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 tommy-ca/notion-skills --skill knowledge-capturegit clone --depth 1 https://github.com/tommy-ca/notion-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/tommy-ca/notion-skills/knowledge-capture)<a href="https://agentmods.dev/skills/tommy-ca/notion-skills/knowledge-capture"><img src="https://agentmods.dev/badge/skills/tommy-ca/notion-skills/knowledge-capture/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/tommy-ca/notion-skills/knowledge-capture"><img src="https://agentmods.dev/badge/skills/tommy-ca/notion-skills/knowledge-capture.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 | $0.00013 | $0.00590 |
| Opus 5 | $0.00006 | $0.00295 |
| Sonnet 5 | $0.00003 | $0.00118 |
| Haiku 4.5 | $0.00001 | $0.00059 |
Grade A, and why
knowledge-capture 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
The Knowledge Capture skill transforms conversations, discussions, and unstructured information into organized, structured documentation in Notion. It helps you preserve institutional knowledge by capturing important conversations and converting them into actionable, well-formatted documentation.
When to Use
Use this skill when you need to:
- Convert transcripts or conversation notes into structured documentation
- Create meeting summaries with action items
- Build knowledge base articles from discussions
- Archive important conversations for future reference
- Extract key insights and decisions from discussions
Features
- Smart Content Extraction: Automatically identifies key points, decisions, and action items from conversations
- Structured Organization: Creates well-organized Notion documents with proper hierarchy
- Metadata Capture: Preserves participants, dates, and context information
- Action Item Tracking: Extracts and formats action items with ownership and deadlines
- Cross-linking: Automatically creates links to related documentation and team members
Requirements
- Notion API Access: Integration token with appropriate permissions
- Target Workspace: Notion workspace where documentation will be stored
- Template (Optional): Pre-defined Notion template for consistent structure
Implementation Details
This skill uses the Notion API to:
- Parse input content (text, transcripts, or discussion notes)
- Extract key information using structural analysis
- Format content according to Notion document standards
- Create or update Notion pages with captured knowledge
- Maintain cross-references and relationships
Typical Workflow
Input: Conversation/Discussion
↓
Parse & Extract
↓
Identify: Key Points, Decisions, Action Items
↓
Format for Notion
↓
Create/Update Notion Document
↓
Output: Structured Documentation
Example Use Cases
- Team Meeting Notes
- Input: Meeting transcript
- Output: Organized meeting summary with decisions and next steps
What ships with it
5 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.
- 9d ago First seen · 91 lines · 13 tokens per session scan A a39a68b5158f
knowledge-capture is a skill published in the GitHub repository tommy-ca/notion-skills (62 stars, last pushed 10mo ago), licensed MIT. It adds 13 tokens to every session and 590 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 2026-08-30.
Other skills, from other repositories
ara-research-manager
Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during…
loop-engineering
Use when a repeatable task must become a bounded Trigger -> Execute -> Verify -> State loop, scheduled automation, goal agent, or metric-driven research cycle.
Wikimate Lint
A read-only checker for an organised notes vault, such as an Obsidian collection, and its Notion index. It looks for duplicates, broken links, isolated notes, and missing or invalid metadata.
agentic-engineering
Use when designing or refactoring a model-native engineering workflow with bounded autonomy, probes, custom evaluation, durable state, and verified write-back.
graph-engineering
Use when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph.
harness-engineering
Use when an agent workflow needs production-like runtime controls for context, tools, permissions, observability, scheduling, evaluation, recovery, or maintenance.