interviewsignal 0.9.10

AI-native interview platform — capture thought process, not puzzle performance

interviewsignal

Broad-interview, not broadcast-reject. One code. Any number of candidates. Every one of them gets a fair shot — real problem, real tools, real feedback. pip install and you're running. Zero setup cost. Completely secure.

No contrived puzzles. No whiteboard anxiety. Just signal.

---

What is broad-interviewing?

The same way broadcasting reaches many listeners with one signal, broad-interviewing reaches many candidates with one interview. Share a code, and every candidate works the problem on their own time, with their own AI tools, on a real problem. You get back structured, graded, ranked results. They get back honest feedback. Both sides win.

Create interview  →  Share code  →  Candidates work  →  Auto-grade  →  Triage  →  Hire

For the startup: you posted a role and got 200 applications. You can't interview all of them live. With interviewsignal, you share one code, submissions arrive auto-graded and ranked, you spend 15 minutes triaging — advance the top 10, reject the rest, done.

For the candidate: no scheduling, no whiteboard, no trick questions. You work the way you actually work — with AI assistance, on your own time. You get your score once the HM grades. Every candidate gets the same shot regardless of timezone, schedule, or interview anxiety.

For everyone: pip install interviewsignal && interview install. That's the entire setup. No platform to sign up for. No vendor contract. No procurement cycle. No setup cost.

---

Install

pip install interviewsignal && interview install

Requires Python 3.10+ and one of the supported platforms.

Enterprise / no personal API key? See Enterprise configuration below.

---

Quickstart

Hiring manager

interview dashboard

First launch opens a setup wizard in your browser — relay URL, API key, create your first interview. Three screens and you're live.

The interview form asks for three things: problem statement, grading rubric, and an optional time limit. You get back a code like INT-4829-XK. That's your broad-interview — share it with 5 candidates or 500. They all get the same problem, submissions arrive auto-graded and ranked. Hit + Create Interview to create more.

Candidate

pip install interviewsignal && interview install
/interview INT-4829-XK

interview install asks for your name and email once and saves them. After that, /interview CODE is fully automatic — the session starts, GitHub OAuth opens a browser tab if configured (one account, one submission), and the problem appears with no further prompts from you. The problem appears once auth completes. A GitHub repo (interview-{code}) is created automatically and a git remote named interview is wired up in your working directory. Work normally — ask the AI questions, write code, run tests. The session records everything automatically.

When done:

/submit

The session is sealed, pushed to the relay, and auto-graded. You'll see your overall score and a one-line summary in the terminal. Once graded, you can also run:

interview score INT-4829-XK

to see your score (if the HM has enabled sharing).

Hiring manager — review

interview dashboard              # → http://localhost:7832
interview dashboard INT-4829-XK  # → jump straight to one interview's submissions

Submissions arrive sorted by score. Flags highlight anomalies — sessions that were too fast, showed no iteration, had suspiciously uniform timing, or show signs of tampered hooks (gaps in the event stream, code changes that don't match the tool log). Select candidates in bulk and advance or reject in one click. Click into any candidate to see the full transcript, dimension scores, and diff. Add comments. Record your decision. Export filtered candidates as CSV.

Use Verify Chain to confirm the session log is tamper-evident. Control what candidates see after grading with the Score Sharing panel.

---

How it works

interviewsignal installs as a skill into your AI coding assistant. It captures the full conversation — prompts, AI reasoning before each action, every tool call (reads, writes, bash commands) — and builds an append-only, hash-chained session log. After each turn, it silently commits any changed files to the local repo, so the candidate's interview-{code} GitHub repo shows a step-by-step commit history. On /submit, the log is sealed and pushed to the relay.

HM side                                 Candidate side
───────────────────────────             ─────────────────────────────
interview dashboard
  ↓ setup wizard (first run):
      relay URL → registers hm_key
      API key → saved locally
      create interview form
  ↓ code INT-4829-XK created
  ↓ package pushed to relay

                                        /interview INT-4829-XK
                                          ↓ fetches problem from relay
                                          ↓ relay auto-configured from package
                                          ↓ GitHub OAuth (one account = one submission)
                                          ↓ interview-{code} repo created
                                          ↓ session recording starts
                                              ↓ hooks capture every tool call
                                              ↓ append-only events.jsonl
                                              ↓ SHA-256 hash chain
                                              ↓ silent commit after each turn
                                        /submit
                                          ↓ session sealed + commit log captured
                                          ↓ git push → interview-{code}
                                          ↓ pushed to relay
                                          ↓ score + summary shown (if auto-graded)

interview dashboard
  ↓ submissions arrive, auto-graded
  ↓ flags highlight anomalies
  ↓ batch advance / reject
  ↓ hire / next round / reject

During session (after each turn):

• Silent git add -A && git commit — captures code state after every candidate prompt; commit message is timestamp + prompt snippet; skips if nothing changed

On submit:

1. session seal — finalises hash chain, captures git diff (start → end) and full per-prompt commit log
2. Git push — pushes entire commit history to the candidate's interview-{code} repo (non-blocking)
3. Push to relay — sealed session (events + manifest + commit log) stored server-side
4. Auto-grade — if enabled and GRADING_API_KEY is configured on relay, grade runs immediately; overall score + summary shown to candidate in terminal

---

Relay

The relay stores interview packages and candidate sessions so HMs and candidates only need to share a short code — no file transfers, no email attachments.

The setup wizard handles relay configuration on first launch. For automation or scripting, you can also configure it directly:

interview configure-relay      # CLI alternative to the wizard

Option 1 — Your own relay (~$5/mo, fully private)

After deploying:

1. Set RELAY_API_KEY (any random string) in Railway → Variables
2. Add a /data volume — this is where sessions are stored
3. Copy your Railway URL (e.g. https://myrelay.up.railway.app)
4. Run interview dashboard and paste the URL into the setup wizard

Optional — auto-grading on submission:

GRADING_API_KEY=<anthropic-key>          # enables auto-grading
GRADING_MODEL=claude-haiku-4-5-20251001  # model to use (default)

Or with Docker:

docker build -t interviewsignal-relay .
docker run -e RELAY_API_KEY=secret -v /data:/data -p 8080:8080 interviewsignal-relay

GitHub OAuth

Prevent candidates from submitting multiple times under different names. One GitHub account = one submission per interview code.

This is a relay operator step — done once at deploy time, not something HMs configure per-interview.

Add to your relay's environment variables:

GITHUB_CLIENT_ID=<your_client_id>
GITHUB_CLIENT_SECRET=<your_client_secret>
RELAY_BASE_URL=https://myrelay.up.railway.app

Create the GitHub OAuth App at github.com/settings/developers:

• Application name: your company or team name
• Callback URL: https://myrelay.up.railway.app/auth/github/callback

When configured, candidates see a browser auth step at session start. The relay enforces uniqueness server-side. The HM sees the candidate's GitHub username, avatar, and a link to their session repo.

Without GitHub OAuth, candidates are identified by name + email.

See docs/relay-api.md for the full API contract and data layout.

Option 2 — Email only (free, no server)

interview configure-relay   # choose 2
interview configure-email   # set up SMTP credentials

Reports are emailed directly to the HM on /submit. The HM saves the JSON attachment to ~/.interview/received/ and it appears in the dashboard.

---

Why this works

Every candidate session is append-only and SHA-256 hash-chained — any tampering breaks the chain. In relay mode, the relay's server-side timestamp is the integrity anchor. Grade revisions require an explicit reason and the audit records whether identity was known at revision time:

The relay enforces strict credential isolation. When a candidate fetches GET /interviews/<code>, they receive only the problem, time limit, and a per-interview submit_token scoped to that code — the HM's admin credential (hm_key) never leaves the server. The submit token is write-only: it can only be used to submit to that specific interview, nothing else. A compromised token grants no read access to other candidates, no rubric, no scoring data.

[2026-04-13T10:47:22Z] grade_recorded  INT-4829-XK  hash=d4abe5e6  score=7.7
[2026-04-13T11:30:00Z] grade_revised   INT-4829-XK  hash=9f2c1a3b  7.7→8.2  reason="missed edge cases"

The session flags system detects signal-noise issues at two levels. Quality flags catch sessions completed in under 10 minutes (too fast), fewer than 3 tool calls (few interactions), no failed-then-fixed iteration pattern (no iteration), statistically uniform event timing (possible scripting), and zero prompts logged (no prompts). Tamper detection flags catch large gaps in the event stream where hooks may have been disabled (hooks gap), code changes in the git diff that don't match the Write/Edit tool calls in the log (diff mismatch), and tool calls with no corresponding user prompts (selective suppression). Candidates control their own machine — security is detection, not prevention. A sparse or gapped session is its own red flag. Flags appear as color-coded indicators in the dashboard — you decide what to do with them.

---

Enterprise configuration

interview configure-llm

Pattern	What to set
Anthropic direct	API key only (default)
Internal proxy (Floodgate, corporate gateway)	Base URL + optional key; proxy handles auth
OpenAI-compatible endpoint	Base URL + key + format=openai

Config stored in ~/.interview/config.json:

{
  "anthropic_base_url":      "https://ai-gateway.corp.internal/anthropic",
  "anthropic_api_key":       "",
  "api_format":              "anthropic",
  "grading_model":           "claude-haiku-4-5-20251001",
  "anthropic_extra_headers": {"X-Team-ID": "ml-hiring"}
}

Environment variable overrides:

ANTHROPIC_API_KEY=...           # API key
ANTHROPIC_BASE_URL=...          # base URL override
INTERVIEW_GRADING_MODEL=...     # model name override

---

Platform support

Platform	Install	Hooks	Activity capture
Claude Code	interview install	PreToolUse / PostToolUse / Stop	Full — prompts, tool calls, reasoning
Codex	interview install --platform codex	PreToolUse	Full
Gemini CLI	interview install --platform gemini	preToolUse / postToolUse	Full
Cursor	interview install --platform cursor	None	Limited — skill instructions only
Aider	interview install --platform aider	None	Limited — skill instructions only

Cursor and Aider don't support lifecycle hooks, so activity capture is limited to what the candidate voluntarily records. For full capture, use Claude Code, Codex, or Gemini CLI.

---

What gets captured

Event	Captured
Candidate prompts	Exact message to the AI assistant
AI reasoning	Plan before each action ("I'll use a hash map because...")
File reads	Path
File writes	Path + content hash
Bash commands	Command + exit code
File edits	Path + change summary
Git state	Branch + commit at start and end
Git diff	Full diff (start → submit)
Per-prompt commits	Silent commit after each turn; timestamp + prompt snippet as message
Commit log	Full commit history (hash, timestamp, message, files changed) in manifest
GitHub repo	Auto-created interview-{code} repo; full commit history pushed on submit
Timestamps	Millisecond precision on every event
Session flags	Quality + tamper signals (too fast, no iteration, uniform timing, hooks gap, diff/commit mismatch, prompt ratio)

The session log is append-only and hash-chained. Any tampering breaks the chain. The dashboard includes a Verify Chain button.

Raw file contents are never stored — only paths, hashes, and command summaries.

---

Configuration reference

# HM dashboard (primary — starts setup wizard on first run)
interview dashboard            # → http://localhost:7832
interview dashboard <CODE>     # jump straight to one interview's submissions

# CLI alternatives for scripting / automation
interview configure-relay      # relay URL + auto-register HM account
interview configure-api-key    # Anthropic API key (direct access)
interview configure-llm        # Enterprise: custom endpoint, proxy, format, extra headers
interview configure-email      # SMTP fallback (email-only mode, no relay)

# Candidate
interview install                        # Claude Code (default)
interview install --platform codex       # Codex
interview install --platform cursor      # Cursor
interview install --platform gemini      # Gemini CLI
interview install --platform aider       # Aider
interview status                         # check active session
interview score <CODE>                   # fetch your score from relay

All config stored in ~/.interview/config.json (permissions: 600).

---

Privacy

Candidate sessions stored on relay: events.jsonl, manifest.json, flags.json. Raw file contents are never stored.

Grading sends the session timeline and git diff to the configured AI endpoint using your own API key — interviewsignal never sees it.

Self-hosted relay: nothing leaves your network. See docs/relay-api.md.

No telemetry. No analytics. No tracking.

---

Built with

Python stdlib only (no external dependencies for core or relay). Grading via Anthropic Messages API or any compatible endpoint. Dashboard is a self-contained local HTTP server. Relay is a single-process stdlib HTTP server backed by flat files.

---

Contributing

Prompts — the grading instructions are open and community-editable. The full interview skill is in interview/skills/interview/SKILL.md.

Worked examples — run a real session, save output to worked/{slug}/, write an honest review.md, open a PR.

Platform support — each new platform is a ~30 line adapter in cli.py.

See ARCHITECTURE.md for module responsibilities and docs/relay-api.md for the relay API contract.

---

Broad-interview, not broadcast-reject. Pure signal.