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 ericrisco/rsc-harness --skill lead-gengit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/lead-gen)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/lead-gen"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/lead-gen.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.02622 |
| Opus 5 | $0.00039 | $0.01311 |
| Sonnet 5 | $0.00015 | $0.00524 |
| Haiku 4.5 | $0.00008 | $0.00262 |
Grade A, and why
lead-gen 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 4d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Gen — The List and the Model That Ranks It
You turn "we sell X to Y" into a deduplicated, scored, compliance-cleared roster of named accounts and people. You define the target, you build the list, you rank it — then you stop. What you produce is a prioritized roster plus the rationale that ranked it; the hand-off is the finish line, not the campaign.
The pipeline — three phases, two gates
Run these in order. Each gate is a hard stop: do not advance until the prior phase produced its artifact.
- Define the target → a falsifiable ICP + persona. Why: you cannot dedupe or score against a vibe; a vague ICP guarantees reps chase the wrong companies.
- Build the list → sourced, deduped, verified rows with provenance. Why: an unverified or undocumented list is a deliverability and legal liability before a single email goes out.
- Score & prioritize → tiered list (A/B/C) with subscores + handoff packet. Why: an unsorted list means reps work the easy-to-reach names, not the right ones.
Between phase 2 and the handoff sits the compliance gate (GDPR LIA + CAN-SPAM). Run it before you hand anything off, never after the first send.
Phase 1 — Define a falsifiable ICP
An ICP is falsifiable when you can look at any company and answer "in or out?" with no judgement call. Write three blocks:
- Firmographic — headcount band, revenue band, region/country, industry/NAICS, funding stage. Numbers, not adjectives.
- Technographic / intent — required stack (e.g. "runs Salesforce"), or an active trigger (hiring for role X, recently raised, surging on a topic). Apollo filters on 1,500+ technologies and active job postings, so make these checkable. (docs.apollo.io People API Search, accessed 2026-06-02.)
- Negative criteria — the
disqualify if…list. This is the half everyone skips and the half that saves the most rep time.
Then write the buyer persona(s) inside the account: title, seniority, the pain they own, the trigger that makes now the moment.
What ships with it
5 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.
- 4d ago First seen · 152 lines · 77 tokens per session scan A 38c380cb2786
lead-gen is a skill published in the GitHub repository ericrisco/rsc-harness (70 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 2,622 once invoked, about $0.0004 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 skills, from other repositories
lead-list-builder
Builds targeted prospect lists from ICP criteria across multiple data sources.
afrexai-lead-hunter
Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Find ideal prospects, enrich with verified data, score against your ICP, and generate personalized outreach — all autonomously.
reddit-leads
Discover B2B leads from Reddit using AI-powered lead scoring via reddapi.dev Leads API. Finds high-intent signals, scores them 0-100, and classifies by lead type (painpoint, solutionrequest, complaint, featurerequest, comparison). Perfect for competitor poaching, pain point discovery, and sales prospecting.
icp-lookalike-expansion
Use this skill when you have a handful of accounts or people who converted and need more like them, or when a black-box 'similar profiles' feed gives you a list you cannot explain. Extracts an explicit similarity signature from the seed and searches on it, so you control what 'similar' means and can widen or tighten…
merge-aliases
Folds two surface names for the same backend system into one canonical entity, keeping every original mention individually retrievable, and refuses to merge pairs that only share spelling.
anchor-and-lock
Consults a check that sits outside the loop system before finalizing any decision the frozen facts bear on, and refuses every attempt by a loop to rewrite a node marked frozen, regardless of how convergent the loop's own reasoning looks.