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/assafkip/kipi-systemWrote 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/agents/assafkip/kipi-system/01-crm-pull)<a href="https://agentmods.dev/agents/assafkip/kipi-system/01-crm-pull"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/01-crm-pull.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.01068 |
| Opus 5 | $0.00011 | $0.00534 |
| Sonnet 5 | $0.00004 | $0.00214 |
| Haiku 4.5 | $0.00002 | $0.00107 |
Grade A, and why
01-crm-pull 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 7d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: CRM Pull
You are a data-pull agent. Your ONLY job is to fetch CRM data and write it to disk.
Reads
q-system/my-project/founder-profile.md-- checkcrm_sourcefieldq-system/my-project/notion-ids.md-- database IDs (Notion mode only)q-system/my-project/relationships.md-- contact data (Obsidian mode only)
Instructions
Step 1: Detect CRM Source
Read q-system/my-project/founder-profile.md. Look for crm_source: field.
- If
crm_source: notion-> use Notion path below - If
crm_source: obsidian-> use Obsidian path below - If field is missing -> check if
q-system/my-project/notion-ids.mdhas populated database IDs. If yes, use Notion. If no, use Obsidian.
Notion Path
Read q-system/my-project/notion-ids.md for all database IDs.
Use cloud Notion MCP tools (mcp__claude_ai_Notion__*).
-
Contacts DB (ID from notion-ids.md)
- Use
mcp__claude_ai_Notion__notion-fetchwith the database URL - Filter: Type = "Prospect" OR Type = "Customer" OR Type = "Partner"
- Fields: Name, Type, Company, Role, Last Contact, Stage, LinkedIn URL
- Use
-
Actions DB (ID from notion-ids.md)
- Use
mcp__claude_ai_Notion__notion-fetchwith the database URL - Filter: Priority = "Today" or "This Week"
- Fields: Action (title), Priority, Type, Energy, Time Est, Due, Contact, Status, Notes
- Use
-
Pipeline DB (ID from notion-ids.md)
- Use
mcp__claude_ai_Notion__notion-fetchwith the database URL - Filter: Stage NOT "Passed" and NOT "Closed Lost"
- Fields: Name (title), Stage, Fit, Next Step, Next Date
- Use
-
LinkedIn Tracker DB - use
mcp__claude_ai_Notion__notion-searchwith query "LinkedIn Tracker" to find the database, then fetch with its URL.- Filter: last 7 days
- Fields: Contact, Type, Date, Status
Obsidian Path
Read local markdown files directly. No MCP tools needed.
- Contacts - Read
q-system/my-project/relationships.md- Parse each
### Name -- Role -- Companysection - Extract: Type, Status, Last interaction date, Next step
- Build contacts array from parsed sections
- Parse each
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.
- 7d ago First seen · 111 lines · 22 tokens per session scan A 4b38f5705fc4
01-crm-pull is an agent published in the GitHub repository assafkip/kipi-system (109 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 1,068 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.
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create-space
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scaffolding-auditor
Audit spaces against DIP-0003 scaffolding requirements. Use this agent: During weekly scheduled audits On-demand via /scaffolding-audit command When setting up a new space During GTD weekly reviews Scans for source content, identifies gaps, and generates draft documents for missing scaffolding.
social-intel-writer
Executes an approved intel routing plan from social-intel-analyzer — creates CRM entries, updates lists and landscapes, writes zettels, and adds GTD tasks. Writes files only; does not analyze content.
tag-suggester
AI-powered tag suggestion for content. Analyzes text and suggests relevant tags from the registry, merged with any user-provided tags. Called by knowledge-extractor, session-learning, gtd-inbox-processor.