oai-solution-reviewer
This skill should be used when the user asks to "grade my solution", "review my code", "score this", "how did I do", "grade sheet", "review my OAI prep", "grade my practice problem", "review my interview prep", "evaluate my solution", "how would this score", or wants feedback on a coding interview practice solution. Evaluates Java implementations against OpenAI interviewer grading criteria and produces a comprehensive grade sheet with letter grades, numeric scores, pass/fail, and prose feedback.
Best use case
oai-solution-reviewer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
This skill should be used when the user asks to "grade my solution", "review my code", "score this", "how did I do", "grade sheet", "review my OAI prep", "grade my practice problem", "review my interview prep", "evaluate my solution", "how would this score", or wants feedback on a coding interview practice solution. Evaluates Java implementations against OpenAI interviewer grading criteria and produces a comprehensive grade sheet with letter grades, numeric scores, pass/fail, and prose feedback.
Teams using oai-solution-reviewer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/oai-solution-reviewer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How oai-solution-reviewer Compares
| Feature / Agent | oai-solution-reviewer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill should be used when the user asks to "grade my solution", "review my code", "score this", "how did I do", "grade sheet", "review my OAI prep", "grade my practice problem", "review my interview prep", "evaluate my solution", "how would this score", or wants feedback on a coding interview practice solution. Evaluates Java implementations against OpenAI interviewer grading criteria and produces a comprehensive grade sheet with letter grades, numeric scores, pass/fail, and prose feedback.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# OAI Solution Reviewer Grade coding interview practice solutions against OpenAI's interviewer evaluation criteria. Produces a structured grade sheet combining letter grades (A-F), numeric scores (1-4), pass/fail verdicts, and detailed prose feedback per dimension. ## When to Use User solutions are typically in `practice/leetcode/src/main/java/sjer/red/openai/` with progressive parts (P1, P2, P3+). Works for any coding problem, not just OAI-specific ones. The user writes Java. ## Evaluation Workflow ### Step 1: Read the Solution Read the implementation file(s) the user wants graded. If the problem has multiple parts (P1, P2, P3), read all completed parts to assess follow-up readiness. Do not read or grade test files -- focus on the implementation only. Identify from the code: - The problem being solved (from Javadoc header or class name) - Which part number this is (P1, P2, P3, etc.) - Whether earlier parts exist (to evaluate progression) ### Step 2: Evaluate Four Dimensions Score each dimension using the detailed rubric in `references/grading-rubric.md`. Consult `references/java-quality-checklist.md` for Java-specific quality signals. **Dimension 1 -- Problem Solving** - Is the approach correct and efficient? - Are the right data structures chosen for the job? - Is time/space complexity optimal or near-optimal? - Are tradeoffs considered (visible in comments or design choices)? - Does the solution handle the problem's core constraints? **Dimension 2 -- Code Quality** - Are variable/method names meaningful and descriptive? - Is logic decomposed into helper methods with single responsibility? - Are edge cases handled proactively (null, empty, boundaries)? - Is the code readable without requiring mental gymnastics? - Are Java idioms used correctly? (Consult `references/java-quality-checklist.md`) - Is this production-quality code, not just "passes the tests" code? **Dimension 3 -- Communication** - Does the code self-document through naming and structure? - Do comments explain _why_, not _what_? - Is the API design clear (method signatures, return types, Javadoc)? - Is reasoning visible in the code (approach comments, tradeoff notes)? - **Template exclusion:** The class-level Javadoc problem header is template-provided, not user-written. Do not credit it. Only evaluate user-authored communication: naming choices, structural clarity, inline comments, method-level Javadoc they added, and tradeoff notes. - _Limitation:_ Verbal fluency and live interviewer interaction cannot be assessed from written code. Note this in the grade sheet. **Dimension 4 -- Testing** - Does the implementation defensively handle edge cases in its logic? - Are boundary conditions addressed (empty collections, zero, negative, overflow)? - Are error conditions handled gracefully (exceptions, invalid input)? - Is there evidence of thinking about what could go wrong? - **Contract vs boundary distinction:** Only penalize missing null handling for _boundary inputs_ (user-facing data, external API responses). When null represents a broken caller contract (e.g., a `Function<>` parameter returning null), an NPE is the correct failure mode -- do not penalize its absence. ### Step 3: Assess Follow-up Readiness If the solution is part of a progressive series (P1 -> P2 -> P3): - Is the code modular enough that the next part would NOT require a rewrite? - Are abstractions at the right level to accommodate added complexity? - Would adding concurrency, persistence, or new features require gutting the existing design? If earlier parts exist, compare: did the code evolve gracefully, or did each part require starting over? ### Step 4: Generate Grade Sheet Produce the grade sheet in exactly this format: ``` # Grade Sheet: [Problem Name] -- Part [N] ## Overall Verdict: [Strong Hire / Hire / Lean No Hire / Strong No Hire] ## Dimension Scores | Dimension | Letter | Score (1-4) | Pass/Fail | |-----------|--------|-------------|-----------| | Problem Solving | [A-F] | [1.0-4.0] | [PASS/FAIL] | | Code Quality | [A-F] | [1.0-4.0] | [PASS/FAIL] | | Communication | [A-F] | [1.0-4.0] | [PASS/FAIL] | | Testing | [A-F] | [1.0-4.0] | [PASS/FAIL] | ## Detailed Feedback ### Problem Solving [Letter | Score/4 | PASS/FAIL] **Strengths:** [what was done well] **Improvements:** [specific, actionable changes] ### Code Quality [Letter | Score/4 | PASS/FAIL] **Strengths:** [what was done well] **Improvements:** [specific, actionable changes] **Java-specific:** [idiom usage, anti-patterns found] ### Communication [Letter | Score/4 | PASS/FAIL] **Strengths:** [what was done well] **Improvements:** [specific, actionable changes] *Note: Verbal communication cannot be assessed from written code.* ### Testing [Letter | Score/4 | PASS/FAIL] **Strengths:** [defensive coding observed] **Improvements:** [edge cases missed, error handling gaps] ## Follow-up Readiness - Could this code extend to Part [N+1] without a rewrite? [Yes/No/Partial] - What would need to change? [specific refactoring needed] ## If This Were a Real Interview... [1-2 paragraph honest, direct assessment. No sugarcoating. Would this pass at OAI? What would the interviewer's internal notes say? What would tip the decision?] ## Top 3 Action Items 1. [highest-impact improvement] 2. [second priority] 3. [third priority] ``` ### Scoring Guide (Quick Reference) | Score | Letter | Verdict | Pass/Fail Threshold | | ------- | ------ | ----------------- | ------------------- | | 3.7-4.0 | A/A+ | Strong Hire | PASS | | 3.3-3.6 | A-/B+ | Hire | PASS | | 3.0-3.2 | B/B+ | Hire (borderline) | PASS | | 2.5-2.9 | B-/C+ | Lean No Hire | FAIL | | 2.0-2.4 | C/C- | Lean No Hire | FAIL | | 1.0-1.9 | D/F | Strong No Hire | FAIL | Pass threshold is 3.0 (maps to "Hire"). Overall verdict is the _lowest_ dimension verdict -- one FAIL dimension means the overall cannot be higher than Lean No Hire. ### Grading Principles - _Be honest, not encouraging._ The goal is to prepare for a real interview, not to feel good. A 2.5 is a 2.5. - _Be specific, not vague._ "Naming could be better" is useless. "Rename `m` to `cellDependencies` on line 47" is actionable. - _Grade against OAI's bar, not a general bar._ OAI expects production-quality code. A solution that "works" but is messy is a Lean No Hire. - _Acknowledge what's done well._ Strong Hire signals should be called out so the user knows what to keep doing. - _Java-specific feedback matters._ Using `Stack` instead of `ArrayDeque` or raw types is a concrete signal to interviewers. ## Additional Resources ### Reference Files For detailed scoring criteria and checklists, consult: - **`references/grading-rubric.md`** -- Per-dimension scoring criteria at each level (Strong Hire through Strong No Hire) with concrete examples - **`references/java-quality-checklist.md`** -- Java-specific idiom checks, anti-pattern detection, and data structure selection guidance
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