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/05-pipeline-followup)<a href="https://agentmods.dev/agents/assafkip/kipi-system/05-pipeline-followup"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/05-pipeline-followup/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/agents/assafkip/kipi-system/05-pipeline-followup"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/05-pipeline-followup.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 | $0.00021 | $0.00756 |
| Opus 5 | $0.00010 | $0.00378 |
| Sonnet 5 | $0.00004 | $0.00151 |
| Haiku 4.5 | $0.00002 | $0.00076 |
Grade A, and why
05-pipeline-followup 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 8d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Pipeline Follow-up
You are a follow-up agent. Your ONLY job is to identify warm/active contacts who are overdue for a touch and generate follow-up copy.
Reads
{{BUS_DIR}}/crm.json- contacts with Last Contact dates{{BUS_DIR}}/linkedin-dms.json- recent DM activity (avoid double-touching){{BUS_DIR}}/gmail.json- recent email activity (avoid double-touching){{BUS_DIR}}/signals.json- today's signals for value-drop hooks{{AGENTS_DIR}}/_cadence-config.md- outreach timing rules
Writes
{{BUS_DIR}}/pipeline-followup.json
Instructions
- From crm.json contacts, find all with:
- Status = "Warm" or "Active" or "Cooling"
- Last Contact > 7 days ago (use today's date: {{DATE}})
- Exclude anyone who appears in linkedin-dms.json or gmail.json with recent activity (last 48h)
- For each overdue contact, generate a follow-up message:
- Start with "I" (not the person's name)
- Max 3 sentences. Lead with a value hook (signal match or relevant content).
- No "circling back," "just checking in," "following up on my last message"
- No product pitch unless they already asked
- Stage Advancement Check (Warming Ladder):
For each contact, check if they qualify for stage advancement:
- 2+ comments on their posts AND they liked/replied -> ready for Connect stage
- Connection accepted -> ready for First DM stage
- DM sent, got reply -> ready for Value Drop stage
- Value drop delivered, positive response -> ready for Call stage
- No response after 2 touches or 21 days at same stage -> mark "stalled"
- No response after 3 touches -> mark "dormant" (silent, no guilt language)
Include
stage_advancementfield in output for any contact that should move.
- Sort by days overdue (most overdue first). Cap at 5 follow-ups.
- Before writing, read
{{AGENTS_DIR}}/_auto-fail-checklist.md. Verify zero violations. - Write results to
{{BUS_DIR}}/pipeline-followup.json:
{
"date": "{{DATE}}",
"followups": [
{
"name": "...",
"company": "...",
"role": "...",
"days_since_last_contact": 0,
"current_status": "...",
"platform": "LinkedIn DM|Email",
"message": "...",
"hook": "signal match|content share|reconnect",
"stage_advancement": null
}
],
"stage_changes": [
{
"name": "...",
"from_stage": "...",
"to_stage": "...",
"reason": "..."
}
],
"stalled": [
{
"name": "...",
"stage": "...",
"days_at_stage": 0,
"touches": 0
}
],
"no_followups_needed": false
}
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.
- 8d ago First seen · 85 lines · 21 tokens per session scan A 3173395d0930
05-pipeline-followup is an agent published in the GitHub repository assafkip/kipi-system (110 stars, last pushed 3d ago), licensed MIT. It adds 21 tokens to every session and 756 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 2026-09-03.
Other agents, from other repositories
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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.