engineering-ai-data-remediation-engineer

engineering-ai-data-remediation-engineer is a skill for Claude Code, Codex from clowlove/Hermes-House. It costs 108 tokens per session (2,849 once invoked), scanned A, original, no licence file.

A data-repair skill for detecting, grouping, and fixing large-scale data problems in a local, isolated environment. It uses a small local language model and semantic clustering, which groups records by meaning, to generate repeatable repair logic while preserving data.

In plain words
What is it for?
Use it to intercept malformed data, classify anomalies, generate deterministic repairs with Ollama, and repair data in pipelines that need to keep running.
Why use it?
It helps recover from bad data without stopping a pipeline, the automated process that moves and transforms data. Local isolation is intended for cases where data must not leave the environment.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to intercept malformed data, classify anomalies, generate deterministic repairs with Ollama, and repair data in pipelines that need to keep running.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/clowlove/hermes-house/engineering-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 clowlove/Hermes-House --skill engineering-ai-data-remediation-engineer
Clone the repo
git clone --depth 1 https://github.com/clowlove/Hermes-House

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 engineering-ai-data-remediation-engineer

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/clowlove/hermes-house/engineering-ai-data-remediation-engineer"><img src="https://agentmods.dev/badge/skills/clowlove/hermes-house/engineering-ai-data-remediation-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,849 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 $0.00108 $0.02849
Opus 5 $0.00054 $0.01425
Sonnet 5 $0.00022 $0.00570
Haiku 4.5 $0.00011 $0.00285

Measured 11d ago against content hash 6777a612e269, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

engineering-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 11d 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.

skills/engineering-ai-data-remediation-engineer/SKILL.md · 206 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Files

What ships with it

1 file 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. 11d ago First seen · 206 lines · 108 tokens per session scan A 6777a612e269

Subscribe to this mod's changes

engineering-ai-data-remediation-engineer is a skill published in the GitHub repository clowlove/Hermes-House (15 stars, last pushed today), with no licence file. It adds 108 tokens to every session and 2,849 once invoked, about $0.0005 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.

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