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 anysiteio/agent-skills --skill anysite-person-analyzergit clone --depth 1 https://github.com/anysiteio/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/anysiteio/agent-skills/anysite-person-analyzer)<a href="https://agentmods.dev/skills/anysiteio/agent-skills/anysite-person-analyzer"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-person-analyzer/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/anysiteio/agent-skills/anysite-person-analyzer"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-person-analyzer.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 | $0.00090 | $0.06987 |
| Opus 5 | $0.00045 | $0.03494 |
| Sonnet 5 | $0.00018 | $0.01397 |
| Haiku 4.5 | $0.00009 | $0.00699 |
Grade A, and why
anysite-person-analyzer 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 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.
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 — 638 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Person Intelligence Analyzer
Comprehensive multi-platform intelligence analysis combining LinkedIn, Twitter/X, Reddit, GitHub, and web presence data to create actionable intelligence reports with cross-platform personality insights.
v2 Tool Interface
All data fetching uses the unified v2 MCP tools:
execute(source, category, endpoint, params)- Fetch data. Returns first page +cache_key.get_page(cache_key, offset, limit)- Load more items from a previous execute (whennext_offsetis returned).query_cache(cache_key, conditions?, sort_by?, aggregate?, group_by?)- Filter, sort, or aggregate cached data without new API calls.export_data(cache_key, format)- Export full dataset as CSV, JSON, or JSONL. Returns download URL.
v2 Error Handling
All execute() calls may return structured errors with llm_hint fields. When an error occurs:
- 412 errors: Resource not found (e.g., user alias incorrect). Follow the
llm_hintto resolve (typically: search first, then use the returned alias/URN). - 422 errors: Wrong parameter format (e.g., passed alias instead of URN). Check
llm_hintfor the correct format. - Rate limits: Continue with data from other sources. Note limitations in report.
Analysis Workflow
Execute phases sequentially, adapting depth based on available data and user requirements.
Phase 1: Initial Data Collection
Starting with LinkedIn Profile URL:
- Use
execute("linkedin", "user", "user", {"user": "<profile_url_or_alias>", "with_experience": true, "with_education": true, "with_skills": true})with full parameters - Extract and save the full URN (format:
urn:li:fsd_profile:ACoAAABCDEF) from the response - this is critical for all subsequent API calls - Also extract: company URN, current role, location, connections count
- Record profile completeness for confidence scoring
- Save the
cache_keyfrom the response for later use withquery_cache()orexport_data()
IMPORTANT - URN Format:
Always use the complete URN format urn:li:fsd_profile:ACoAAABCDEF from the profile response for all subsequent calls to execute("linkedin", "user", "user_posts", ...), execute("linkedin", "user", "user_comments", ...), and execute("linkedin", "user", "user_reactions", ...). Do not use shortened versions or profile URLs.
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 · 638 lines · 90 tokens per session scan A 10a3b28e8a07
anysite-person-analyzer is a skill published in the GitHub repository anysiteio/agent-skills (19 stars, last pushed 27d ago), licensed MIT. It adds 90 tokens to every session and 6,987 once invoked, about $0.0005 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 2026-08-30.
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