|
npx skills add https://github.com/WILLOSCAR/research-units-pipeline-skills --skill pipeline-auditor
Purpose: a deterministic “regression test” for the writing stage.
It answers:
This skill is analysis-only. It does not edit content. For all survey-family profiles, style/citation-shape violations are blocking by default.
output/DRAFT.mdoutline/outline.ymloutline/evidence_bindings.jsonlcitations/ref.biboutput/AUDIT_REPORT.mdoutput/TEMPLATE_RESIDUE_SCORECARD.jsonA150++ citation targets (used by the auditor):
Course-paper targets:
..., …), TODO markers, scaffold tags.outline/outline.yml.course_paper, >=2 for survey/deep (inserted by section-merger from outline/tables_appendix.md; index tables remain internal).This subsection ..., In this subsection ...)Next, we move ..., We now turn to ...)this run, this workspace)this pipeline, this stage, and quality gateblock only when the same sentence contains a Harness anchor such as a
checkpoint, Unit ID, Harness lock, attempt ledger, or template residue;
ordinary subject-matter uses remain non-blocking warnings
survey synthesis/comparisons should ....Taken together, ... and similar high-signal generator stems.citations/ref.bib exists): undefined keys, duplicates, basic formatting red flags.[@a] [@b]) and no duplicate keys inside one block ([@a; @a]). Mid-sentence citation ratio is >=20% for course_paper and >=30% for survey/deep.outline/evidence_bindings.jsonl exists): citations used per H3 should stay within the bound evidence set.output/FRONT_MATTER_CONTEXT.json to record the selected front-matter assets and hashes, then require the three template-owning Skill implementations to match .harness/harness.lock.json; missing provenance, legacy locks, and repository drift block acceptance.The JSON scorecard records the measured ratio, counts, threshold, selected asset
hashes, heading-aware examples, and implementation-lock result. During normal
Harness execution, Completion projects its verdict and dimensions into
.harness/evaluations/ledger.jsonl, including failed Attempts, so Run Audit can
expose the latest measurement instead of reducing it to PASS/FAIL. The scorecard
file remains the complete evidence object; the ledger is intentionally smaller.
The current 10% limit is an initial policy target. The published Survey replay
completes the current 31-check contract at 0/226 residue, establishing
attainability for one retained Artifact set. Clean from-scratch execution,
unrelated topics, and cross-profile calibration remain open.
Treat output/AUDIT_REPORT.md as a “what to fix next” router.
Common FAIL families -> responsible stage/skill:
subsection-briefs / evidence-draft / writer-context-pack, then rewrite affected sections.table-schema + appendix-table-writer produced outline/tables_appendix.md (>=1 course-paper table; >=2 survey/deep tables; citation-backed, no placeholders), then rerun section-merger.transition-weaver (and ensure briefs include bridge_terms / contrast_hook), then re-merge.sections/S*.md via writer-selfloop (local, section-level) or subsection-polisher.draft-polisher or local section rewrites).section-mapper → evidence-binder) and regenerate packs.citation-diversifier → citation-injector (NO NEW FACTS), then draft-polisher.sections/S<sec_id>.md front-matter file via writer-selfloop (front-matter path) using dense positioning + method paragraph.If you want the auditor to PASS *without* a heavy polish loop:
uv run python .codex/skills/pipeline-auditor/scripts/run.py --helpuv run python .codex/skills/pipeline-auditor/scripts/run.py --workspace <workspace>--workspace <dir>--unit-id <U###> (optional; for logs)--inputs <semicolon-separated> (rare override; prefer defaults)--outputs <semicolon-separated> (rare override; defaults write output/AUDIT_REPORT.md and output/TEMPLATE_RESIDUE_SCORECARD.json)--checkpoint <C#> (optional)global-reviewer and before LaTeX/PDF:uv run python .codex/skills/pipeline-auditor/scripts/run.py --workspace <workspace>Fix:
citation-verifier and ensure citations/ref.bib contains every cited key.Fix:
sections/* files, then re-merge.Fix:
citation-diversifier to produce output/CITATION_BUDGET_REPORT.md.citation-injector (edits output/DRAFT.md, writes output/CITATION_INJECTION_REPORT.md).draft-polisher → global-reviewer → auditor.Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take willoscar/pipeline-auditor from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.