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Interview · Version 1.9.0 · Reviewed 2026-08-02

DSA Problem Solver

Practice and improve technique selection and complexity analysis with evidence, explicit trade-offs, and a verification plan.

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

Works through algorithm problems by clarifying constraints, choosing a technique, proving correctness, and analyzing complexity.

₹99 one-time

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What this skill helps you do

  • Technique selection
  • Complexity analysis
  • Edge-case discovery

How DSA Problem Solver works

You provide

The problem statement, constraints, and your current attempt

It inspects

Input bounds and the invariant that makes a technique valid

It decides

A technique choice justified against one real alternative

You verify

Trace empty, single, duplicate, and boundary inputs by hand

What it checks first

DSA Problem Solver works through algorithm problems by clarifying constraints, choosing a technique, proving correctness, and analyzing complexity. Use it when the work involves Technique selection, Complexity analysis, Edge-case discovery.

  1. Whether the requirements were clarified before a solution was chosen.
  2. Whether the chosen approach is justified against an alternative rather than asserted.
  3. How edge cases and failure conditions are handled once the happy path works.
  4. Whether complexity claims match the implementation actually written.

Failure modes it recognizes

  • Optimizing prematurely and losing correctness, which is scored far worse than a slower correct solution.
  • Silence during problem solving, leaving the interviewer unable to assess reasoning.
  • A stated complexity that does not match the code, which undermines every other claim.
  • Missing the constraint that makes the obvious approach invalid.

Answers it will reject

  • Pattern-matching to a memorized solution without checking that the constraints match.
  • Rewriting from scratch when a targeted fix would do, which burns the remaining time.
  • Claiming an approach is optimal without stating the assumption that makes it so.

Decision rules it applies

  • Clarify inputs, output, scale, and constraints before selecting an approach.
  • State the trade-off between two viable approaches before committing to one.
  • Get a correct solution first, then improve it deliberately and explain why.

Evidence it asks for

  • Trace a small input by hand through the written code before declaring it done.
  • Enumerate empty, single, duplicate, and boundary inputs explicitly.
  • Re-derive the complexity from the code rather than from the intended design.

The method inside

  1. Establish the target signal and current evidence for technique selection.
  2. Identify the highest-leverage gap affecting complexity analysis.
  3. Practice or rewrite edge-case discovery without inventing experience, constraints, or outcomes.
  4. Give calibrated feedback, one improved example, and the next focused exercise.

Deliverables

  • Technique selection assessment
  • Complexity analysis decision and action plan
  • Edge-case discovery verification checklist

Evidence requirements

  • Problem statement or interview prompt
  • Known constraints and assumptions
  • Candidate reasoning or draft solution

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

I need to find the k-th largest element in a stream. Walk me through the approach and the trade-offs.

Expected output

Streaming changes the problem: you cannot sort what you have not seen. A size-k min heap keeps the k largest in O(log k) per element...

Boundaries and compatibility

Ideal for

  • Technique selection: produce a decision or artifact grounded in supplied evidence.
  • Complexity analysis: produce a decision or artifact grounded in supplied evidence.
  • Edge-case discovery: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Providing leaked interview questions
  • Pretending one answer guarantees a hiring level

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.