Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/eliornl/rolemule/mock-interview-featuregit clone --depth 1 https://github.com/eliornl/rolemuleWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.01856 |
| Opus 5 | $0.00000 | $0.00928 |
| Sonnet 5 | $0.00000 | $0.00371 |
| Haiku 4.5 | $0.00000 | $0.00186 |
Grade A, and why
mock-interview-feature scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Practice Interview (Mock Interview)
Standalone on-demand feature — not in the LangGraph workflow. Conversational practice for a workflow session using the user’s BYOK LLM. English only in v1.
Product summary
- Tab: 9th application-detail tab Mock Session (
data-tab="practice",#pane-practice) — after Interview (Optimize CV sits after Resume) - Styles:
hr|pro|manager(required at start) - Duration:
10|15|20minutes (default 15) — countdown from serverends_at, not fixed round count - Voice: browser Web Speech API (STT) +
speechSynthesis(TTS); typed fallback always available - Submit UX: voice auto-submits after ~5s silence (
VOICE_SILENCE_MS); typing uses Enter to send (Shift+Enter = newline); Submit answer button remains - Replay question / Mute this question as quiet session links; End early / Abort under More; confirm before End early
- STAR coach always on; mid-session tips under the answer box (no mid-session topic coverage)
- Debrief includes dimension score explanations in the UI, plus answer_reviews (stronger sample per candidate answer, capped at 6) with Copy; legacy
weakest_answer_rewritestill filled for older clients - On timer expiry or End early, any draft answer (≥5 chars) is sent as
final_answeronPOST .../finish(not a separate/turn) so it is included in the debrief transcript - If End early is clicked while a turn is still in flight, the client queues finish, shows scoring UI, and finishes after the turn completes; finish also retries on 409 busy
- Mic pauses while TTS speaks (avoids capturing interviewer audio)
- While waiting for stream: current-question card shows Loading… until the first speak delta
- Ending early / time-up: timer stops immediately and UI shows scoring state until debrief is ready
- Debrief: overall score banner (full summary, no clamp), score breakdown (number on the right of each card), strengths/improvements, stronger sample answers — no Topics row, no Copy summary
- Low-time coaching: last question (~2 min), short summary (~1 min), wrap-up (≤30s)
- No audio upload / no server Whisper in v1
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 111 lines · 0 tokens per session scan A a1bdbdf786f9
mock-interview-feature is a cursor rule published in the GitHub repository eliornl/rolemule (37 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,856 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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