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 ArabelaTso/Skills-4-SE --skill error-explanation-generatorgit clone --depth 1 https://github.com/ArabelaTso/Skills-4-SEWrote 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/arabelatso/skills-4-se/error-explanation-generator)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/error-explanation-generator"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/error-explanation-generator/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.
<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/error-explanation-generator"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/error-explanation-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 401 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 420 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00097 | $0.03546 |
| Opus 5 | $0.00048 | $0.01773 |
| Sonnet 5 | $0.00019 | $0.00709 |
| Haiku 4.5 | $0.00010 | $0.00355 |
Grade A, and why
error-explanation-generator 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.
How it starts
The opening of the file, as written. The whole thing — 606 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Error Explanation Generator
Analyze test failures and provide clear explanations with actionable fixes.
Core Capabilities
This skill helps debug failed tests by:
- Parsing error messages - Extract key information from test output
- Identifying root causes - Determine why tests fail
- Explaining clearly - Translate technical errors into understandable language
- Providing fixes - Suggest concrete, actionable solutions
- Recognizing patterns - Detect common error categories across frameworks
Error Analysis Workflow
Step 1: Gather Error Context
Collect all relevant information:
Essential Information:
- Complete error message and stack trace
- Test framework being used (pytest, jest, junit, etc.)
- Test code that failed
- Code being tested (if accessible)
- Programming language
Additional Context (if available):
- Recent code changes
- Environment details (OS, language version, dependencies)
- Test configuration files
How to gather:
# Python pytest
pytest -v --tb=long
# JavaScript/TypeScript jest
npm test -- --verbose
# Java junit
mvn test -X
# Go tests
go test -v
Step 2: Parse and Categorize the Error
Identify the error category using references/error_patterns.md:
Common Categories:
- Assertion Failures - Expected vs actual value mismatch
- Exceptions/Errors - Runtime errors during test execution
- Timeout Errors - Tests taking too long
- Setup/Teardown Failures - Fixture or initialization issues
- Import/Dependency Errors - Missing modules or broken imports
- Type Errors - Type mismatches in typed languages
- Mock/Stub Issues - Problems with test doubles
- Configuration Errors - Test framework or build config issues
- Compilation Errors - Syntax or build failures
- Flaky Test Issues - Intermittent failures
Step 3: Extract Key Information
Pull out critical details:
From Error Message:
- Error type (AssertionError, TypeError, NullPointerException, etc.)
- Expected vs actual values
- Error description
- Line numbers where error occurred
What ships with it
2 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.
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.
- 11d ago First seen · 606 lines · 97 tokens per session scan A fa978da17f82
error-explanation-generator is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (252 stars, last pushed 21d ago), licensed Apache-2.0. It adds 97 tokens to every session and 3,546 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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