langchain-data-handling
Implement LangChain data privacy and handling best practices. Use when handling sensitive data, implementing PII protection, or ensuring data compliance in LLM applications. Trigger with phrases like "langchain data privacy", "langchain PII", "langchain GDPR", "langchain data handling", "langchain compliance".
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill langchain-data-handling81
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
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 when to use the skill and provides explicit trigger terms. The main weakness is the lack of specific concrete actions - it describes the domain well but could better enumerate what specific privacy implementations it covers.
Suggestions
Add specific concrete actions like 'mask PII in prompts', 'implement data retention policies', 'configure audit logging', or 'set up anonymization pipelines' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (LangChain data privacy) and mentions some actions like 'implementing PII protection' and 'ensuring data compliance', but lacks specific concrete actions like 'mask PII fields', 'encrypt data at rest', or 'implement audit logging'. | 2 / 3 |
Completeness | Clearly answers both what ('Implement LangChain data privacy and handling best practices') and when ('Use when handling sensitive data, implementing PII protection, or ensuring data compliance'), with explicit trigger guidance provided. | 3 / 3 |
Trigger Term Quality | Explicitly lists natural trigger phrases users would say: 'langchain data privacy', 'langchain PII', 'langchain GDPR', 'langchain data handling', 'langchain compliance'. These cover common variations users might naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | The combination of 'LangChain' + 'data privacy/PII/GDPR/compliance' creates a clear niche that is unlikely to conflict with general LangChain skills or general data privacy skills. The specific trigger terms further reduce conflict risk. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
72%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 excellent code examples covering PII detection, data retention, consent management, and audit logging. The main weaknesses are some verbosity in the code examples and missing validation checkpoints for what are inherently risky data privacy operations. The structure and progressive disclosure are well done.
Suggestions
Add explicit validation checkpoints between steps, such as 'Verify PII masking worked correctly before sending to LLM' with example validation code
Condense the PII_PATTERNS list and dataclass definitions - Claude can infer the pattern structure from fewer examples
Add error handling guidance for when PII detection fails or consent checks fail in production scenarios
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill provides substantial code examples but includes some unnecessary verbosity like the full dataclass definitions and extensive comments. The code is functional but could be tightened - for example, the PII patterns list and some boilerplate could be condensed. | 2 / 3 |
Actionability | Excellent executable code throughout with complete, copy-paste ready implementations. Each step includes working Python code with clear usage examples showing inputs and expected outputs. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but lacks explicit validation checkpoints between steps. For data privacy operations (which are high-stakes), there should be verification steps like 'validate PII was masked before proceeding' or error handling guidance. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections, a concise checklist summary, and appropriate external references to GDPR/CCPA resources. Content is appropriately organized without excessive nesting or monolithic blocks. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
75%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 12 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 12 / 16 Passed | |
- Reviewed
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