langchain-debug-bundle
Collect LangChain debug evidence for troubleshooting and bug reports. Use when preparing GitHub issues, collecting LangSmith traces, or gathering diagnostic info for complex LangChain failures. Trigger: "langchain debug bundle", "langchain diagnostics", "langchain support info", "collect langchain logs", "langchain trace".
64
77%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/langchain-pack/skills/langchain-debug-bundle/SKILL.mdQuality
Discovery
89%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 well-structured skill description with strong completeness and distinctiveness. It clearly defines both what the skill does and when to use it, with explicit trigger terms. The main weakness is that the specific capabilities could be more granular—listing concrete actions like exporting traces, capturing dependency versions, or serializing configurations would strengthen specificity.
Suggestions
Add more concrete actions to improve specificity, e.g., 'Captures stack traces, exports LangSmith run URLs, dumps dependency versions, and serializes chain configurations for LangChain debug bundles.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (LangChain debug evidence) and some actions (collecting traces, gathering diagnostic info, preparing GitHub issues), but doesn't list multiple concrete specific actions like 'capture stack traces, export LangSmith run URLs, dump dependency versions, serialize chain configs'. | 2 / 3 |
Completeness | Clearly answers both 'what' (collect LangChain debug evidence for troubleshooting and bug reports) and 'when' (preparing GitHub issues, collecting LangSmith traces, gathering diagnostic info for complex LangChain failures) with explicit trigger terms listed. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'langchain debug bundle', 'langchain diagnostics', 'langchain support info', 'collect langchain logs', 'langchain trace', plus contextual triggers like 'GitHub issues', 'LangSmith traces', and 'diagnostic info'. These are terms users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Very clear niche targeting specifically LangChain debug evidence collection. The combination of 'LangChain' + 'debug bundle/diagnostics/traces' is highly distinctive and unlikely to conflict with general debugging skills or other LangChain skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with executable code examples covering the full diagnostic workflow for LangChain debugging. Its main weaknesses are the length of inline code that could be split into bundle files for better progressive disclosure, and the lack of explicit validation checkpoints between steps. The error handling table and bug report checklist are valuable additions.
Suggestions
Add validation checkpoints between steps, e.g., verify package.json exists before Step 1, confirm the debug bundle file was written successfully in Step 3, and add a feedback loop for handling trace collection failures.
Move the full DebugTraceHandler class and the minimal reproduction template into separate bundle files (e.g., debug_trace_handler.ts, minimal_repro.ts) and reference them from SKILL.md to reduce inline bulk.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably focused but includes some unnecessary verbosity. The full callback handler implementation with all method signatures is quite long and could be trimmed. The 'Current State' section with shell commands is a nice touch but the 'Overview' sentence restates what the title already conveys. Some code comments are unnecessary for Claude. | 2 / 3 |
Actionability | The skill provides fully executable TypeScript and Python code throughout. The callback handler, debug bundle generator, minimal reproduction template, and LangSmith trace export are all copy-paste ready with concrete imports, types, and usage examples. | 3 / 3 |
Workflow Clarity | The steps are clearly numbered and sequenced (1-5), and the bug report section ties them together. However, there are no explicit validation checkpoints between steps — for instance, no verification that the environment snapshot captured valid data, no check that the debug bundle was written correctly, and no feedback loop for handling failures in the collection process itself. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and a logical flow, but it's quite long (~180 lines of code) with no offloading to separate files. The full callback handler implementation and the reproduction template could be referenced as separate files. The reference to 'langchain-common-errors' at the end is a good cross-reference, but the inline content is heavy for a SKILL.md overview. | 2 / 3 |
Total | 9 / 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 | |
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