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Skill for the documentation infrastructure module providing figure management, image handling, markdown integration, and API glossary generation.
Passed 21 of 22 test cases with the skill, and 3 of 22 without it.
and 22 more in the full report
18 fixed3 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 18 test cases the skill fixed. Same model, same prompt, with and without the skill.
What target source directory and manuscript destination path are used when infrastructure.documentation.generate_glossary_cli is executed with no command line arguments?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies <repo>/project/src as the default source directory when no arguments are provided. | Passed | Passed |
| Identifies <repo>/project/manuscript/98_symbols_glossary.md as the default destination path when no arguments are provided. | Missed | Passed |
The instructions your model reads when the skill loads.
Figure management, image handling, and documentation integration for research manuscripts.
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",
)
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
test cases passed, vs 3 / 22 without
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