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 docxology/template --skill pipelinegit clone --depth 1 https://github.com/docxology/templateWrote 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/pipeline)<a href="https://agentmods.dev/skills/docxology/template/pipeline"><img src="https://agentmods.dev/badge/skills/docxology/template/pipeline/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/pipeline"><img src="https://agentmods.dev/badge/skills/docxology/template/pipeline.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 | $0.00052 | $0.00665 |
| Opus 5.5 | $0.00021 | $0.00266 |
| Sonnet 5.5 | $0.00010 | $0.00133 |
| Haiku 4.5 | $0.00005 | $0.00067 |
Grade A, and why
template-pipeline 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 7d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
template-pipeline
Numbered pipeline stage scripts in scripts/pipeline/.
When to use
Load this skill when you need to:
- Run or debug a specific pipeline stage
- Add a new stage script
- Understand the stage → infrastructure module mapping
- Work with opt-in stages (08 connector_search, 09 provenance_record, 10 research_workflow)
Stage inventory
| Script | Infrastructure module |
|---|---|
stage_00_setup.py |
infrastructure.core.runtime.setup_checks |
stage_01_test.py |
infrastructure.core.testing.pytest_orchestration, infrastructure.core.testing.test_runner, infrastructure.reporting.pipeline_test_runner |
stage_02_analysis.py |
infrastructure.core.analysis_pipeline, infrastructure.core.script_discovery |
stage_03_render.py |
infrastructure.rendering.pipeline (all enabled formats; historical YAML label PDF Rendering) |
stage_04_validate.py |
infrastructure.validation.output.pipeline, infrastructure.validation.publication.rendered_provenance |
stage_05_copy.py |
infrastructure.core.files, infrastructure.validation.output.render_formats, infrastructure.validation.output.validator, infrastructure.reporting.output_statistics |
stage_06_llm_review.py |
infrastructure.llm.review.pipeline_runner |
stage_07_executive_report.py |
infrastructure.reporting.multi_project_reporter, infrastructure.reporting.output_organizer |
stage_08_connector_search.py |
infrastructure.search.connectors (opt-in) |
stage_09_provenance_record.py |
infrastructure.provenance (opt-in) |
stage_10_research_workflow.py |
infrastructure.research.ResearchWorkflow (opt-in) |
stage_11_ebook.py |
infrastructure.rendering.ebook_stage |
stage_12_metadata.py |
infrastructure.publishing.metadata.metadata_stage |
Bootstrap pattern
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
from scripts import ensure_repo_root_on_path
ensure_repo_root_on_path()
What ships with it
17 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 1.6 KB runs code
- AGENTS.md 3.3 KB
- README.md 1.9 KB
- stage_00_setup.py 2.4 KB runs code
- stage_01_test.py 3.0 KB runs code
- stage_02_analysis.py 4.1 KB runs code
- stage_03_render.py 2.1 KB runs code
- stage_04_validate.py 2.2 KB runs code
- stage_05_copy.py 2.6 KB runs code
- stage_06_llm_review.py 4.5 KB runs code
- stage_07_executive_report.py 2.0 KB runs code
- stage_08_connector_search.py 1009 B runs code
- stage_09_provenance_record.py 6.8 KB runs code
- stage_10_research_workflow.py 3.3 KB runs code
- stage_11_ebook.py 1.7 KB runs code
- stage_12_metadata.py 1.0 KB runs code
- stage_13_docxplus.py 1.9 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.
- 7d ago Changed 2dcf8df361b9
- 1mo ago Changed aeab3ae9a9bc
- 2mo ago First seen · 59 lines · 52 tokens per session scan A 6f8f0c6f8f89
template-pipeline is a skill published in the GitHub repository docxology/template (23 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 665 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.
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