Genexus18MCP: Skill for Claude Code

.agents/skills/gxe-artifact-parity/SKILL.md

gxe-artifact-parity is a skill for Claude Code, Codex from lennix1337/Genexus18MCP. It costs 48 tokens per session (417 once invoked), scanned A, original, MIT.

A read-only checker that compares the files produced by two code-generation systems, such as an engine and its software development kit (SDK). It reports matching, missing, and extra files using file sizes and SHA-256 checksums.

In plain words
What is it for?
Use it to investigate differences between generated artifact trees and validate a limited artifact-parity claim from offline directories.
Why use it?
Claims that two systems produce the same output are easy to make from incomplete or unequal inputs. This provides a controlled comparison and indicates whether the selected files match.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is lennix1337/Genexus18MCP's own configuration. It tells Claude Code and Codex how to work on Genexus18MCP itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Genexus18MCP configures →

Reuse

Borrowing it

Nothing to install: this file belongs to lennix1337/Genexus18MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/lennix1337/Genexus18MCP/main/.agents/skills/gxe-artifact-parity/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/lennix1337/Genexus18MCP

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 gxe-artifact-parity

README.md
[![agentmods](https://agentmods.dev/badge/skills/lennix1337/genexus18mcp/gxe-artifact-parity/github.svg)](https://agentmods.dev/skills/lennix1337/genexus18mcp/gxe-artifact-parity)
Your own site
<a href="https://agentmods.dev/skills/lennix1337/genexus18mcp/gxe-artifact-parity"><img src="https://agentmods.dev/badge/skills/lennix1337/genexus18mcp/gxe-artifact-parity/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 gxe-artifact-parity

Your own site · 80×15
<a href="https://agentmods.dev/skills/lennix1337/genexus18mcp/gxe-artifact-parity"><img src="https://agentmods.dev/badge/skills/lennix1337/genexus18mcp/gxe-artifact-parity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 417 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 $0.00048 $0.00417
Opus 5.5 $0.00019 $0.00167
Sonnet 5.5 $0.00010 $0.00083
Haiku 4.5 $0.00005 $0.00042

Measured yesterday against content hash da990ae6f004, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gxe-artifact-parity 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.

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.

.agents/skills/gxe-artifact-parity/SKILL.md · 32 lines

What it actually says

Generated artifact parity

  1. Resolve the repository root (../../.. from this skill directory). Read src/gx-engine/scripts/artifact_diff.py --help with Python from that root. Confirm the comparator and both explicit input directories exist.
  2. Establish provenance: record source snapshot, object closure, SDK/generator, generation options and generation outcome for each tree. Use disposable C:/KBs/*teste* offline outputs or synthetic fixtures. Stale/unequal workloads may be inspected, but cannot certify parity. This skill does not generate output, open a KB or authorize an SDK call.
  3. Run python src/gx-engine/scripts/artifact_diff.py --engine <engine-out> --sdk <sdk-out> --out <report-outside-both-trees> from the repository root. Keep reports outside Git. Compare all extensions for an inventory claim; an explicit extension filter limits the claim to that selection.
  4. Read the JSON inventory and process exit status together. Exit 0 means a nonempty selected file set matched by size and SHA-256; 1 means differences; 2 means invalid input or incomplete comparison. Account for engine-only, SDK-only and excluded files. Directory-only differences, runnable behavior, SDK metadata and generated applications are separate validation gates.
  5. Report provenance, selected denominator, exclusions, differences and a verified/not-verified/blocked conclusion for this exact artifact comparison. Matching artifacts alone never establishes full SDK replacement or speed.

For comparator changes, run python -m unittest discover -s src/gx-engine/scripts -p test_artifact_diff.py; mutations must detect unequal content and writes through an output alias before restoring passing tests.

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 · 32 lines · 48 tokens per session scan A da990ae6f004

Subscribe to this mod's changes

gxe-artifact-parity is a skill published in the GitHub repository lennix1337/Genexus18MCP (38 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 417 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-22.

Related

Other skills, from other repositories

refactor-ops

Safe refactoring patterns - extract, rename, restructure with test-driven methodology and dead code detection. Use for: refactor, refactoring, extract function, extract component, rename, move file, restructure, dead code, unused imports, code smell, duplicate code, long function, god object, feature envy, DRY…

0xDarkMatter/claude-mods · 95 tokens

pair-programming

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with…

frankxai/claude-skills-library · 85 tokens

backpropagation

Trace runtime bugs back to spec gaps — identify missing acceptance criteria, update specs, generate regression tests, and detect patterns.

LucasDuys/forge · 27 tokens

systematic-debugging

4-phase root cause debugging: understand bugs before fixing.

NousResearch/hermes-agent · 16 tokens

langsmith-observability

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

davila7/claude-code-templates · 45 tokens

evidence-audit

Evidence-led audit/fix rounds for any codebase in any language. Finds only defects that a runnable proof reproduces on current code, applies the narrowest in-scope patch, verifies with a second proof, promotes high-risk proofs to permanent regression tests, and reports exact validation results. Use whenever the user…

WrongStack/WrongStack · 152 tokens