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 skills add glebis/claude-skills --skill coaching-session-summarizergit clone --depth 1 https://github.com/glebis/claude-skillsWrote 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/glebis/claude-skills/coaching-session-summarizer)<a href="https://agentmods.dev/skills/glebis/claude-skills/coaching-session-summarizer"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/coaching-session-summarizer/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/glebis/claude-skills/coaching-session-summarizer"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/coaching-session-summarizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00072 | $0.01255 |
| Opus 5.5 | $0.00029 | $0.00502 |
| Sonnet 5.5 | $0.00014 | $0.00251 |
| Haiku 4.5 | $0.00007 | $0.00126 |
Grade A, and why
coaching-session-summarizer 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 2mo 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coaching Session Summarizer
Overview
Analyzes a coaching/therapy session transcript and appends a structured summary (key insights, decisions, action items, deep analysis, connected trails) to the note.
The agent (Claude Code) performs the analysis directly — reading the transcript and writing the summary in this session. There is no Anthropic API call and no billing; it runs entirely on the active subscription. A legacy API-based script is kept only as a headless fallback (see bottom).
When to Use This Skill
- A new Fathom/Granola transcript was synced to the vault (coaching or therapy)
- User asks to summarize/analyze a session (
/summarize-session [file]or similar) - After
calendar-syncor a Granola export, when a new*-coaching.md,*-therapy.md, or*-session.mdfile appears — offer to summarize it
Workflow (agent-driven — default)
Do this in-session with native tools. No API key required.
Step 1 — Gather context
Run the deterministic helper to get the transcript text, previous sessions, and the trail list in one shot:
python3 ~/.claude/skills/coaching-session-summarizer/scripts/gather_context.py \
<transcript-file> --vault ~/Brains/brain
It prints:
- Previous sessions with the same participant (paths) —
Readthese only in deep mode, for cross-session pattern detection - Available trails — pick 2–4 most relevant to link
- Session content — the summary + transcript to analyze (any prior AI-Generated Summary is stripped so re-runs stay clean)
Pass --participant <name-slug> if the filename doesn't encode the person
(e.g. Granola exports titled by topic): --participant gleb-kalinin.
Step 2 — Analyze
Read the session content and extract, in the analytical voice of a session analyst (objective, using the speaker's authentic language where it matters):
- Key Insights — 3–5 main realizations / breakthroughs / observations
- Decisions Made — concrete choices or commitments
- Action Items — specific next steps; prefix time-sensitive ones with
[URGENT]and scheduling items with[SCHEDULING] - Session Themes — 2–3 recurring topics or patterns
What ships with it
2 files 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.
- 2mo ago First seen · 144 lines · 72 tokens per session scan A 540382f62300
coaching-session-summarizer is a skill published in the GitHub repository glebis/claude-skills (407 stars, last pushed 2d ago), licensed MIT. It adds 72 tokens to every session and 1,255 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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