Assess candidates' pi skills in a real terminal
Run pi assessments in CodeSubmit IDE, locally with Git, or live in a CodePair terminal to see how candidates curate context, run shell checks, and ship code.
>Read src/rateLimiter.ts and fix window reset boundary in refillBucket
Updated refillBucket in src/rateLimiter.ts across 9 lines
Ran npm test on rateLimiter.test.ts
All unit tests passed
>
What to evaluate
Skills to evaluate in a pi assessment
Engineers who choose a minimalist harness like pi care about tight context and explicit tool loops, making their terminal decisions easy to evaluate.
Deliberate context curation
Strong candidates load only the relevant files and symbols into the prompt window rather than dumping the entire repository into context.
Fast shell state inspection
Watch how engineers combine standard Unix shell utilities with pi file edits and bash commands to trace application behavior quickly.
Tight verification after each edit
Effective candidates keep the agent loop on a short leash by running checks after each small diff and editing trivial fixes directly.
Clean commits and verification notes
Reviewers can see whether the candidate left a readable commit history and wrote concise notes explaining how the fix was verified.
How it works
How a pi coding assessment runs
Assign a take-home in CodeSubmit IDE or over local Git, or run a live collaborative interview with pi in CodePair.
Configure the repository task
Pick an open-ended task or upload your own repo, then choose CodeSubmit IDE with a per-candidate AI budget, local Git, or CodePair.
Candidate works with pi
In CodeSubmit IDE, pi runs in a recorded, budget-capped workspace, or the candidate works locally over Git or live in CodePair.
Inspect the shipped code
Your team reviews the working code, commits, test output, notes, recorded session, and usage summary, or opens the submission in CodePair.
pi repository tasks
Practical problems for a pi interview test
Assign bounded repository tasks where candidates use pi in the terminal to ship a fix, run the test suite, and document tradeoffs.
Fix duplicate webhook retries in LedgerPulse
Merchant payout webhooks occasionally process twice during network timeouts, creating duplicate ledger entries that break daily reconciliation reports for finance teams.
Preserve existing webhook handler signatures and pass all replay tests.
Add timezone window filtering to RouteDock
Dispatchers scheduling regional freight deliveries see carrier pickup slots shifted by an hour when warehouse depots cross daylight saving boundaries.
Keep database timestamps in UTC without adding external date libraries.
Bound memory usage in TraceHarbor ingester
Sudden log bursts cause the telemetry worker buffer to grow without a ceiling until the container restarts and drops queued spans.
Enforce backpressure within the existing stream pipeline and add unit coverage.
Reviewer evidence
What your hiring team sees after submission
Instead of grading a chat transcript, reviewers inspect the working repository, the commit trail, and an AI review of structure and testing gaps.
Diffs and commits
Inspect the exact files changed, the commit sequence, and whether the candidate kept the patch scoped to the problem.
Tests and notes
Read the automated test results alongside the candidate's written notes on tradeoffs, edge cases, and how they verified the fix.
AI review summary
Review an AI summary of repository structure, testing gaps, and suggested prompts before opening the submission live in CodePair.
Real repository work beats algorithm brainteasers
Engineers who use the pi coding harness in their daily work rely on fast shell inspection, selective context loading, and incremental test runs across real codebases. Timed algorithm puzzles strip away the repository, the terminal, and the verification loop, testing memorized patterns while telling you nothing about how a candidate actually ships software.
A practical task in CodeSubmit IDE, a local Git repository, or live in CodePair shows how a developer reads unfamiliar code, scopes prompts for pi, and verifies edge cases in the shell before submitting. Your team evaluates working software, clean diffs, and clear written reasoning on problems that resemble the work after onboarding.
Frequently asked questions
How do we interview engineers who use the pi coding harness?
Run a take-home in CodeSubmit IDE with a recorded session and AI budget, assign a repository project over local Git, or host a live interview in CodePair. You review the working software, tests, commits, and usage summary.
How do you assess terminal coding agent workflows with pi?
Look at how the candidate curates file context, combines shell commands with pi edits, and verifies tests. CodeSubmit captures diffs, commits, test output, notes, and in CodeSubmit IDE, the recorded session and a usage summary.
Does the pi.dev CLI run inside the CodeSubmit browser workspace?
Yes. For take-homes, pi runs in CodeSubmit IDE with a recorded session and per-candidate AI budget, or locally over Git. For live interviews, pi runs inside CodePair alongside Claude Code, Codex CLI, Gemini CLI, and opencode.
Can we use our own codebase for a pi technical assessment?
Yes, you can upload your own repository or choose a task from the CodeSubmit library for either a short coding screen, a full project, or a live CodePair session. Submitted repositories can also be opened in CodePair for a live debrief.
Run your first pi assessment with CodeSubmit
Assign a task in CodeSubmit IDE or Git, or launch a live CodePair session, to see how candidates ship verified code with pi.