few-shot-examples

few-shot-examples is a skill for Claude Code, Codex from jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction. It costs 30 tokens per session (4,945 once invoked), scanned A, a copy of few-shot-examples, MIT.

A collection of example inputs and outputs for construction AI tasks such as classification, data extraction, and analysis.

In plain words
What is it for?
Use it to supply examples for construction classification, extraction, and analysis tasks.
Why use it?
Examples show the AI what kind of answer is expected, which can make results more consistent for construction work.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to supply examples for construction classification, extraction, and analysis tasks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/few-shot-examples
Install

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.

Any agent
npx skills add jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill few-shot-examples
Clone the repo
git clone --depth 1 https://github.com/jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for few-shot-examples

README.md
[![agentmods](https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/few-shot-examples/github.svg)](https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/few-shot-examples)
Your own site
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/few-shot-examples"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/few-shot-examples/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.

agentmods 80×15 button for few-shot-examples

Your own site · 80×15
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/few-shot-examples"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/few-shot-examples.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,945 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.04945
Opus 5 $0.00015 $0.02472
Sonnet 5 $0.00006 $0.00989
Haiku 4.5 $0.00003 $0.00494

Measured 9d ago against content hash cc3cc17ccc66, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

few-shot-examples 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 9d 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.

Origin

This is a copy

100% identical to few-shot-examples — 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.

4_DDC_Curated/Prompt-Engineering/few-shot-examples/SKILL.md · 642 lines

How it starts

The opening of the file, as written. The whole thing — 642 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Few-Shot Examples for Construction AI

Overview

Curated few-shot examples for construction industry AI tasks. These examples improve LLM performance by providing domain-specific context for classification, extraction, and analysis tasks.

Few-Shot Framework

Example Manager

from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
import json
import random

@dataclass
class FewShotExample:
    input: str
    output: str
    explanation: Optional[str] = None
    tags: List[str] = field(default_factory=list)
    difficulty: str = "medium"  # easy, medium, hard
    source: str = ""

@dataclass
class ExampleSet:
    name: str
    description: str
    task_type: str
    examples: List[FewShotExample]
    version: str = "1.0"

    def get_examples(self, n: int = 3, difficulty: str = None) -> List[FewShotExample]:
        """Get n examples, optionally filtered by difficulty."""
        filtered = self.examples
        if difficulty:
            filtered = [e for e in self.examples if e.difficulty == difficulty]
        return filtered[:n]

    def get_random_examples(self, n: int = 3) -> List[FewShotExample]:
        """Get n random examples for variety."""
        return random.sample(self.examples, min(n, len(self.examples)))

    def format_for_prompt(self, n: int = 3) -> str:
        """Format examples for inclusion in prompt."""
        examples = self.get_examples(n)
        formatted = []

        for i, ex in enumerate(examples, 1):
            formatted.append(f"Example {i}:")
            formatted.append(f"Input: {ex.input}")
            formatted.append(f"Output: {ex.output}")
            if ex.explanation:
                formatted.append(f"Explanation: {ex.explanation}")
            formatted.append("")

        return "\n".join(formatted)


class ConstructionExampleLibrary:
    """Library of construction-specific few-shot examples."""

    def __init__(self):
        self.example_sets: Dict[str, ExampleSet] = {}
        self._register_defaults()

    def register(self, example_set: ExampleSet):
        self.example_sets[example_set.name] = example_set

    def get(self, name: str) -> Optional[ExampleSet]:
        return self.example_sets.get(name)

    def _register_defaults(self):
        for example_set in DEFAULT_EXAMPLE_SETS:
            self.register(example_set)

Read the full file on GitHub · 642 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 642 lines · 30 tokens per session scan A cc3cc17ccc66

Subscribe to this mod's changes

few-shot-examples is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo ago), licensed MIT. It adds 30 tokens to every session and 4,945 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to few-shot-examples, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

prompt-optimization

Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…

langwatch/langwatch · 105 tokens

enhance-prompt

Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.

google-labs-code/stitch-skills · 41 tokens

prompt-engineer

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…

Jeffallan/claude-skills · 93 tokens

seedance-vocab-en

This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.

Emily2040/seedance-2.0 · 61 tokens

ideogram4

Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…

digitalsamba/claude-code-video-toolkit · 99 tokens