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 apify/awesome-skills --skill apify-lead-scoring-enrichmentgit clone --depth 1 https://github.com/apify/awesome-skillsWrote 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/apify/awesome-skills/apify-lead-scoring-enrichment)<a href="https://agentmods.dev/skills/apify/awesome-skills/apify-lead-scoring-enrichment"><img src="https://agentmods.dev/badge/skills/apify/awesome-skills/apify-lead-scoring-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/apify/awesome-skills/apify-lead-scoring-enrichment"><img src="https://agentmods.dev/badge/skills/apify/awesome-skills/apify-lead-scoring-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 113 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 210 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 224 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 318 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00208 | $0.04510 |
| Opus 5.5 | $0.00083 | $0.01804 |
| Sonnet 5.5 | $0.00042 | $0.00902 |
| Haiku 4.5 | $0.00021 | $0.00451 |
Grade A, and why
apify-lead-scoring-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 1mo 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Scoring & Enrichment
Turn a CSV of company URLs into a scored, contact-enriched pitch list. The agent asks the user for scoring rules in plain English ("+10 if using Shopify", "-5 if company size <10"), picks an enrichment path (departments or copywriters), and orchestrates six Apify Actors through four helper scripts.
Prerequisites
- Apify account with an active
APIFY_TOKEN(Console → Settings → Integrations) - Node.js 20.6+ (needed for native
--env-filesupport) - A
.envfile at the skill root containingAPIFY_TOKEN=apify_api_... - One-time inside
scripts/:npm install(installscsv-parse,csv-stringify)
Optional but recommended: the Apify CLI (npm i -g apify-cli) for ad-hoc Actor
calls. The helper scripts hit the REST API directly and do not need the CLI.
Workflow
Copy this checklist and track progress:
Task Progress:
- [ ] Step 1: Collect CSV path and validate required column (company_url)
- [ ] Step 2: Collect scoring rules per source (tech / content / metadata)
- [ ] Step 3: Collect enrichment path (departments OR copywriters)
- [ ] Step 4: Run scoring Actors (writes scoring.json)
- [ ] Step 5: Apply scoring rules per lead → assign per-source scores + outreach_hook (writes scored.json)
- [ ] Step 5b: Compute theoretical min/max score, ask user for qualification threshold, filter leads → qualified_leads.csv
- [ ] Step 6: Run enrichment path against qualified_leads.csv (writes enrichment.json)
- [ ] Step 7: Merge scoring + enrichment onto the ORIGINAL CSV → leads.enriched.csv (qualified column marks who made the cut)
Step 1: CSV intake
Ask the user for the CSV path. Required column: company_url. Recognized
optional columns pass through untouched: company_name, first_name,
last_name, role, department. Reject the run if company_url is
missing. Trim to a bare domain (strip trailing slash, www. optional) when
feeding downstream Actors that expect a domain.
What ships with it
12 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.
- .gitignore 350 B
- examples/leads.example.csv 103 B
- examples/scoring-rules.example.md 1.7 KB
- references/actor-index.md 7.4 KB
- references/gotchas.md 5.5 KB
- scripts/apify_client.js 4.2 KB runs code
- scripts/enrich_copywriters.js 7.7 KB runs code
- scripts/enrich_departments.js 7.8 KB runs code
- scripts/filter_qualified.js 1.8 KB runs code
- scripts/merge_output.js 3.8 KB runs code
- scripts/package.json 276 B
- scripts/run_scoring.js 4.2 KB runs code
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.
- 1mo ago First seen · 325 lines · 208 tokens per session scan A a07e763180a7
apify-lead-scoring-enrichment is a skill published in the GitHub repository apify/awesome-skills (283 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 208 tokens to every session and 4,510 once invoked, about $0.0008 per session on Opus 5.5. 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 2026-08-30.
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