agent-openai-memory
Add memory capabilities to your agent. Use when: (1) User asks about 'memory', 'state', 'remember', 'conversation history', (2) Want to persist conversations or user preferences, (3) Adding checkpointing or long-term storage.
78
73%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./agent-openai-advanced/.claude/skills/agent-memory/SKILL.mdQuality
Discovery
82%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 solid description with explicit trigger guidance and good keyword coverage. Its main weakness is that the 'what' portion is somewhat high-level—'add memory capabilities' is broad and could benefit from listing more concrete actions like specific storage/retrieval operations. The description also uses second person ('your agent') which is not ideal but less penalizable than first person.
Suggestions
Add more specific concrete actions to the 'what' portion, e.g., 'Store and retrieve conversation history, save user preferences across sessions, implement checkpointing for long-running agent workflows.'
Rephrase to third person voice: 'Adds memory capabilities to agents' instead of 'Add memory capabilities to your agent.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain ('memory capabilities') and some actions like 'persist conversations', 'user preferences', 'checkpointing', 'long-term storage', but doesn't list multiple concrete specific actions (e.g., no mention of specific APIs, storage mechanisms, or retrieval methods). | 2 / 3 |
Completeness | Clearly answers both 'what' (add memory capabilities to your agent) and 'when' with an explicit 'Use when:' clause listing three distinct trigger scenarios. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'memory', 'state', 'remember', 'conversation history', 'persist conversations', 'user preferences', 'checkpointing', 'long-term storage'. These cover a good range of terms a user would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | 'State' and 'remember' are somewhat generic terms that could overlap with other skills (e.g., state management in UI frameworks, or general knowledge retrieval). However, the combination with 'memory', 'conversation history', and 'checkpointing' narrows the scope reasonably well. | 2 / 3 |
Total | 10 / 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.
The skill provides strong actionable content with executable code and testing commands, and includes a useful troubleshooting table. However, it is somewhat verbose with explanatory sections that Claude doesn't need (session lifecycle, key concepts table), and the workflow lacks explicit sequencing and validation checkpoints for the multi-step setup process. The content would benefit from tightening and clearer step-by-step ordering.
Suggestions
Add explicit numbered setup workflow with validation checkpoints (e.g., 'Step 1: Create Lakebase instance → verify with `databricks lakebase instances get <name>` → Step 2: Configure databricks.yml → Step 3: Set .env → Step 4: Test with curl')
Remove or significantly condense the 'How Sessions Work' and 'Key Concepts' sections—Claude understands these patterns and the code examples are self-explanatory
Consider splitting configuration file details (databricks.yml, .env) into a separate reference file to keep the main skill focused on the essential workflow
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary explanatory content (e.g., 'How Sessions Work' section explaining the session lifecycle is somewhat redundant for Claude). The tables and configuration sections are useful but the overall document could be tightened—the 'Key Concepts' table explains fairly obvious mappings, and the 'How This Template Uses Sessions' section narrates code that is self-explanatory. | 2 / 3 |
Actionability | The skill provides fully executable code snippets for session creation, session ID extraction, configuration files (databricks.yml, .env), and curl commands for testing. All examples are copy-paste ready with clear placeholders for user-specific values. | 3 / 3 |
Workflow Clarity | The prerequisites and configuration steps are listed but lack explicit sequencing and validation checkpoints. There's no clear 'do step 1, verify, then step 2' flow—the user must piece together the order from separate sections (Prerequisites → Configuration → Testing). The troubleshooting table partially compensates but there are no inline validation steps. | 2 / 3 |
Progressive Disclosure | The document references other skills (lakebase-setup, run-locally, deploy) which is good, but the main content is quite long and monolithic. Configuration details for databricks.yml, .env, and the full code walkthrough could be split into separate reference files. The 'Next Steps' section provides navigation but the body itself could benefit from better separation of overview vs. detail. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- databricks/app-templates
- Commit
dfeb4ac
- Reviewed
Table of Contents
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