infrastructure-documentation

Documentation tools for research projects that manage figures, images, Markdown integration, and API glossaries.

In plain words
What is it for?
Registering and publishing figures, tracking captions and labels, inserting images into manuscripts, and generating API documentation.
Why use it?
They reduce manual work and prevent documentation errors, such as missing figure files or inconsistent numbering and references.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/docxology/template/documentation
Any agent
npx skills add docxology/template --skill documentation
Clone the repo
git clone --depth 1 https://github.com/docxology/template

Made for: Claude Code, Codex.

Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 739 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00047 $0.00739
Opus 5 $0.00023 $0.00369
Sonnet 5 $0.00009 $0.00148
Haiku 4.5 $0.00005 $0.00074

Measured 2d ago against content hash 068c71292f7f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

infrastructure-documentation 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 2d ago.

The scan reads SKILL.md. This mod also ships 23 executable files (__init__.py, _coverage_workspace.py, _publication_records_check.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

infrastructure/documentation/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Documentation Module

Figure management, image handling, and documentation integration for research manuscripts.

GeneratedFigureRegistry (generated_figure_registry.py)

Use the fail-closed writer for deterministic project pipelines. Figure labels, filenames, captions, and qualified generator names remain project-owned; the shared writer verifies that every declared file exists before atomically writing output/figures/figure_registry.json.

from infrastructure.documentation import publish_generated_figures

written = publish_generated_figures(
    output_dir,
    PROJECT_FIGURE_SPECS,
    generated_paths,
    schema_version="my-project-figure-registry-v1",
)

FigureManager (figure_manager.py)

Automatic figure numbering, cross-referencing, and metadata tracking:

from infrastructure.documentation import FigureManager, FigureMetadata

manager = FigureManager()

# Register a figure (label auto-generated from filename if omitted)
manager.register_figure(
    filename="results.png",
    caption="Experimental results showing...",
    label="fig:results",
    section="Results",
)

# Get figure metadata by label
meta = manager.get_figure("fig:results")  # Returns FigureMetadata | None

ImageManager (image_manager.py)

Image file management and processing:

from pathlib import Path
from infrastructure.documentation import ImageManager, FigureManager

img_manager = ImageManager(FigureManager())

# Insert a registered figure into a markdown file under a section
img_manager.insert_figure(Path("manuscript/01_intro.md"), "fig:results", section="Results")

# Validate that referenced figures exist and labels are registered
errors = img_manager.validate_figures(Path("manuscript/01_intro.md"))  # list[(label, error)]

MarkdownIntegration (markdown_integration.py)

Auto-insertion of figures and content into markdown manuscripts:

from pathlib import Path
from infrastructure.documentation import MarkdownIntegration, FigureManager

integrator = MarkdownIntegration(Path("manuscript"), FigureManager())

# Insert a figure into a named section of a markdown file
integrator.insert_figure_in_section(Path("manuscript/01_intro.md"), "fig:results", "Results")

Read the full file on GitHub · 112 lines

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. 2d ago First seen · 112 lines · 47 tokens per session scan A 068c71292f7f

Subscribe to this mod's changes

infrastructure-documentation is a skill published in the GitHub repository docxology/template (19 stars, last pushed 2d ago), licensed Apache-2.0. It adds 47 tokens to every session and 739 once invoked, about $0.0002 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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