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 Owl-Listener/ai-design-skills --skill few-shot-patternsgit clone --depth 1 https://github.com/Owl-Listener/ai-design-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/owl-listener/ai-design-skills/few-shot-patterns)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/few-shot-patterns"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/few-shot-patterns/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/owl-listener/ai-design-skills/few-shot-patterns"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/few-shot-patterns.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 | $0.00014 | $0.00580 |
| Opus 5 | $0.00007 | $0.00290 |
| Sonnet 5 | $0.00003 | $0.00116 |
| Haiku 4.5 | $0.00001 | $0.00058 |
Grade A, and why
few-shot-patterns 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Few-Shot Patterns
Few-shot prompting provides examples of input-output pairs that demonstrate the desired behavior. The AI learns the pattern from the examples and applies it to new inputs. The quality of examples directly determines the quality of outputs.
Why Examples Work
Examples communicate what instructions alone cannot:
- Implicit patterns: The AI picks up on format, style, and reasoning patterns from examples without being told explicitly
- Ambiguity resolution: When instructions could be interpreted multiple ways, examples show which interpretation you want
- Quality calibration: Examples set the bar for output quality, length, and depth
- Edge case handling: Examples of tricky cases teach the AI how to handle similar situations
Example Design Principles
Diversity: Examples should cover different scenarios, not repeat the same type
- Include easy cases, hard cases, and edge cases
- Vary the input format and content
- Show different valid output formats if applicable Clarity: Each example should demonstrate one clear pattern
- Avoid examples that could be interpreted multiple ways
- Make the mapping from input to output obvious
- Remove irrelevant variation between examples Quality: Examples set the ceiling for output quality
- Every example should be one you'd be happy to ship
- If the example has a flaw, the AI will replicate that flaw
- Invest time in crafting examples — they're worth more than instructions Ordering: The position of examples matters
- Put the most representative example first
- Put edge cases after common cases
- End with an example closest to the expected input type
How Many Examples
- Zero-shot: No examples. Relies entirely on instructions. Good for simple, well-understood tasks.
- One-shot: Single example. Enough when the pattern is straightforward.
- Few-shot (3-5): Multiple examples. Best for complex patterns or nuanced quality requirements.
- Many-shot (5+): Diminishing returns but useful for highly variable tasks or when consistency is critical. Balance example count against context window cost. More examples means less room for other context.
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 · 48 lines · 14 tokens per session scan A 623e9b92bfe0
few-shot-patterns is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 580 once invoked, about $0.0001 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 2026-08-30.
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