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.
What this skill does
# 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
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.