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Data · Version 1.1.0 · Reviewed 2026-08-02

Batch Pipeline Recovery Agent

Make data systems more correct and operable for failed-run recovery and backfill planning with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Plans safe recovery for failed batch and ETL workflows using checkpoints, idempotency, partial-output cleanup, backfills, and validation.

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What this skill helps you do

  • Failed-run recovery
  • Backfill planning
  • Output reconciliation

How Batch Pipeline Recovery Agent works

You provide

Build definition, timings, and cache statistics

It inspects

Layer ordering and secret exposure for failed-run recovery

It decides

A backfill planning change that keeps every gate intact

You verify

Per-stage duration and cache hit rate re-measured

What it checks first

Batch Pipeline Recovery Skill plans safe recovery for failed batch and ETL workflows using checkpoints, idempotency, partial-output cleanup, backfills, and validation. Use it when the work involves Failed-run recovery, Backfill planning, Output reconciliation.

  1. Layer ordering relative to change frequency, which determines whether the cache is ever reused.
  2. Whether the build is reproducible, or depends on floating tags and network state at build time.
  3. Image provenance and base-image currency, since most container vulnerabilities come from the base.
  4. Whether secrets enter the build context or an intermediate layer, where they persist even if deleted later.
  5. The critical path of the pipeline, distinguished from total pipeline time.

Failure modes it recognizes

  • Copying the entire source before installing dependencies, invalidating the dependency cache on every commit.
  • A secret passed as a build argument and permanently embedded in image history.
  • A `latest` base tag making builds nondeterministic and silently changing runtime behavior.
  • Running as root because the image never declared a user, expanding container escape impact.
  • A cache key that includes a timestamp, so the cache never hits.
  • Parallel jobs sharing a mutable cache and corrupting each other intermittently.

Answers it will reject

  • Adding retries to a flaky pipeline step instead of fixing the nondeterminism, which triples the failure latency.
  • Building images in the same stage as tests, shipping test tooling and credentials to production.
  • Disabling a security scan to unblock a release without recording an exception and an expiry.
  • Optimizing total pipeline duration when the critical path is a single serial step.

Decision rules it applies

  • Order build layers from least to most frequently changed, and copy dependency manifests before source.
  • Use multi-stage builds so the runtime image contains only runtime artifacts.
  • Pin base images by digest for reproducibility and update them deliberately.
  • Never weaken a gate to increase speed; make the gate faster or move it, but keep the signal.

Evidence it asks for

  • Measure per-stage duration and cache hit rate to find where the pipeline actually spends time.
  • Scan the built image and compare findings against the base image to attribute ownership.
  • Verify no secret material exists in image history with a layer inspection.

The method inside

  1. Establish what is actually true about failed-run recovery from the supplied evidence, and mark what is missing.
  2. Identify the mechanism behind backfill planning rather than restating the symptom.
  3. Choose the smallest defensible change for output reconciliation, weighing impact, confidence, effort, and reversibility.
  4. Plan migration and observability before cutover

Deliverables

  • Failed-run recovery assessment
  • Backfill planning decision and action plan
  • Output reconciliation verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

A six-hour nightly pipeline failed after writing four of ten partitions. How do we resume without duplicating data?

Expected output

Identify the committed checkpoint and whether partition writes are atomic. Remove or version incomplete outputs, restart only missing partitions with the same run ID, then reconcile row counts and checksums before publishing the dataset...

Boundaries and compatibility

Ideal for

  • Failed-run recovery: produce a decision or artifact grounded in supplied evidence.
  • Backfill planning: produce a decision or artifact grounded in supplied evidence.
  • Output reconciliation: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.