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 Asaiuta/reverse-workbench-skill --skill competition-stego-mediagit clone --depth 1 https://github.com/Asaiuta/reverse-workbench-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/asaiuta/reverse-workbench-skill/competition-stego-media)<a href="https://agentmods.dev/skills/asaiuta/reverse-workbench-skill/competition-stego-media"><img src="https://agentmods.dev/badge/skills/asaiuta/reverse-workbench-skill/competition-stego-media/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/asaiuta/reverse-workbench-skill/competition-stego-media"><img src="https://agentmods.dev/badge/skills/asaiuta/reverse-workbench-skill/competition-stego-media.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 | $0.00100 | $0.00599 |
| Opus 5 | $0.00050 | $0.00300 |
| Sonnet 5 | $0.00020 | $0.00120 |
| Haiku 4.5 | $0.00010 | $0.00060 |
Grade A, and why
competition-stego-media 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 10d 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.
This is a copy
100% identical to competition-stego-media — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competition Stego Media
Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.
Use this skill when the challenge lives inside a media container, hidden channel, or appended payload rather than a conventional crypto blob.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Confirm the real container type, dimensions, duration, codec, and chunk layout before guessing a hidden layer.
- Check metadata, thumbnails, sidecar files, and appended trailers before deeper signal-domain work.
- Rank candidate channels by evidence: alpha, palette, LSB, transform-domain residue, frame order, or container slack.
- Preserve each extracted layer separately so the transform chain stays reproducible.
- Stop when the hidden payload is reproduced, not merely suspected.
Workflow
1. Establish Container Truth
- Inspect headers, chunk tables, EXIF or document metadata, container indexes, thumbnails, and file size anomalies.
- Compare declared format against observed structure to catch polyglots, appended archives, or malformed trailers.
- Record exact offsets, frame numbers, or channel boundaries that look promising.
2. Inspect Candidate Channels
- Check alpha, palette order, RGB or YUV planes, LSBs, spectrogram features, document object streams, or video frame deltas.
- Prefer evidence-driven attempts over brute forcing every transform.
- Note whether the payload is plain bytes, another media layer, compressed data, or an encrypted blob.
3. Reconstruct The Hidden Payload Path
- Keep the chain in order: container -> channel or carrier -> extraction -> decompression or decode -> final parse.
- Separate extraction success from final interpretation; a channel hit is not the same as artifact recovery.
- If the problem becomes primarily about cryptography after extraction, hand off to the broader crypto skill.
What ships with it
2 files 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.
- 10d ago First seen · 51 lines · 100 tokens per session scan A 34c6ec0ebc77
competition-stego-media is a skill published in the GitHub repository Asaiuta/reverse-workbench-skill (2 stars, last pushed 27d ago), licensed MIT. It adds 100 tokens to every session and 599 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to competition-stego-media, differing in 0 lines, and is treated as a copy.
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