Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/cynthiajones34/GBrainnpx agentmods add skills/cynthiajones34/gbrain/voice-post-callWrote 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/skills/cynthiajones34/gbrain/voice-post-call)<a href="https://agentmods.dev/skills/cynthiajones34/gbrain/voice-post-call"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/voice-post-call/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/skills/cynthiajones34/gbrain/voice-post-call"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/voice-post-call.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 | $0.00000 | $0.01627 |
| Opus 5 | $0.00000 | $0.00813 |
| Sonnet 5 | $0.00000 | $0.00325 |
| Haiku 4.5 | $0.00000 | $0.00163 |
Grade A, and why
voice-post-call 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 9d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
voice-post-call — Post-session transcript + summary handling
Convention: see conventions/quality.md for citation rules + back-link enforcement.
Convention: see _brain-filing-rules.md for filing decision protocol.
Iron Law
Every call gets processed, even on tool-call failure. The voice persona MAY call a log_call_summary tool mid-session, OR the call may end without that tool firing (model forgot, WebRTC dropped, browser crashed). The automatic call-end handler in services/voice-agent/code/server.mjs posts a structured signal regardless so the brain still gets the transcript + audio reference.
If both paths fire (the tool call AND the call-end handler), the second one is idempotent — it sees the brain page already exists and updates instead of duplicating.
The pipeline
1. CAPTURE → MediaRecorder on the host repo's voice-agent service captures
the full call audio (webm/opus) to /tmp/calls/<ts>-<persona>.webm.
The browser client at /call?test=1 also captures via WebAudio-tee
for E2E asserts; production /call uses server-side capture only.
2. TRANSCRIBE → Whisper (via gbrain transcription) processes the audio. Output:
full transcript (timestamped) + speaker labels where possible.
3. SUMMARIZE → A separate LLM call produces a 3-5 sentence summary covering
key topics, decisions, and unresolved items.
4. WRITE → Create or update meetings/YYYY-MM-DD-call-<persona>.md with:
- frontmatter (date, persona, duration, ratings)
- full transcript in a "Transcript" block-quote section
- summary in a "Summary" section
- audio link (file://, or signed URL if uploaded to storage)
- any entity cross-links (people, companies mentioned)
5. CROSS-LINK → For each entity in the transcript (person, company), append a
timeline entry to people/<slug>.md or companies/<slug>.md pointing
back to this call page. Iron Law: per conventions/quality.md.
6. POST → Send the summary to the operator's messaging surface (Telegram,
Slack, Discord — whichever is wired in $TARGET_REPO/.env).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 165 lines · 0 tokens per session scan A 7b428d0d0bd2
voice-post-call is a skill published in the GitHub repository cynthiajones34/GBrain (0 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,627 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 2026-08-31.
Other skills, from other repositories
memory-proactive
Proactive layered recall and generic domain-aware routing.
memory-archivist
A set of scripts for archiving conversations, syncing them to a knowledge graph, updating summaries, and managing stored memories over time. A knowledge graph is a linked collection of information and relationships.
memory-starter-kit
Historical starter note for the memory sidecar stack.
mind
Local project memory with recall, provenance, policy, and dreams.
personal-knowledge-graph
Use when maintaining a LoomKG/Obsidian knowledge graph.
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.