adaption-ai

adaption-ai is a skill for Claude Code, Codex from svngoku/coding-agents-skills. It costs 149 tokens per session (1,646 once invoked), scanned A, original, MIT.

A Python SDK guide for Adaption AI, a service that adds or adapts examples in datasets used for machine-learning systems.

In plain words
What is it for?
It is for building pipelines that import CSV, JSON, JSONL, or Parquet data from local files, Hugging Face, or Kaggle, then augment and evaluate it.
Why use it?
It documents the steps for uploading data, running an adaptation job, checking its quality, and downloading the result.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for building pipelines that import CSV, JSON, JSONL, or Parquet data from local files, Hugging Face, or Kaggle, then augment and evaluate it.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/svngoku/coding-agents-skills/adaption-ai
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 svngoku/coding-agents-skills --skill adaption-ai
Clone the repo
git clone --depth 1 https://github.com/svngoku/coding-agents-skills

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 adaption-ai

README.md
[![agentmods](https://agentmods.dev/badge/skills/svngoku/coding-agents-skills/adaption-ai.svg)](https://agentmods.dev/skills/svngoku/coding-agents-skills/adaption-ai)
Your own site
<a href="https://agentmods.dev/skills/svngoku/coding-agents-skills/adaption-ai"><img src="https://agentmods.dev/badge/skills/svngoku/coding-agents-skills/adaption-ai.svg" alt="Measured on agentmods" height="20"></a>
Per session 149 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,646 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 $0.00149 $0.01646
Opus 5 $0.00075 $0.00823
Sonnet 5 $0.00030 $0.00329
Haiku 4.5 $0.00015 $0.00165

Measured 8d ago against content hash d18f8a43e66c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

adaption-ai scanned grade A with 1 finding 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.

The scan reads SKILL.md. This mod also ships 4 executable files (graders/check.py, scripts/async_pipelines.py, scripts/e2e_pipeline.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

| Full run parameters, all API endpoints, HTTP/curl examples | `references/api-reference.md` |
skills/adaption-ai/SKILL.md · 167 lines

How it starts

The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adaption AI SDK Skill

Build dataset augmentation pipelines with Adaption's Adaptive Data platform. This skill covers the full lifecycle: ingest → adapt → wait → evaluate → export.

Quick Reference

pip install adaption
from adaption import Adaption
client = Adaption(api_key="pt_live_...")  # or set ADAPTION_API_KEY env var

Async client: from adaption import AsyncAdaption

Core Lifecycle

  1. Ingest — Upload local file, import from HuggingFace, or import from Kaggle
  2. Adapt — Start an augmentation run with column mapping + optional controls
  3. Wait — Poll for completion with exponential backoff
  4. Evaluate — Fetch quality metrics (score_before/after, improvement %)
  5. Export — Download augmented dataset via presigned URL

Ingest Methods

Local file upload

Supported formats: .csv, .json, .jsonl, .parquet

result = client.datasets.upload_file("training_data.csv")
result = client.datasets.upload_file("data.csv", name="my-dataset")  # custom name
dataset_id = result.dataset_id

Hugging Face import (async on server — poll before running)

resp = client.datasets.create_from_huggingface(
    url="https://huggingface.co/datasets/org/repo",
    files=["train.csv"],
)

Kaggle import (requires Kaggle API creds registered in Adaption settings)

resp = client.datasets.create_from_kaggle(
    url="https://www.kaggle.com/datasets/org/dataset-name",
    files=["data.csv"],
)

Running Adaptation Jobs

datasets.run() — the central method

Required: dataset_id, column_mapping with at least "prompt" key.

run = client.datasets.run(
    dataset_id,
    column_mapping={
        "prompt": "instruction",       # required
        "completion": "response",      # optional
        # "chat": "conversation",      # optional — alternative to prompt+completion
        # "context": ["source", "ref"],# optional — list of context columns
    },
    # --- Optional controls (see references/ for details) ---
    brand_controls={...},
    recipe_specification={...},
    job_specification={...},
    estimate=True,  # dry-run: get cost quote without starting
)

Read the full file on GitHub · 167 lines

Files

What ships with it

9 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. 8d ago First seen · 167 lines · 149 tokens per session scan A d18f8a43e66c

Subscribe to this mod's changes

adaption-ai is a skill published in the GitHub repository svngoku/coding-agents-skills (10 stars, last pushed 25d ago), licensed MIT. It adds 149 tokens to every session and 1,646 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

iflytek-image-understanding

An image-analysis tool that describes pictures and answers questions about what they contain. It uses an AI vision service, which interprets visual content rather than only reading text files.

iflytek/iFly-Skills · 61 tokens

multi-agent-orchestration

Expert guide for designing and orchestrating multi-agent systems, agent swarms, graph-based workflows (LangGraph, CrewAI, AutoGen), shared state memory, and human-in-the-loop guardrails in English and Indonesian.

roedyrustam/vibes-plug · 50 tokens

ai-cost-token-optimizer

Expert guide for LLM API cost optimization, Prompt Caching, model routing (Flash/Pro/Opus), semantic caching, and token budgeting / Panduan ahli optimasi biaya API LLM, Prompt Caching, model routing, dan semantic caching.

roedyrustam/vibes-plug · 57 tokens

ai-media-generation-expert

Expert guide for AI image generation (Flux, DALL-E, Stable Diffusion), video generation (Sora, Runway), voice synthesis (ElevenLabs TTS), and speech recognition (Whisper STT) integration / Panduan ahli integrasi AI generasi gambar, video, suara (TTS), dan pengenalan suara (STT).

roedyrustam/vibes-plug · 78 tokens

vector-db-rag-expert

Expert guide for high-performance Vector Databases, RAG architectures, pgvector HNSW indexing, hybrid search (Dense + BM25), and semantic chunking / Panduan ahli Vector DB, arsitektur RAG, pgvector HNSW, dan hybrid search.

roedyrustam/vibes-plug · 62 tokens

ai-llm-integration-expert

Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP), RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), arsitektur RAG, vector database, dan agen AI.

roedyrustam/vibes-plug · 67 tokens