ai-data-remediation-engineer

ai-data-remediation-engineer is a skill for Claude Code from Prorise-cool/prorise-claude-skills. It costs 88 tokens per session (2,086 once invoked), scanned A, original, no licence file.

A specialist guide for finding and correcting unusual or damaged data in automated data pipelines. It is described as using local small language models and grouping similar problems to create repeatable fixes.

In plain words
What is it for?
Use it when designing or reviewing the part of a data pipeline that detects, labels, and fixes data anomalies. It is intended for environments where models run locally without network access.
Why use it?
It helps address bad data as it moves through a pipeline, so problems can be detected and corrected before they spread. The description also focuses on preventing data loss.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it when designing or reviewing the part of a data pipeline that detects, labels, and fixes data anomalies. It is intended for environments where models run locally without network access.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/prorise-cool/prorise-claude-skills/ai-data-remediation-engineer
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 Prorise-cool/prorise-claude-skills --skill ai-data-remediation-engineer
Clone the repo
git clone --depth 1 https://github.com/Prorise-cool/prorise-claude-skills

Made for: Claude Code.

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 ai-data-remediation-engineer

README.md
[![agentmods](https://agentmods.dev/badge/skills/prorise-cool/prorise-claude-skills/ai-data-remediation-engineer/github.svg)](https://agentmods.dev/skills/prorise-cool/prorise-claude-skills/ai-data-remediation-engineer)
Your own site
<a href="https://agentmods.dev/skills/prorise-cool/prorise-claude-skills/ai-data-remediation-engineer"><img src="https://agentmods.dev/badge/skills/prorise-cool/prorise-claude-skills/ai-data-remediation-engineer/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 ai-data-remediation-engineer

Your own site · 80×15
<a href="https://agentmods.dev/skills/prorise-cool/prorise-claude-skills/ai-data-remediation-engineer"><img src="https://agentmods.dev/badge/skills/prorise-cool/prorise-claude-skills/ai-data-remediation-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,086 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 unknown 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 $0.00088 $0.02086
Opus 5 $0.00044 $0.01043
Sonnet 5 $0.00018 $0.00417
Haiku 4.5 $0.00009 $0.00209

Measured 12d ago against content hash 38df0e5bc9ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ai-data-remediation-engineer 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 12d 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.

.claude/skills/ai-specialist/references/domains/ai-data-remediation-engineer/SKILL.md · 187 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

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. 12d ago First seen · 187 lines · 88 tokens per session scan A 38df0e5bc9ab

Subscribe to this mod's changes

ai-data-remediation-engineer is a skill published in the GitHub repository Prorise-cool/prorise-claude-skills (24 stars, last pushed 6mo ago), with no licence file. It adds 88 tokens to every session and 2,086 once invoked, about $0.0004 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 2026-08-30.

Related

Other skills, from other repositories

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

agent-platform-model-registry

Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

google/skills · 60 tokens

foundry-config-setup

Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.

microsoft/agent-framework · 65 tokens

google-cloud-solution-agentic-analytics-spark-knowledge-catalog

Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…

google/skills · 138 tokens

training-check

Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.

wanshuiyin/Auto-claude-code-research-in-sleep · 35 tokens

nemo-automodel-launcher-config

Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.

NVIDIA/skills · 30 tokens