Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/docxology/templatenpx agentmods add skills/docxology/template/validationWrote 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/docxology/template/validation)<a href="https://agentmods.dev/skills/docxology/template/validation"><img src="https://agentmods.dev/badge/skills/docxology/template/validation/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/docxology/template/validation"><img src="https://agentmods.dev/badge/skills/docxology/template/validation.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 | $0.01411 |
| Opus 5.5 | $0.00022 | $0.00564 |
| Sonnet 5.5 | $0.00011 | $0.00282 |
| Haiku 4.5 | $0.00005 | $0.00141 |
Grade A, and why
infrastructure-validation 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 10d 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validation Module
Quality assurance and content validation tools for research outputs. Covers PDFs, markdown, links, output integrity, and comprehensive audits.
PDF Validation (content/pdf_validator.py)
from infrastructure.validation import validate_pdf_rendering, extract_text_from_pdf, scan_for_issues
# Validate a rendered PDF
results = validate_pdf_rendering(pdf_path)
# Extract text for analysis
text = extract_text_from_pdf(pdf_path)
# Scan for rendering issues
issues = scan_for_issues(text)
CLI:
uv run python -m infrastructure.validation.cli pdf --project {project} --repo-root .
Markdown Validation (markdown_validator.py)
from pathlib import Path
from infrastructure.validation.content.discovery import discover_markdown_files
from infrastructure.validation.content.markdown_validator import (
collect_symbols,
validate_images,
validate_markdown,
validate_math,
validate_refs,
)
repo_root = Path(".")
manuscript_dir = repo_root / "projects" / "project" / "manuscript"
md_files = [str(path) for path in discover_markdown_files(manuscript_dir, scope="tree")]
labels, anchors = collect_symbols(md_files)
# Validate all markdown in a directory
problems, exit_code = validate_markdown(manuscript_dir, repo_root)
# Individual checks
image_issues = validate_images(md_files, repo_root)
ref_issues = validate_refs(md_files, repo_root, labels, anchors)
math_issues = validate_math(md_files, repo_root)
CLI:
uv run python -m infrastructure.validation.cli markdown projects/{name}/manuscript/
Output Integrity (integrity/checks.py)
from infrastructure.validation import (
verify_output_integrity, verify_file_integrity,
verify_cross_references, verify_data_consistency,
verify_academic_standards, generate_integrity_report,
)
# Full integrity check
report = verify_output_integrity(output_dir)
# Individual checks
verify_file_integrity(file_path)
verify_cross_references(manuscript_dir)
verify_data_consistency(data_dir)
verify_academic_standards(manuscript_dir)
What ships with it
60 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.
- __init__.py 4.4 KB runs code
- AGENTS.md 5.1 KB
- claims.py 16 KB runs code
- cli/__init__.py 105 B runs code
- cli/__main__.py 147 B runs code
- cli/AGENTS.md 4.2 KB
- cli/main.py 19 KB runs code
- cli/README.md 409 B
- content/__init__.py 859 B runs code
- content/AGENTS.md 8.3 KB
- content/ai_writing.py 7.9 KB runs code
- content/diagnostic_codes.py 1.6 KB runs code
- content/discovery.py 2.9 KB runs code
- content/figure_validator.py 10 KB runs code
- content/markdown_strip.py 1.5 KB runs code
- content/markdown_validator.py 3.1 KB runs code
- content/pdf_discovery.py 1.2 KB runs code
- content/pdf_reporting.py 2.0 KB runs code
- content/pdf_validator.py 11 KB runs code
- content/prerender.py 2.4 KB runs code
- content/README.md 341 B
- content/symbols.py 3.6 KB runs code
- content/validator_citations.py 4.0 KB runs code
- content/validator_images.py 2.5 KB runs code
- content/validator_math.py 4.4 KB runs code
- content/validator_pitfalls.py 3.3 KB runs code
- content/validator_refs.py 3.8 KB runs code
- docs/__init__.py 1.2 KB runs code
- docs/_docs_scan_report.py 3.9 KB runs code
- docs/_io.py 757 B runs code
- docs/accuracy.py 17 KB runs code
- docs/AGENTS.md 6.5 KB
- docs/completeness.py 9.0 KB runs code
- docs/consistency_lint.py 1.2 KB runs code
- docs/consistency/__init__.py 1.3 KB runs code
- docs/consistency/_shared.py 4.4 KB runs code
- docs/consistency/AGENTS.md 1.1 KB
- docs/consistency/ghost_paths.py 4.9 KB runs code
- docs/consistency/import_resolution.py 5.8 KB runs code
- docs/consistency/memory_decision.py 3.0 KB runs code
- docs/consistency/package_counts.py 5.1 KB runs code
- docs/consistency/project_discovery.py 2.3 KB runs code
- docs/consistency/readme_inventory.py 1.9 KB runs code
- docs/consistency/README.md 401 B
- docs/consistency/shell_contracts.py 6.5 KB runs code
- docs/cross_link_lint.py 17 KB runs code
- docs/discovery.py 15 KB runs code
- docs/doc_pair_lint.py 6.7 KB runs code
- docs/lint_runner.py 12 KB runs code
- docs/mermaid_lint.py 23 KB runs code
- docs/models.py 2.2 KB runs code
- docs/module_coverage.py 4.5 KB runs code
- docs/public_audit.py 24 KB runs code
- docs/quality.py 9.9 KB runs code
- docs/README.md 2.0 KB
- docs/scan_scope.py 5.0 KB runs code
- docs/scanner.py 8.8 KB runs code
- docs/verification.py 3.6 KB runs code
- evidence_registry_collectors.py 16 KB runs code
- evidence_registry.py 24 KB runs code
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.
- 10d ago Changed 56e607dc98cc
- 1mo ago First seen · 209 lines · 54 tokens per session scan A 4a8e1aa82d58
infrastructure-validation is a skill published in the GitHub repository docxology/template (20 stars, last pushed 2d ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,411 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
paper-figures
Extract figures from downloaded papers and include them in survey/review reports. Use when the report covers other groups' work and benefits from their architecture diagrams, experimental plots, or system schematics. Your brain prompt supplies the absolute path to the extract-figures script as {{EXTRACTFIGURES}} — use…
taxonomy-vto
A reference guide for diagnosing visual problems in LaTeX research papers. It defines categories such as wasted page space, misplaced figures, inconsistent formatting, overflow, alignment, and template-migration issues.
beamer-deck
Create an academic presentation as a LaTeX Beamer source and reviewed PDF with an original theme. Use when the requested deliverable is a conference, seminar, or lecture deck in Beamer. Not for PowerPoint or RevealJS; use $pptx or $quarto-deck.
bib-parse
Extract citations from a PDF and generate a validated .bib file. Use when the user asks to extract citations from a PDF and generate a validated .bib file. Reads the PDF, identifies referenced works, constructs BibTeX entries, and verifies metadata.
latex-health-check
Compile all LaTeX projects and report cross-project build consistency. Use when checking whether a collection of papers builds cleanly. Not for rendered visual inspection after a clean build; use $latex-polish.
latex-polish
Inspect a cleanly compiling LaTeX document for source pathologies and rendered visual defects by linting and viewing selected PDF pages. Use when compilation succeeds but title pages, floats, tables, figures, or layout still need publication-quality review. Not for basic compilation health; use $latex-health-check.