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 moonlight-lupin/agent-skills --skill people-enrichmentgit clone --depth 1 https://github.com/moonlight-lupin/agent-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/moonlight-lupin/agent-skills/people-enrichment)<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/people-enrichment"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/people-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/moonlight-lupin/agent-skills/people-enrichment"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/people-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 53 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 | $0.00203 | $0.02239 |
| Opus 5 | $0.00102 | $0.01120 |
| Sonnet 5 | $0.00041 | $0.00448 |
| Haiku 4.5 | $0.00020 | $0.00224 |
Grade A, and why
people-enrichment scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `pip install openpyxl` (HTTP uses the stdlib `urllib` — no `requests` needed) How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
People Enrichment & Search (People Data Labs)
One script, scripts/enrich.py, with five subcommands over PDL's Person and Company datasets. All share the same API-key handling, the boolean-PII contact logic, status flags, .xlsx styling, and a --dry-run preflight mode.
| Subcommand | Input | Output | Billing |
|---|---|---|---|
person-enrich |
list of named people | profile + work history | 1 credit / match |
person-identify |
list of people (ambiguous) | several scored candidates each | per PDL identify terms |
person-search |
criteria flags | a list of matching people | 1 credit / record returned |
company-enrich |
list of companies | firmographics | 1 credit / match |
company-search |
criteria flags | a list of matching companies | 1 credit / record returned |
The two search commands return data per result, so cost scales with --size. Always state the rough cost before a large search and keep --size modest unless the user asks for more (max 100 per request).
Scope and routing
Use this skill when the user wants PDL person/company enrichment or search for a legitimate, proportionate purpose. Do not use it to scrape LinkedIn directly, to compile an intrusive profile, or where a host environment mandates a different data provider.
Why People Data Labs, not a LinkedIn scraper
Scraping LinkedIn is fragile and legally risky. This skill uses PDL as a licensed aggregator rather than live LinkedIn scraping. If the user insists on live scraping, explain the trade-off rather than building a scraper.
The API key
scripts/enrich.py needs a PDL key, resolved at runtime in this order: PDL_API_KEY env var → .env in the cwd → .env next to the script. The key must never be written into this skill or any script. If absent, live commands exit with instructions. --dry-run and --self-test do not require a key.
Create the .env only locally:
echo 'PDL_API_KEY=their_key_here' > .env
Treat .env as a secret: don't print, commit, or upload it. --self-test runs offline (canned data, no key) and writes sample people + company sheets so the user can see the output format before spending credits.
What ships with it
7 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.
- 12d ago First seen · 163 lines · 203 tokens per session scan A 10df9cf6fd44
people-enrichment is a skill published in the GitHub repository moonlight-lupin/agent-skills (64 stars, last pushed 5d ago), licensed MIT. It adds 203 tokens to every session and 2,239 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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