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 Stijnman/grok-custom-skills --skill social-media-profile-findergit clone --depth 1 https://github.com/Stijnman/grok-custom-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/stijnman/grok-custom-skills/social-media-profile-finder)<a href="https://agentmods.dev/skills/stijnman/grok-custom-skills/social-media-profile-finder"><img src="https://agentmods.dev/badge/skills/stijnman/grok-custom-skills/social-media-profile-finder/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/stijnman/grok-custom-skills/social-media-profile-finder"><img src="https://agentmods.dev/badge/skills/stijnman/grok-custom-skills/social-media-profile-finder.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 | $0.00073 | $0.00839 |
| Opus 5 | $0.00036 | $0.00419 |
| Sonnet 5 | $0.00015 | $0.00168 |
| Haiku 4.5 | $0.00007 | $0.00084 |
Grade A, and why
social-media-profile-finder 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Media Profile Finder
Overview
Identify the most likely public Facebook and Instagram profiles that belong to a person when the user supplies (1) a photo from social media, (2) a name, and (3) a location. Combines reverse-image search, face search, and name-location Google dorks. Public data only.
Inputs
- Photo or photo URL / description of a face-forward image
- Full name or closest known name
- City, region, or country
If any of the three is missing, ask for it before running the full workflow. A name-only or photo-only search is allowed but must be labeled lower confidence.
Workflow
Run these steps in order. Do not skip the photo step when a photo exists.
-
Prepare the image
- Prefer the original file over a screenshot.
- If multiple faces, crop mentally or describe the target face.
- Note lighting, angle, distinctive features, clothing, background landmarks.
-
Reverse image and face search
- Exact-file engines first — Google Lens / Google Images, Yandex Images, TinEye.
- Face engines second — FaceCheck.ID, PimEyes, similar public face indexes.
- Record every URL where the same face or same file appears (profile pics, tagged photos, news, forums).
- Pixel match finds copies of a file. Face match finds the same person in different photos. You usually need both.
-
Name + location search Use web_search and open_page with queries such as
"Full Name" "City"site:facebook.com"Full Name" "City"site:instagram.com"Full Name" instagramCity"Full Name" facebookCity- common username stems of the name plus city Also search the name without quotes and with middle initial / nickname variants.
-
Cross-check A candidate only counts if at least two of these align
- Face match (same person, not a lookalike)
- Name or close alias
- Location consistency (current city, hometown, check-ins, language, friends from that area)
- Photo reuse across platforms Rank High / Medium / Low. Common names without a face match stay Low.
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 First seen · 83 lines · 73 tokens per session scan A 94dbe83e224d
social-media-profile-finder is a skill published in the GitHub repository Stijnman/grok-custom-skills (9 stars, last pushed yesterday), licensed MIT. It adds 73 tokens to every session and 839 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 2026-09-05.
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