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Career · Version 2.2.0 · Reviewed 2026-08-02

Resume Review Agent

Improve impact rewriting and seniority calibration with evidence, explicit trade-offs, and a verification plan.

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

Rewrites engineering resumes around measurable impact, scope, and technical specificity instead of responsibility lists.

₹99 one-time

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What this skill helps you do

  • Impact rewriting
  • Seniority calibration
  • ATS keyword alignment

How Resume Review Agent works

You provide

Prompts, model versions, evaluation data, and failures

It inspects

Failure class and context sufficiency for impact rewriting

It decides

A seniority calibration change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

Resume Review Skill rewrites engineering resumes around measurable impact, scope, and technical specificity instead of responsibility lists. Use it when the work involves Impact rewriting, Seniority calibration, ATS keyword alignment.

  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 the artifact, actors, boundaries, and invariants relevant to impact rewriting.
  2. Trace concrete failure or abuse paths for seniority calibration; do not report checklist items without a mechanism.
  3. Prioritize ATS keyword alignment findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Impact rewriting assessment
  • Seniority calibration decision and action plan
  • ATS keyword alignment verification checklist

Evidence requirements

  • Current resume or career artifact
  • Target role and job description
  • Verified scope, outcomes, and metrics

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

Review my backend engineer resume and tell me where the impact statements are too vague to be credible.

Expected output

Six bullets describe responsibilities rather than outcomes. "Improved API performance" needs the baseline, the change, and the result: which endpoint, what latency before and after, and at what traffic level...

Boundaries and compatibility

Ideal for

  • Impact rewriting: produce a decision or artifact grounded in supplied evidence.
  • Seniority calibration: produce a decision or artifact grounded in supplied evidence.
  • ATS keyword alignment: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Inventing employers, metrics, projects, or credentials
  • Guaranteeing interviews or hiring outcomes

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.