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 jlaska/obsidian-claude-plugins --skill people-researchgit clone --depth 1 https://github.com/jlaska/obsidian-claude-pluginsWrote 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/jlaska/obsidian-claude-plugins/people-research)<a href="https://agentmods.dev/skills/jlaska/obsidian-claude-plugins/people-research"><img src="https://agentmods.dev/badge/skills/jlaska/obsidian-claude-plugins/people-research/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/jlaska/obsidian-claude-plugins/people-research"><img src="https://agentmods.dev/badge/skills/jlaska/obsidian-claude-plugins/people-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00042 | $0.03291 |
| Opus 5 | $0.00021 | $0.01646 |
| Sonnet 5 | $0.00008 | $0.00658 |
| Haiku 4.5 | $0.00004 | $0.00329 |
Grade A, and why
people-research 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 7d 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
People Research
Researches a person from LinkedIn, GitHub, the web, and Red Hat LDAP, then creates or updates an Obsidian People note with a structured dossier: career arc, technical focus, connection context, and links.
When to Use
Invoke /people-research [name or context] when:
- Preparing for a meeting with someone you don't know well
- Creating a People note for a new contact
- Enriching an existing sparse People note with research
- A calendar attendee doesn't have a People note yet
Works for external contacts (dossier format) and Red Hat employees (vault template format with LDAP enrichment). Complements /people-enrichment, which does batch LDAP updates for Red Hat contacts — this skill does deep single-person research across multiple sources.
Input can be:
- A full name:
/people-research Spencer Smith - A name + company:
/people-research Spencer Smith at SideroLabs - A meeting reference:
/people-research the Spencer from my 11am meeting today - A LinkedIn username or email
Workflow
Step 1 — Discover Vault Root
cat ~/Library/Application\ Support/obsidian/obsidian.json
Parse the JSON to find the vault with "open": true. Extract vault_root.
Step 2 — Resolve Person Identity
If the input is a meeting reference (e.g., "Spencer from my 2pm" or "the person on my calendar today"):
- Search
<vault_root>/MEETINGS/for the meeting file matching the description - Read the file's
attendeesfrontmatter field to identify the target person
If the input is a name (with or without company hint):
- Check if a People file already exists:
<vault_root>/PEOPLE/<First Last>.md - If it exists, read frontmatter — extract any known email, LinkedIn username, or company. These seed the research.
Step 3 — Search and Disambiguate
Run searches in parallel:
mcp__linkedin__search_peoplewith the person's name (and company if known)WebSearchwith query:"<Full Name>" <company if known> site:linkedin.com
If one strong match (name + company align, or only one plausible result): State who was found — "Found Spencer Smith, Senior Director at Sidero Labs (Greenville, SC) — proceeding." — and continue without asking. The user can interrupt if wrong.
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.
- 7d ago Changed · +6 lines 46386c3dc697
- 10d ago First seen · 362 lines · 42 tokens per session scan A 5047f236cfdf
people-research is a skill published in the GitHub repository jlaska/obsidian-claude-plugins (2 stars, last pushed 9d ago), licensed MIT. It adds 42 tokens to every session and 3,291 once invoked, about $0.0002 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 2026-08-31.
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