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Business Operations · Version 1.2.0 · Reviewed 2026-08-02

Partner Integration Evaluator

Remove friction from demand evidence and maintenance cost with evidence, explicit trade-offs, and a verification plan.

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

Evaluates integration partnerships on real customer demand, maintenance cost, and support burden.

₹149 one-time

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What this skill helps you do

  • Demand evidence
  • Maintenance cost
  • Support ownership

How Partner Integration Evaluator works

You provide

Prompts, model versions, evaluation data, and failures

It inspects

Failure class and context sufficiency for demand evidence

It decides

A maintenance cost change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

Partner Integration Evaluator evaluates integration partnerships on real customer demand, maintenance cost, and support burden. Use it when the work involves Demand evidence, Maintenance cost, Support ownership.

  1. Whether the failure is systematic across a class of inputs or random, which separates a capability gap from a sampling issue.
  2. Whether evaluation data overlaps training or prompt-development data, which invalidates the measurement.
  3. Token distribution of inputs and outputs, since cost and latency are driven by the tail, not the mean.
  4. Whether the system has a defined behavior for low confidence, or always produces an answer.
  5. Version pinning across model, prompt, retrieval, and tools, because an unpinned component makes regressions unattributable.

Failure modes it recognizes

  • Silent quality regression after a provider updates a model behind an unversioned alias.
  • Evaluation overfitting where the prompt was tuned on the same examples used to score it.
  • Cost and latency dominated by a small number of very long inputs that were never in the test set.
  • Tool-calling loops where the model retries a failing tool without a bounded attempt budget.
  • Confident fabrication when context is insufficient because no refusal path was defined.
  • Distribution shift where production inputs diverge from the evaluation set over time.

Answers it will reject

  • Judging quality by reading a few outputs, which cannot detect a regression of a few percent.
  • Using a larger model to fix a problem caused by missing context, paying more for the same failure.
  • Fine-tuning before exhausting prompting and retrieval, which is slower to iterate and harder to reverse.
  • Using an LLM judge without validating the judge against human labels on the same rubric.

Decision rules it applies

  • Establish a labeled evaluation set and a baseline before changing anything; without a baseline there is no improvement, only change.
  • Pin every version and change one component at a time.
  • Define and test the refusal path explicitly; a system that cannot say "I do not know" will fabricate.
  • Budget latency and cost on p95 token counts, not averages.

Evidence it asks for

  • Score per input class (easy, hard, adversarial, no-answer) so aggregate scores cannot hide a broken class.
  • Log model version, prompt version, and retrieval version on every request for regression attribution.
  • Track p50 and p95 tokens and cost per successful task, not per call.

The method inside

  1. Map actual work and queues rather than the documented happy path
  2. Classify waits, rework, ownership gaps, and legitimate exceptions
  3. Identify the constraint producing the largest downstream effect
  4. Redesign the handoff with measurable entry and exit criteria

Deliverables

  • Demand evidence current-state map
  • Maintenance cost root-cause register
  • Support ownership future-state control plan

Evidence requirements

  • Process map, SOP, timestamps, exceptions, and work records
  • Owners, entry-exit criteria, SLAs, and system boundaries
  • Representative cases including failures and workarounds

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 partner wants to build an integration with us and it sounds like good exposure. What should we consider?

Expected output

Every integration is a permanent maintenance and support commitment, so the question is demand rather than exposure. Quantify how many customers asked for it, decide who owns support when it breaks, and agree what happens when either API changes...

Boundaries and compatibility

Ideal for

  • Demand evidence: produce a decision or artifact grounded in supplied evidence.
  • Maintenance cost: produce a decision or artifact grounded in supplied evidence.
  • Support ownership: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Automating a broken process without diagnosis
  • Treating every exception as employee noncompliance

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.