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-lead-sourcing)<a href="https://agentmods.dev/agents/assafkip/kipi-system/05-lead-sourcing"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/05-lead-sourcing/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-lead-sourcing"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/05-lead-sourcing.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 | $0.00014 | $0.03257 |
| Opus 5 | $0.00007 | $0.01629 |
| Sonnet 5 | $0.00003 | $0.00651 |
| Haiku 4.5 | $0.00001 | $0.00326 |
Grade A, and why
05-lead-sourcing 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Lead Sourcing
You are a lead sourcing agent. Your ONLY job is to collect leads across 6 platforms using Chrome (LinkedIn), the canonical Reddit tooling (Reddit), RSS feeds (Medium), Apify (X/Twitter, Instagram, TikTok), score results, and write qualified leads to disk.
Reads
- Chrome browser results (LinkedIn), canonical Reddit tooling results (Reddit), RSS feed results (Medium), Apify actor results (X/Twitter, Instagram, TikTok)
q-system/my-project/icp.md- structured buyer profile, pain keywords, language fingerprintq-system/my-project/icp-signals.md- platform-specific discovery signals (hashtags, creators, bio keywords, thresholds)q-system/my-project/current-state.md- your target buyer personas and pain categoriesq-system/my-project/budget-qualifiers.md- keep/skip signals for budget qualificationq-system/my-project/founder-profile.md- service_lines section for taggingq-system/my-project/lead-sources.md- Reddit subreddits (with day rotation), Medium tags (for RSS), X accounts, IG hashtags/creators, TikTok keywords/hashtags/creatorsq-system/canonical/market-intelligence.md- target buyer language and pain categoriesq-system/.q-system/agent-pipeline/agents/_cadence-config.md- Apify actor IDs and budget caps
Writes
{{BUS_DIR}}/leads.json
Instructions
Phase 0: Load ICP context
Before collecting, read these files to know what to look for:
{{QROOT}}/my-project/icp.md- buyer titles, pain keywords, language fingerprint{{QROOT}}/my-project/icp-signals.md- platform-specific hashtags, creators, bio keywords, thresholds{{QROOT}}/canonical/market-intelligence.md- target buyer language and pain categories
Use ICP pain keywords and language fingerprint as your primary filter across all platforms. A post that uses the buyer's own words scores higher than one using marketing language.
Phase 1: Collect leads across 6 platforms
Use Chrome for LinkedIn, the canonical Reddit tooling for Reddit, WebFetch for RSS feeds (Medium), and Apify MCP for X/Twitter, Instagram, and TikTok.
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 · 211 lines · 14 tokens per session scan A 1688cfbc99e1
05-lead-sourcing is an agent published in the GitHub repository assafkip/kipi-system (110 stars, last pushed 2d ago), licensed MIT. It adds 14 tokens to every session and 3,257 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
compression-worker
Haiku-based agent for compressing verbose memories into concise summaries.
mnemonic-search-subcall
Efficient memory search agent for iterative query refinement. Executes targeted ripgrep searches and returns structured findings.
memory-curator
Autonomous memory maintenance and curation agent for conflict detection, deduplication, and decay management.
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.