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Reliability · Version 1.2.0 · Reviewed 2026-08-02

Incident Timing Advisor

Reduce production risk in declaration triggers and escalation timing with evidence, explicit trade-offs, and a verification plan.

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

Decides when to declare, escalate, and communicate during ambiguous early-stage incidents.

₹99 one-time

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What this skill helps you do

  • Declaration triggers
  • Escalation timing
  • Early communication

How Incident Timing Advisor works

You provide

Build definition, timings, and cache statistics

It inspects

Layer ordering and secret exposure for declaration triggers

It decides

A escalation timing change that keeps every gate intact

You verify

Per-stage duration and cache hit rate re-measured

What it checks first

Incident Timing Advisor decides when to declare, escalate, and communicate during ambiguous early-stage incidents. Use it when the work involves Declaration triggers, Escalation timing, Early communication.

  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. Reconstruct the symptom timeline and define what healthy behavior would look like for declaration triggers.
  2. Rank hypotheses for escalation timing by evidence, blast radius, and ability to explain every observed symptom.
  3. Run the cheapest discriminating check for early communication; update confidence only when evidence changes.
  4. Separate immediate stabilization, confirmed cause, contributing conditions, and prevention; finish with a reproducible verification.

Deliverables

  • Declaration triggers assessment
  • Escalation timing decision and action plan
  • Early communication verification checklist

Evidence requirements

  • User-visible symptoms and SLO impact
  • Timeline, telemetry, deploys, and dependency state
  • Current mitigations and operational 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

We waited 40 minutes to declare an incident because we were not sure it was real, and that delay hurt.

Expected output

Waiting for certainty guarantees delay, because certainty arrives after impact. Declare on a measurable trigger rather than on confidence, make declaration cheap and reversible, and treat a stood-down incident as a success rather than a false alarm...

Boundaries and compatibility

Ideal for

  • Declaration triggers: produce a decision or artifact grounded in supplied evidence.
  • Escalation timing: produce a decision or artifact grounded in supplied evidence.
  • Early communication: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Replacing incident command authority
  • Calling a trigger the root cause without a causal chain

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.