langchain-common-errors
Diagnose and fix common LangChain errors and exceptions. Use when encountering LangChain import errors, auth failures, output parsing issues, agent loops, or version conflicts. Trigger: "langchain error", "langchain exception", "debug langchain", "langchain not working", "langchain troubleshoot".
90
89%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Risky
Do not use without reviewing
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly defines its scope (LangChain error diagnosis and fixing), enumerates specific error categories, and provides both a 'Use when' clause and explicit trigger terms. It uses proper third-person voice and is concise without being vague. The description would perform well in a large skill library for accurate selection.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and error types: import errors, auth failures, output parsing issues, agent loops, and version conflicts. 'Diagnose and fix' are clear action verbs applied to a well-defined domain. | 3 / 3 |
Completeness | Clearly answers both 'what' (diagnose and fix common LangChain errors including import errors, auth failures, output parsing issues, agent loops, version conflicts) and 'when' (explicit 'Use when' clause plus a dedicated 'Trigger' section with natural language phrases). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would actually say: 'langchain error', 'langchain exception', 'debug langchain', 'langchain not working', 'langchain troubleshoot'. Also includes domain-specific terms like 'import errors', 'auth failures', 'output parsing issues', 'agent loops', 'version conflicts'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — narrowly scoped to LangChain-specific errors and troubleshooting. The combination of 'LangChain' with specific error categories like 'agent loops' and 'output parsing issues' makes it very unlikely to conflict with general Python debugging or other framework skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
79%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong troubleshooting reference with excellent actionability — exact error messages paired with copy-paste fixes covering the most common LangChain failure modes. The content is concise and respects Claude's intelligence. The main weaknesses are the lack of a structured diagnostic workflow (check A → then B → then C) and the monolithic structure that could benefit from splitting detailed sections into separate files.
Suggestions
Add a brief diagnostic workflow at the top: a numbered sequence like '1. Check versions → 2. Verify env vars → 3. Test minimal connection → 4. Enable debug logging' to guide systematic troubleshooting.
Consider splitting the diagnostic script and detailed per-category errors into separate referenced files to improve progressive disclosure and reduce the main file length.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient throughout. It leads with exact error messages and provides direct fixes without explaining what LangChain is or how libraries work. Every section earns its place with specific error-to-fix mappings. | 3 / 3 |
Actionability | Nearly every error includes copy-paste ready code with exact import paths, commands, and constructor options. The diagnostic script is fully executable, and fixes are concrete with both wrong and right patterns shown. | 3 / 3 |
Workflow Clarity | The skill is structured as a reference/lookup rather than a multi-step workflow, which is appropriate for a troubleshooting guide. However, the debugging section lacks a clear diagnostic sequence (e.g., 'check versions first, then env vars, then try minimal reproduction') and the diagnostic script doesn't include a feedback loop for fixing discovered issues. | 2 / 3 |
Progressive Disclosure | The content is well-organized with clear section headers making it easy to scan, but at ~200 lines it's a long single file. Some sections (like the full diagnostic script or the detailed agent errors) could be split into referenced files. The 'langchain-debug-bundle' reference at the end is vague with no actual bundle provided. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
3a2d27d
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.