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 svngoku/coding-agents-skills --skill adaption-aigit clone --depth 1 https://github.com/svngoku/coding-agents-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/svngoku/coding-agents-skills/adaption-ai)<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>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 | $0.00149 | $0.01646 |
| Opus 5 | $0.00075 | $0.00823 |
| Sonnet 5 | $0.00030 | $0.00329 |
| Haiku 4.5 | $0.00015 | $0.00165 |
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.
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` | 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
- Ingest — Upload local file, import from HuggingFace, or import from Kaggle
- Adapt — Start an augmentation run with column mapping + optional controls
- Wait — Poll for completion with exponential backoff
- Evaluate — Fetch quality metrics (score_before/after, improvement %)
- 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
)
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.
- eval.yaml 987 B
- graders/check.py 1.8 KB runs code
- instructions/pipeline-task.md 888 B
- references/api-reference.md 4.8 KB
- references/guides.md 3.5 KB
- rubrics/quality.md 590 B
- scripts/async_pipelines.py 2.1 KB runs code
- scripts/e2e_pipeline.py 5.2 KB runs code
- solutions/reference-pipeline.py 1.4 KB runs code
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 · 167 lines · 149 tokens per session scan A d18f8a43e66c
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.
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