# Few Shot Curator

> When a task needs a specific format or style and zero-shot keeps drifting, give 2-5 carefully chosen examples instead of more instructions. Use for extraction/classification/formatting/tone tasks the model keeps getting subtly wrong. Trigger with /few-shot-curator or "add examples", "show it a few examples", "few-shot this".

- Skill: `zavelinski/few-shot-curator` (Agent Skill)
- Install (CLI): `npx skillmds@latest add zavelinski/few-shot-curator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zavelinski/few-shot-curator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: zavelinski (https://skillmd.com/u/zavelinski)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zavelinski/few-shot-curator

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# few-shot-curator

When zero-shot drifts on format or style, a few well-chosen examples steer it better than another paragraph of instructions. The trick is WHICH examples: relevant, diverse, correct.

## Why this exists (evidence)

- In-context learning is strongly sensitive to which examples you pick: good example selection beats random examples, and relevant + diverse examples beat more-but-noisy ones (Liu et al., "What Makes Good In-Context Examples for GPT-3?", and the broader ICL literature). The win is from curation, not quantity.
- It targets the failure where the model "understands" the task but keeps producing the wrong shape/tone, and adding more prose instructions does not fix it.

## When to use

- Output format/structure the model keeps getting subtly wrong (a specific JSON, a commit-message style, a tone).
- Extraction/classification where edge cases need to be shown, not described.
- NOT when zero-shot already nails it (examples just cost tokens), and not as a crutch for an unclear task (fix the instruction first).

## The method

1. **Pick 2-5 examples, curated:**
   - **Relevant:** close to the actual inputs you expect.
   - **Diverse:** cover the range and the edge cases (the tricky ones, not five near-duplicates).
   - **Correct:** each example's output is exactly what you want (a wrong example teaches the wrong thing).
   - **Format-faithful:** examples show the EXACT output shape you want back.
2. **Order intentionally:** put the most representative/important last (recency in the prompt carries weight).
3. **Keep them lean:** shortest examples that still demonstrate the pattern (compose with prompt-compression).
4. **Iterate:** if it still drifts on a case, add an example of THAT case rather than more prose.

## How to run it

- Maintain a small bank of golden input->output pairs for recurring task types; select the relevant few per task.
- For classification, include at least one example per class and per known confusable.
- Verify the examples are correct before using them (a bad few-shot example is worse than none).

## Composes with

- `structured-output`: examples + a schema = strong, consistent shape.
- `prompt-compression`: keep the examples minimal so they do not bloat context.
- `self-consistency`: if unsure which examples help, test a couple of sets and keep the one that yields consistent correct output.

## Honest limits

- Examples cost context tokens every call; do not few-shot what zero-shot already does.
- Wrong/biased examples actively teach the wrong behavior; curate, do not grab the first samples.
- More examples is not better past a few; relevance and correctness beat count.