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 Saprophytic-seattle561/reverse-skill --skill competition-prompt-injectiongit clone --depth 1 https://github.com/Saprophytic-seattle561/reverse-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/saprophytic-seattle561/reverse-skill/competition-prompt-injection)<a href="https://agentmods.dev/skills/saprophytic-seattle561/reverse-skill/competition-prompt-injection"><img src="https://agentmods.dev/badge/skills/saprophytic-seattle561/reverse-skill/competition-prompt-injection/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/saprophytic-seattle561/reverse-skill/competition-prompt-injection"><img src="https://agentmods.dev/badge/skills/saprophytic-seattle561/reverse-skill/competition-prompt-injection.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 | $0.00105 | $0.00505 |
| Opus 5 | $0.00053 | $0.00253 |
| Sonnet 5 | $0.00021 | $0.00101 |
| Haiku 4.5 | $0.00011 | $0.00051 |
Grade A, and why
competition-prompt-injection 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-prompt-injection — 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.
What it actually says
Competition Prompt Injection
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 is primarily about trust boundaries inside an agentic system.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Identify the first untrusted content that becomes model-visible.
- Map the chain from retrieval, memory, or transcript into planner or executor behavior.
- Record the exact point where text becomes a tool argument, file path, network target, or secret request.
- Prove one minimal exploit chain before exploring variants.
- Keep prompt snippets and tool transitions in compact evidence blocks.
Workflow
1. Map The Control Stack
- Track system, developer, user, retrieved, memory, planner, and tool-response layers separately.
- Distinguish claimed capability from runtime-exposed capability.
- Note what the model can actually call, read, or mutate.
2. Prove The Boundary Crossing
- Reproduce one chain from untrusted text to changed planner behavior, changed tool args, or secret exposure.
- Keep the decisive transcript compact: source chunk, rewritten planner state, final tool invocation.
- Prefer the smallest transcript that still demonstrates the bug.
3. Report By Boundary
- State which layer failed: retrieval, summarizer, planner, executor, tool normalization, or output post-processing.
- Separate instruction drift from actual side effect.
Read This Reference
- Load
references/prompt-injection.mdfor the checklist, evidence layout, and common prompt-boundary pitfalls.
What To Preserve
- Original malicious chunk or prompt
- Intermediate summary or planner drift if it matters
- Final tool args, file paths, or exposed secret surface
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 · 50 lines · 105 tokens per session scan A 7c0344fa94d3
competition-prompt-injection is a skill published in the GitHub repository Saprophytic-seattle561/reverse-skill (1 stars, last pushed today), licensed MIT. It adds 105 tokens to every session and 505 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-prompt-injection, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
burp-scan
Burp Suite scanning via MCP tools — passive traffic analysis, active payload testing, OOB verification, and vulnerability reporting using Burp's proxy, HTTP sender, Collaborator, and scanner APIs. Use when the user has Burp Suite running with the AI Agent MCP server and wants to scan, test, or analyze web traffic…
solopi-ai
A command-line framework for testing Android apps and devices with SoloPi, including on-device or cloud AI decision models. It manages devices, test cases, recorded interactions, replays, performance history, and evidence.
argent-tv-interact
Control and inspect TV apps via argent — Apple TV (tvOS), Android TV (leanback), and Amazon Fire TV (Vega). Boot the target, read focus, navigate with the D-pad remote, type, screenshot, and on Vega debug the JS runtime (evaluate, console logs, network inspector). Use when a task targets a TV (runtimeKind "tv", or…
flutter-feature-based-clean-architecture
Organize Flutter apps with modular feature-based clean architecture. Use when creating features under lib/features/ with domain, data, and presentation layers. Do not use for test-only, BlocBuilder, navigation, or spinner requests.
database-redis
Optimize Redis as cache and coordination infrastructure with TTL, eviction, and latency-aware key design. Use when implementing Redis caching, key invalidation, or Redis performance work.
Detox Mobile Testing
Gray-box end-to-end testing for React Native apps with Detox. Covers .detoxrc.js configuration, build and test commands, matchers, device.launchApp control, automatic synchronization, and macOS CI pipelines.