Name: agent-openai-memory
Rating: 62.4 (1 reviews)
Author: databricks

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.

Quality

73%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./agent-openai-advanced/.claude/skills/agent-memory/SKILL.md

Quality

Content

64%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill provides solid, actionable guidance with executable code and configuration examples for adding session-based memory to an OpenAI Agents SDK application on Databricks. Its main weaknesses are moderate verbosity in explaining concepts Claude already knows (like how sessions work conceptually) and a lack of explicit validation checkpoints in the setup workflow. The troubleshooting table is a nice addition but doesn't substitute for inline verification steps.

Suggestions

Remove or significantly condense the 'How Sessions Work' and 'Key Concepts' sections — Claude understands these concepts and the code examples already demonstrate them.

Add explicit validation checkpoints in the workflow, e.g., 'Verify Lakebase instance is running: `databricks lakebase instances get <name>` before proceeding to configuration'.

Move the 'lakebase-setup' skill reference to the Prerequisites section where it's most actionable, rather than only in 'Next Steps'.

Dimension	Reasoning	Score
Conciseness	The skill includes some unnecessary explanatory content (e.g., 'How Sessions Work' section explaining the general concept of sessions, the 'Key Concepts' table defining obvious terms). However, the configuration examples and code snippets are reasonably focused. Could be tightened by removing conceptual explanations Claude already understands.	2 / 3
Actionability	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 validation checkpoints. There's no 'verify your Lakebase instance is running before proceeding' step or validation between configuration and testing. The troubleshooting table partially compensates but doesn't constitute a proper feedback loop in the workflow.	2 / 3
Progressive Disclosure	References to other skills (lakebase-setup, run-locally, deploy) are present in 'Next Steps' but not clearly signaled throughout the document where they'd be most relevant. The document is somewhat long with inline configuration details that could potentially be split out, though no bundle files exist to support this.	2 / 3
	Total	9 / 12 Passed

Description

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 reasonably well-structured description with a clear 'Use when' clause and good trigger term coverage. Its main weakness is that the 'what' portion is somewhat vague—'add memory capabilities' doesn't specify concrete actions like storing/retrieving memories, configuring storage backends, or managing conversation windows. The description also uses second person ('your agent') which is a minor style issue but not penalized as heavily as first person.

Suggestions

Make the 'what' portion more specific by listing concrete actions, e.g., 'Store and retrieve conversation history, persist user preferences across sessions, implement checkpointing for long-running agent tasks.'

Improve distinctiveness by specifying the technology or framework context (e.g., 'LangChain memory', 'agent framework') to reduce overlap with general database or caching skills.

Dimension	Reasoning	Score
Specificity	Names the domain ('memory capabilities') and some actions like 'persist conversations', 'adding checkpointing', 'long-term storage', but doesn't list concrete specific actions (e.g., what APIs, what data structures, what storage backends). It's more of a feature category than a list of concrete operations.	2 / 3
Completeness	Clearly answers both 'what' (add memory capabilities to your agent) and 'when' with an explicit 'Use when:' clause listing three specific trigger scenarios. The when clause is well-structured with numbered conditions.	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 might naturally use.	3 / 3
Distinctiveness Conflict Risk	'Memory capabilities' and 'state' could overlap with other skills related to caching, database management, or session handling. While the trigger terms like 'conversation history' and 'checkpointing' help narrow the scope, 'state' and 'remember' are broad enough to potentially conflict with other skills.	2 / 3
	Total	10 / 12 Passed

Validation

100%

Warnings & errors only

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: 2302cd3

Reviewed: 5 days ago

Table of Contents

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