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 agentmods add skills/docxology/template/documentationnpx skills add docxology/template --skill documentationgit clone --depth 1 https://github.com/docxology/templateWhat 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 | $0.00047 | $0.00739 |
| Opus 5 | $0.00023 | $0.00369 |
| Sonnet 5 | $0.00009 | $0.00148 |
| Haiku 4.5 | $0.00005 | $0.00074 |
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.
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 — 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")
What ships with it
25 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 1.2 KB runs code
- _coverage_workspace.py 24 KB runs code
- _publication_records_check.py 7.6 KB runs code
- _publication_records_external.py 5.1 KB runs code
- _publication_records_load.py 6.3 KB runs code
- _publication_records_render.py 9.7 KB runs code
- _publication_records_types.py 5.4 KB runs code
- active_projects_doc.py 2.0 KB runs code
- AGENTS.md 6.0 KB
- api_reference_gen.py 20 KB runs code
- architecture_overview.py 15 KB runs code
- backlog_normalizer.py 12 KB runs code
- backlog.py 17 KB runs code
- counts_coverage.py 32 KB runs code
- counts_doc.py 24 KB runs code
- figure_manager.py 9.1 KB runs code
- generate_glossary_cli.py 3.0 KB runs code
- generated_figure_registry.py 6.4 KB runs code
- glossary_gen.py 4.2 KB runs code
- image_manager.py 7.7 KB runs code
- markdown_integration.py 7.6 KB runs code
- publication_records.py 2.0 KB runs code
- publication_standalone.py 3.4 KB runs code
- README.md 24 KB
- stage_table.py 12 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.
- 2d ago First seen · 112 lines · 47 tokens per session scan A 068c71292f7f
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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