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 CamiProject/semantic-deer-flow --skill surprise-megit clone --depth 1 https://github.com/CamiProject/semantic-deer-flowWrote 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/camiproject/semantic-deer-flow/surprise-me)<a href="https://agentmods.dev/skills/camiproject/semantic-deer-flow/surprise-me"><img src="https://agentmods.dev/badge/skills/camiproject/semantic-deer-flow/surprise-me/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/camiproject/semantic-deer-flow/surprise-me"><img src="https://agentmods.dev/badge/skills/camiproject/semantic-deer-flow/surprise-me.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 | $0.00067 | $0.00549 |
| Opus 5 | $0.00034 | $0.00275 |
| Sonnet 5 | $0.00013 | $0.00110 |
| Haiku 4.5 | $0.00007 | $0.00055 |
Grade A, and why
surprise-me 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 surprise-me — 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Surprise Me
Deliver an unexpected, delightful experience by dynamically discovering available skills and combining them creatively.
Workflow
Step 1: Discover Available Skills
Read all the skills listed in the <available_skills>.
Step 2: Plan the Surprise
Select 1 to 3 skills and design a creative mashup. The goal is a single cohesive deliverable, not separate demos.
Creative combination principles:
- Juxtapose skills in unexpected ways (e.g., a presentation about algorithmic art, a research report turned into a slide deck, a styled doc with canvas-designed illustrations)
- Incorporate the user's known interests/context from memory if available
- Prioritize visual impact and emotional delight over information density
- The output should feel like a gift — polished, surprising, and fun
Theme ideas (pick or remix):
- Something tied to today's date, season, or trending news
- A mini creative project the user never asked for but would love
- A playful "what if" concept
- An aesthetic artifact combining data + design
- A fun interactive HTML/React experience
Step 3: Fallback — No Other Skills Available
If no other skills are discovered (only surprise-me exists), use one of these fallbacks:
- News-based surprise: Search today's news for a fascinating story, then create a beautifully designed HTML artifact presenting it in a visually striking way
- Interactive HTML experience: Build a creative single-page web experience — generative art, a mini-game, a visual poem, an animated infographic, or an interactive story
- Personalized artifact: Use known user context to create something personal and delightful
Step 4: Execute
- Read the full SKILL.md body of each selected skill
- Follow each skill's instructions for technical execution
- Combine outputs into one cohesive deliverable
- Present the result with minimal preamble — let the work speak for itself
Step 5: Reveal
Present the surprise with minimal spoilers. A short teaser line, then the artifact.
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 · 54 lines · 67 tokens per session scan A e93e72ca445d
surprise-me is a skill published in the GitHub repository CamiProject/semantic-deer-flow (21 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 549 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to surprise-me, differing in 0 lines, and is treated as a copy.
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