evo-defect-pipeline

evo-defect-pipeline is a skill for Claude Code, Codex from OpenLAIR/OpenSkill. It costs 39 tokens per session (236 once invoked), scanned A, original, Apache-2.0.

An end-to-end tool for turning test-centre defect logs into structured JSON records. It normalises defect descriptions and matches them against product codebooks, which are reference files mapping product labels to codes.

In plain words
What is it for?
Use it to read log CSVs and codebook files, match each record to a code, and write the processed results to solution.json.
Why use it?
It reduces manual cleanup and matching when the same defect or product may be written in different ways across log files.

Skill for Claude CodeCodex

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

Good fit Use it to read log CSVs and codebook files, match each record to a code, and write the processed results to solution.json.

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Install with agentmods
npx agentmods add skills/openlair/openskill/evo-defect-pipeline
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 OpenLAIR/OpenSkill --skill evo-defect-pipeline
Clone the repo
git clone --depth 1 https://github.com/OpenLAIR/OpenSkill

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 evo-defect-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/openlair/openskill/evo-defect-pipeline/github.svg)](https://agentmods.dev/skills/openlair/openskill/evo-defect-pipeline)
Your own site
<a href="https://agentmods.dev/skills/openlair/openskill/evo-defect-pipeline"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-defect-pipeline/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 evo-defect-pipeline

Your own site · 80×15
<a href="https://agentmods.dev/skills/openlair/openskill/evo-defect-pipeline"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-defect-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 236 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 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 $0.00039 $0.00236
Opus 5 $0.00019 $0.00118
Sonnet 5 $0.00008 $0.00047
Haiku 4.5 $0.00004 $0.00024

Measured yesterday against content hash 6e20f328bb00, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

evo-defect-pipeline 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 yesterday.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/utils.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.

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.

tasks-evolved/manufacturing-codebook-normalization/environment/skills/evo-defect-pipeline/SKILL.md · 27 lines

What it actually says

evo-defect-pipeline

Orchestration pipeline for defect text normalization.

Key Functions

  • safe_read_csv(file_path) - Read CSV with encoding fallbacks (utf-8, gbk, replace)
  • discover_codebooks(directory) - Find codebook CSVs, map product_id to filepath
  • build_codebook_mapping(codebook_df) - Build preprocessed label -> code dict
  • process_record(row, codebook_dict, standard_texts, threshold) - Process single record
  • export_results_to_json(records, output_path) - Write JSON with numpy-safe encoding
  • run_pipeline(data_dir, output_path, threshold) - Full end-to-end pipeline

Usage

import sys
sys.path.insert(0, '/app/environment/skills/evo-defect-pipeline/scripts')
from utils import run_pipeline
run_pipeline('/app/data', '/app/output/solution.json')
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. yesterday First seen · 27 lines · 39 tokens per session scan A 6e20f328bb00

Subscribe to this mod's changes

evo-defect-pipeline is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 39 tokens to every session and 236 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 2026-09-11.