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 runxhq/runx --skill lead-enrichmentgit clone --depth 1 https://github.com/runxhq/runxWrote 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/runxhq/runx/lead-enrichment)<a href="https://agentmods.dev/skills/runxhq/runx/lead-enrichment"><img src="https://agentmods.dev/badge/skills/runxhq/runx/lead-enrichment/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/runxhq/runx/lead-enrichment"><img src="https://agentmods.dev/badge/skills/runxhq/runx/lead-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00804 |
| Opus 5 | $0.00012 | $0.00402 |
| Sonnet 5 | $0.00005 | $0.00161 |
| Haiku 4.5 | $0.00002 | $0.00080 |
Grade A, and why
lead-enrichment 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Enrichment
Turn known lead, account, and engagement signals into a reviewable picture of fit, confidence, risk, and the narrowest sensible follow-up. Good enrichment does not mean filling every field. It means making useful claims only where the evidence supports them and making “do not contact” or “learn more first” first- class outcomes.
This is a supplied-signal synthesis skill. It does not scrape the web, query an enrichment vendor, update a CRM, or send outreach. Upstream systems own source collection; downstream routing and provider skills own action.
When to use it
Use lead-enrichment when a product already has bounded signals and needs a
consistent, auditable assessment before routing a lead. It is useful for
combining product activity, declared firmographics, CRM facts, and consent state
without letting an agent silently invent the missing pieces.
Do not use it to infer sensitive traits, reconstruct personal profiles, or manufacture permission from engagement. A recommendation is not consent and a source digest is not proof that Runx independently verified the provider.
How it works
- Supply the known lead fields and typed signals with unique source references, upstream SHA-256 digests, claims, and observation times.
- Deterministic admission checks provenance, freshness, duplicates, consent, suppression, region, and channel constraints before synthesis.
- Opt-out and do-not-contact signals stop the lane immediately. The model never gets to reason its way around them.
- Synthesis builds the lead profile, fit assessment, recommendation, and risk flags using only admitted lead fields and signals.
- Finalization rejects invented source references and any language claiming outreach permission, CRM mutation, or send completion.
Inputs and result
leadcontains known identity and account fields; unknown values remain unknown.signalscontain stablesource_ref,source_digest, type, claim, andobserved_atfields.as_ofandmax_age_daysestablish a reproducible freshness decision.constraintscarry consent, suppression, region, and allowed-channel state.
What ships with it
4 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.
- 8d ago First seen · 86 lines · 23 tokens per session scan A 5e0d371bc8a6
lead-enrichment is a skill published in the GitHub repository runxhq/runx (87 stars, last pushed 3d ago), licensed Apache-2.0. It adds 23 tokens to every session and 804 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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