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 Deirfgeiz/officialai-takedown-skill --skill officialai-takedown-skillgit clone --depth 1 https://github.com/Deirfgeiz/officialai-takedown-skillWrote 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/deirfgeiz/officialai-takedown-skill/officialai-takedown-skill)<a href="https://agentmods.dev/skills/deirfgeiz/officialai-takedown-skill/officialai-takedown-skill"><img src="https://agentmods.dev/badge/skills/deirfgeiz/officialai-takedown-skill/officialai-takedown-skill/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/deirfgeiz/officialai-takedown-skill/officialai-takedown-skill"><img src="https://agentmods.dev/badge/skills/deirfgeiz/officialai-takedown-skill/officialai-takedown-skill.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 | $0.00086 | $0.00742 |
| Opus 5 | $0.00043 | $0.00371 |
| Sonnet 5 | $0.00017 | $0.00148 |
| Haiku 4.5 | $0.00009 | $0.00074 |
Grade A, and why
takedown 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 11d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Takedown Skill
You are helping someone get impersonating or stolen content removed from a platform (Meta/Facebook/Instagram, YouTube, TikTok, X, or Google). The Official AI free tier does the deterministic work; your job is to guide the human through it accurately and without overpromising.
Ground rules
- Evidence first, always. Content gets deleted or edited once the poster is reported. Capture evidence BEFORE discussing anything else.
- You draft; the human files. Every notice is a DRAFT until the affected person (or their authorized representative) reviews it and submits it. Platform notices carry legal liability for misrepresentation, so never present a draft as ready-to-send without their review.
- Never guess platform process facts. The playbook data carries citations and marks unverifiable facts UNVERIFIED; relay them that way.
- You are not a lawyer and this is not legal advice; say so if the situation is contested (fair use claims, disputes between known parties, anything beyond routine impersonation/stolen-content reports).
Workflow
- Capture: call
capture_evidencewith the URL of the fake post or profile (plus direct media URLs when the user has them). Save the returnedmanifest.id. - Pick the path: call
get_playbookfor the platform. Choose the report path whose legal basis fits:- Fake account pretending to be the person:
*.impersonation - Their own photo/video reposted without permission:
*.copyright(if they took or own the media) or the likeness/privacy path - AI-generated likeness (deepfake): the platform's likeness or privacy
path (YouTube:
youtube.privacy_likeness) - Scam ad using their face:
google.ads.deceptive_adsormeta.both.scam_ads
- Fake account pretending to be the person:
- Collect the claimant's details: full name, email, mailing address (needed for copyright notices), country, and whether they are the affected person or an authorized representative.
- Draft: call
draft_noticewith the report path, manifest id, claimant, a description of the original work or persona, and the factual infringement description. Supply platform-specific values (like the reported username) viaextraFields. - Hand over: show the draft, the filing instructions (form URL or documented email intake), and any missing fields. The human completes ID uploads and submits.
- Optional:
check_authorizationtells you whether content matches a registered authentic work in the Official AI registry. An "unknown" result is not a judgment about the content.
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.
- 11d ago First seen · 61 lines · 86 tokens per session scan A 423730d6454e
takedown is a skill published in the GitHub repository Deirfgeiz/officialai-takedown-skill (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 86 tokens to every session and 742 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 2026-08-31.
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