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 datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill few-shot-examplesgit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/few-shot-examples)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/few-shot-examples"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/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.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/few-shot-examples"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/few-shot-examples.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00030 | $0.04945 |
| Opus 5 | $0.00015 | $0.02472 |
| Sonnet 5 | $0.00006 | $0.00989 |
| Haiku 4.5 | $0.00003 | $0.00494 |
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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- few-shot-examples — 100% identical, 0 lines differ
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)
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.
- 8d ago First seen · 642 lines · 30 tokens per session scan A cc3cc17ccc66
few-shot-examples is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 20d 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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