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 calesthio/generative-media-skills --skill amazon-rekognitiongit clone --depth 1 https://github.com/calesthio/generative-media-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/calesthio/generative-media-skills/amazon-rekognition)<a href="https://agentmods.dev/skills/calesthio/generative-media-skills/amazon-rekognition"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/amazon-rekognition/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/calesthio/generative-media-skills/amazon-rekognition"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/amazon-rekognition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 YARA Match · line 16 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Anti-Refusal · line 115 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
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 | $0.00082 | $0.06403 |
| Opus 5 | $0.00041 | $0.03202 |
| Sonnet 5 | $0.00016 | $0.01281 |
| Haiku 4.5 | $0.00008 | $0.00640 |
Grade A, and why
amazon-rekognition 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 9d 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 — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Rekognition for Production Image and Video Understanding
Amazon Rekognition is an AWS computer-vision service for analyzing existing images and videos. Use it to extract metadata, screen user-generated content, build searchable media libraries, support media operations, or add narrowly governed identity-verification steps. Do not use this skill for generating images, editing pixels, creating synthetic media, or judging whether a human reference is ethically usable for generation; those are separate generation and reference-analysis tasks.
This skill treats Rekognition as an analysis system that returns probabilistic metadata. It is not a replacement for editorial review, legal review, consent management, or a human moderation policy.
Evidence Legend
- Documented fact: Stated in AWS documentation or AWS-owned service pages. Consequential facts include source links and volatile facts include a verification date.
- Production heuristic: Operational guidance inferred from documented behavior and common production requirements; validate against the application, jurisdiction, and risk tolerance.
- Empirical observation: A result from a reproducible test. This skill does not include first-party experiments; when you test, record dataset, date, region, API version/model version, thresholds, and confusion matrix.
Activation Boundaries
Use Rekognition when the user asks for:
- Image labels, objects, scenes, concepts, celebrities, image properties, or bounding boxes.
- OCR in images or stored video, especially signs, packaging, screenshots, thumbnails, or metadata extraction.
- Moderation or brand-safety screening for images, stored video, or UGC workflows.
- Custom object, logo, scene, or concept detection with Amazon Rekognition Custom Labels.
- Custom moderation adapters for domain-specific moderation performance.
- Stored-video analysis: labels, moderation, text, celebrities, faces, people tracking, face search, or segment detection.
- Searchable media libraries from Rekognition metadata stored in a search index or database.
- AWS-native async pipelines using S3, IAM, SNS, SQS, Lambda, Kinesis, CloudTrail, and cost controls.
- Face liveness or face search only when the use case is explicit identity verification, access control, fraud prevention, or user-consented account recovery.
What ships with it
1 file 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.
- 9d ago First seen · 391 lines · 0 tokens per session scan A a3a8883e7cb8
amazon-rekognition is a skill published in the GitHub repository calesthio/generative-media-skills (170 stars, last pushed 2mo ago), licensed MIT. It adds 82 tokens to every session and 6,403 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 2026-09-03.
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