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 timurgaleev/memex --skill meeting-ingestiongit clone --depth 1 https://github.com/timurgaleev/memexWrote 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/timurgaleev/memex/meeting-ingestion)<a href="https://agentmods.dev/skills/timurgaleev/memex/meeting-ingestion"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/meeting-ingestion/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/timurgaleev/memex/meeting-ingestion"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/meeting-ingestion.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 | $0.00042 | $0.00950 |
| Opus 5 | $0.00021 | $0.00475 |
| Sonnet 5 | $0.00008 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
meeting-ingestion 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 11d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Ingestion Skill
Filing rule: Read
_brain-filing-rules.md(viaget_skill _brain-filing-rules) before creating any new page.
Contract
This skill guarantees:
- Meeting page created with attendees, summary, key decisions, action items
- EVERY attendee gets a people page (created or updated)
- EVERY company discussed gets entity propagation
- Timeline events on ALL mentioned entities (timeline merge)
- Meeting is NOT fully ingested until enrich runs for every entity
- Back-links created bidirectionally
Convention: See
conventions/quality.md(viaget_skill conventions/quality) for Iron Law back-linking.
Every attendee and company mentioned MUST get a back-link from their page to the meeting page. An unlinked mention is a broken brain.
Phases
Phase 1: Parse the transcript
Extract from the transcript:
- Attendees (names, roles if available)
- Date, time, duration
- Key topics discussed
- Decisions made
- Action items with owners
- Companies and projects mentioned
Phase 2: Create meeting page
Write the page with page_put under meetings/:
# {Meeting Title} — {Date}
**Attendees:** {list with links to people pages}
**Date:** {YYYY-MM-DD}
**Duration:** {if available}
## Summary
{3-5 bullet key outcomes}
## Key Decisions
{Decisions with context}
## Action Items
{Tasks with owners and deadlines}
## Discussion Notes
{Structured notes by topic}
Phase 3: Attendee enrichment (MANDATORY)
For EACH attendee:
search "{name}"— does a people page exist?- If NO → create via enrich skill (this is mandatory, not optional)
- If YES → update compiled truth with meeting context
- Add a timeline event on the person's page:
add_timeline_eventwith the person's slug, the meeting date, and"Attended <meeting-title>"
Note: Typed-link derivation runs when the meeting page is written via
page_put: attended links from the meeting to each attendee whose page
is referenced as [Name](people/slug) are derived from the page body. If
a mention is NOT a resolvable page reference, create the link explicitly
with link (kind attended). You DO always need add_timeline_event
for dated events — link derivation only handles links, not timeline
entries.
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.
- 11d ago First seen · 134 lines · 42 tokens per session scan A e5ef173f0351
meeting-ingestion is a skill published in the GitHub repository timurgaleev/memex (8 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 950 once invoked, about $0.0002 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 skills, from other repositories
kb
Set up, evolve, or operate a hraness/kb local-first Markdown knowledge base for coding-agent memory. Use when a user asks to design KB conventions or a recurring KB ritual; search or query a KB or Obsidian vault; load or recover repository context, plans, decisions, concepts, backlinks, semantic search, or Git…
grounded-knowledge-workflow
Use Grounded Knowledge Engine to select an isolated workspace, answer from local evidence, resume an explicitly identified project, create an explicit project checkpoint, and retain durable knowledge. Trigger when a user asks about their documents, workspace or client context, previous research, project state…
weasley-deepmind
A reference for using weasley-deepmind, a server that stores and retrieves an AI agent’s long-term memories between sessions. It describes memory storage, search, language processing, and related tools.
gbrain-ingest
Ingest meetings, articles, docs, and conversations into the brain. Updates existing pages with new info, creates pages for new entities, maintains cross-references.
gbrain-maintain
Periodic brain maintenance. Finds contradictions, stale info, orphan pages, and missing cross-references to keep the knowledge graph healthy.
gbrain-query
Answer questions from the brain using FTS5 + semantic search + structured queries. Synthesizes across multiple pages with citations.