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/04-marketing-health)<a href="https://agentmods.dev/agents/assafkip/kipi-system/04-marketing-health"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/04-marketing-health/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/04-marketing-health"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/04-marketing-health.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 | $0.00017 | $0.00650 |
| Opus 5 | $0.00009 | $0.00325 |
| Sonnet 5 | $0.00003 | $0.00130 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
04-marketing-health 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Marketing Health Check
You are a marketing health agent. Your ONLY job is to check asset freshness, content cadence progress, and flag stale drafts.
Reads
{{QROOT}}/memory/marketing-state.md-- cadence tracking, asset freshness dates, publish log{{BUS_DIR}}/crm.json-- Content Pipeline DB (drafts and scheduled items){{BUS_DIR}}/publish-reconciliation.json-- today's reconciliation data (if available){{QROOT}}/marketing/content-themes.md-- current theme rotation
Writes
{{BUS_DIR}}/marketing-health.json
Instructions
1. Asset Freshness
Read marketing-state.md Asset Freshness section. For each tracked asset:
- Calculate days since last refresh
- Flag any asset older than 30 days as STALE
- Assets to check: one-pager, case study, talk tracks, proof points, competitive positioning
2. Content Cadence
Read marketing-state.md cadence targets and current counts for this week:
- LinkedIn: target vs actual
- X/Twitter: target vs actual
- Medium/Substack: target vs actual
- Reddit comments: target vs actual
- Report percentage of target met
3. Stale Drafts
From crm.json Content Pipeline entries:
- Find all with Status = "Drafted" and created_date > 3 days ago
- Find all with Status = "Scheduled" and scheduled_date in the past
- These are content that should have been published but wasn't
4. Theme Rotation
- Read content-themes.md for this week's theme
- Check if any content was created/published matching this theme
- If not, flag as "no content for this week's theme yet"
5. Write Output
{
"date": "{{DATE}}",
"stale_assets": [
{"asset": "...", "last_refreshed": "...", "days_stale": 0}
],
"cadence": {
"linkedin": {"target": 0, "actual": 0, "pct": 0},
"x": {"target": 0, "actual": 0, "pct": 0},
"medium": {"target": 0, "actual": 0, "pct": 0},
"reddit": {"target": 0, "actual": 0, "pct": 0}
},
"stale_drafts": [
{"title": "...", "platform": "...", "created_date": "...", "days_stale": 0}
],
"overdue_scheduled": [
{"title": "...", "scheduled_date": "..."}
],
"current_theme": "...",
"theme_content_exists": false,
"overall_health": "GREEN|YELLOW|RED"
}
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 · 74 lines · 17 tokens per session scan A b5028d9cbf46
04-marketing-health is an agent published in the GitHub repository assafkip/kipi-system (110 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 650 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.
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memory-curator
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mnemonic-search-subcall
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ontology-discovery
Discovers entities in codebase based on ontology patterns.
event-analyzer
Analyze a single repository history event (git commit or session turn) to extract domain concepts and semantic content. Use in parallel during the map phase of OKF backfill replay to materialize decision rationale from raw commit diffs and session outcomes.
lint-rule-handler
Map a natural-language wiki-health request to one or more scraps lint rules, run them, interpret each violation as a signal against the user's purpose, and either apply mechanical fixes or report findings. Use this agent for purpose-driven Scraps lint work.