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.
git clone --depth 1 https://github.com/zircote/human-voiceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/zircote/human-voice/voice-resume)<a href="https://agentmods.dev/commands/zircote/human-voice/voice-resume"><img src="https://agentmods.dev/badge/commands/zircote/human-voice/voice-resume/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/zircote/human-voice/voice-resume"><img src="https://agentmods.dev/badge/commands/zircote/human-voice/voice-resume.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00012 | $0.00435 |
| Opus 5 | $0.00006 | $0.00217 |
| Sonnet 5 | $0.00002 | $0.00087 |
| Haiku 4.5 | $0.00001 | $0.00044 |
Grade A, and why
voice-resume 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 8d 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.
What it actually says
/resume — Resume a Paused Interview Session
Resume a previously paused voice interview session.
Procedure
-
List sessions: Run
bin/voice-session listto get all sessions as JSON. -
Filter resumable: Show sessions with
stateofpausedorin_progress. If a session ID was provided as an argument, use that directly. If no resumable sessions exist, suggest/voice-interview. -
Present choices: If multiple resumable sessions, display a table and use
AskUserQuestion:# Session ID State Module Questions Updated 1 a1b2c3d4-... paused M03 18/~70 2026-04-01 2 e5f6g7h8-... in_progress M07 42/~70 2026-03-28 -
Resume session: Run
bin/voice-session resume {session_id}to restore state. -
Launch conductor: Spawn the
interview-conductoragent with this prompt:You are resuming a paused voice elicitation interview.
Session ID: {session_id} Session directory: {session_dir} Project root: {project_root}
Load state.json, recap progress conversationally to the user, then run the FULL interview loop. Use
bin/voice-sequencer next-questionto get each question, present it via AskUserQuestion, record the response, update state, and loop. Do NOT exit after a single question — continue until the interview is complete or the user pauses again.
Output
Confirm which session was resumed, show progress, then present the next question.
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.
- 8d ago First seen · 40 lines · 12 tokens per session scan A be4a6107b96d
voice-resume is a command published in the GitHub repository zircote/human-voice (10 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 435 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-31.
Other commands, from other repositories
graph
Extract prerequisite concept graph from converted course materials and write course-index/concept-graph.md.
alt
Import an Exam Radar (OPTIMETA Alt plugin) export and fold its lecture-emphasis exam signal into the course index — radar.md, a lecture-emphasis column on coverage.md, and a gold-zone weakmap.
quiz
Generate N practice problems on a topic. Saves problem MD + hidden answer MD. User solves on paper, uploads answer PDF, then runs /grade. First arg weakmap → auto-load latest weakmap report and target its top weaknesses.
weakmap
Priority-ranked weakness report. No arg → fresh report from latest errors per pattern. With concept arg → patch latest report by adding the user-declared weakness, save as new timestamped file.
derive
Save a clean reference derivation of a target equation or theorem to derivations/. Draws from course materials (textbook, lecture notes) rather than testing the user.
mock
Generate a mock exam matched to the course's structure (inferred from course-index). Saves problem MD + hidden solution MD. User solves on paper, uploads PDF, runs /grade.