What is template-pipeline-debugging?

A troubleshooting guide for a research-project pipeline, which is a sequence of automated stages such as setup, tests, analysis, PDF rendering, and validation.

Use it to reproduce failures, isolate a broken stage, classify the cause, and resume from a checkpoint after fixing it.

It helps find the first real cause of a failure instead of focusing on the final error message or repeatedly rerunning the whole pipeline.

template-pipeline-debugging — Skill by docxology

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/pipeline-debugging

Any agent

npx skills add docxology/template --skill pipeline-debugging

Clone the repo

git clone --depth 1 https://github.com/docxology/template

Made for: Claude Code, Codex.

Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 937 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.

Model	Per session	Once invoked
Fable 5	$0.00103	$0.00937
Opus 5	$0.00051	$0.00468
Sonnet 5	$0.00021	$0.00187
Haiku 4.5	$0.00010	$0.00094

Measured 2d ago against content hash dd3a20f54dcc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

template-pipeline-debugging 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.

docs/prompts/pipeline-debugging/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pipeline debugging

Natural invoke

"My pipeline failed at PDF render for template_code_project"
"Project Analysis finishes in under a second with no figures — help debug"
"run.sh --pipeline failed; what's the first real error?"
"Resume from checkpoint after fixing project tests"

Inputs to confirm

Project — from docs/_generated/active_projects.md; infer from context if obvious.
Invocation — full vs --core-only, --resume, --skip-infra, multi-project.
Failing stage — if unknown, reproduce first.

Workflow

Reproduce — re-run the failing invocation verbatim; capture full stderr/stdout and exit status. Quote the first real error, not the last line. Name the failing stage (setup, infra tests, project tests, analysis, render, validate, LLM review, LLM translations, copy).
Isolate — run that stage's underlying command directly (pytest target, analysis script, validation CLI, renderer). Use --resume to skip good upstream stages while iterating.
Classify — dependency/uv gap, missing system tool (LaTeX/pandoc-crossref), nondeterministic input, coverage gate, undefined citation/cross-ref, thin-orchestrator violation (logic in scripts/ not src/), or logic bug. When Project Analysis completes in under a second with no figures, treat as import/dependency failure and isolate via scripts/pipeline/stage_02_analysis.py. Trace where bad state enters; fix at ingestion.
Fix minimally — re-run full pipeline green; confirm --resume shows no upstream regression. Update projects/<n>/AGENTS.md / README.md if the failure mode was non-obvious.

Deliverables

Failing stage, first real error (quoted), root cause, minimal patch (file + snippet).
Exact commands + raw exit status proving green.
Do not claim "fixed" without a clean full-pipeline run.

Verification commands

uv sync
uv run python scripts/runner/execute_pipeline.py --project <project>
uv run python scripts/runner/execute_pipeline.py --project <project> --resume
uv run python scripts/runner/execute_pipeline.py --project <project> --core-only
uv run python scripts/pipeline/stage_01_test.py --project <project>
uv run pytest projects/<project>/tests/ --cov=projects/<project>/src --cov-fail-under=90 -q
uv run python -m infrastructure.validation.cli prerender projects/<project>/manuscript --repo-root .
uv run python -m infrastructure.validation.cli pdf output/<project>/pdf/
uv run python -m infrastructure.rendering.latex_package_validator
LOG_LEVEL=0 uv run python scripts/runner/execute_pipeline.py --project <project> --resume

Read the full file on GitHub · 76 lines

Files

What ships with it

2 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.

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.

2d ago First seen · 76 lines · 103 tokens per session scan A dd3a20f54dcc

Subscribe to this mod's changes

template-pipeline-debugging is a skill published in the GitHub repository docxology/template (19 stars, last pushed 2d ago), licensed Apache-2.0. It adds 103 tokens to every session and 937 once invoked, about $0.0005 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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