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Pipeline debugging — measured AI agent skill | DecimalAI

docxology/template-pipeline-debugging

SkillScore82Maintained

Systematic pipeline DAG failure triage for the Research Project Template. USE WHEN ./run.sh or execute_pipeline.py fails, a stage stalls (setup, tests, analysis, render, validate, LLM, copy), pytest/coverage gate fails mid-pipeline, PDF render or validate breaks, Project Analysis finishes too fast with no figures, or user says pipeline debug, stage failed, resume checkpoint, core-only triage — even without naming this skill or docs/prompts.

by docxology

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Doc Ops pack

decimalai skills pull docxology-template-pipeline-debugging

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/docxology-template-pipeline-debugging/SKILL.md

Raw file: /s/docxology-template-pipeline-debugging/SKILL.md ↗ · machine-readable summary at /s/docxology-template-pipeline-debugging.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 6d ago

—-19% tokens0% turns82% of whole cases pass with skill (18/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-04	✗→✓	▲ Improved	-12%	0%
case-06	✗→✓	▲ Improved	-22%	0%
case-07	✗→✓	▲ Improved	-12%	0%
case-08	✗→✓	▲ Improved	34%	0%
case-09	✗→✓	▲ Improved	-25%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/1/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

docxology / template

docs/prompts/pipeline-debugging/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/1/2026Version v1

Skill contents

SKILL.md

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

bash
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

Stage semantics: docs/RUN_GUIDE.md, AGENTS.md.

When NOT to use

Determinism / double-run drift only → reproducibility-audit
Claim truth in prose → manuscript-claim-verification
Validation infrastructure deep dive without a failing stage → validation-quality

Want to see if docxology/template-pipeline-debugging would activate on your phrasing? Try it before you install — no LLM call, no signup.

Other measured skills in the registry, with their headline benchmark lift.