
# For each PDF, extract text, read it, and build a redaction list
pages = extract_text(os.path.join(input_dir, 'paper.pdf'))
refs_page = find_references_start_page(pages)
# Build redaction list from discovered content:
# - Read page 0 for author block, affiliations, emails
# - Scan all pages before refs for arXiv IDs, venue headers, GitHub URLs
# - Read acknowledgements section for person names
# - Check PDF metadata for author names
redaction_list = [
{'text': 'Discovered Author Name', 'pages': 'before_refs'},
{'text': 'Discovered University', 'pages': 'before_refs'},
{'text': '[email protected]', 'pages': 'all'},
{'text': 'arXiv:YYMM.NNNNN', 'pages': 'before_refs'},
]
issues = run_anonymization(
os.path.join(input_dir, 'paper.pdf'),
os.path.join(output_dir, 'paper.pdf'),
redaction_list,
refs_page
)
if issues:
for issue in issues:
print(f"WARNING: {issue}")
Redaction List Format
Each entry is a dict with:
text: The exact string to search for and redactpages: Scope —'all'(every page),'before_refs'(pages before References section), or a list of 0-based page indices
What ships with it
1 file 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.
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.
- 12d ago First seen · 70 lines · 54 tokens per session scan A 250206a41734
evo-pdf-anonymizer is a skill published in the GitHub repository Zhang-Henry/CoEvoSkills (66 stars, last pushed 22d ago), licensed Apache-2.0. It adds 54 tokens to every session and 727 once invoked, about $0.0003 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 2026-08-30.
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