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managed-memory

Give an agent durable, cross-session long-term memory using Databricks MANAGED memory (the Unity Catalog memory-store REST APIs) as tools — governed by UC with no infra the customer needs to run. This works for either OpenAI Agents SDK or LangGraph templates. Use when: the agent should remember a user's (or a team/org's shared) preferences/facts/decisions across conversations; keywords 'long-term memory', 'managed memory', 'memory store', 'agentic memory'. This is separate from the self-hosted Lakebase memory solution with skills in (agent-openai-memory / agent-langgraph-memory).

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is managed-memory in databricks/app-templates

SKILL.md
Quality
Evals
Security

Quality

Content

63%Weight 40%Scale 1-5

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

A thorough, highly actionable skill with executable code, exact API calls, clear sequencing, and strong validation/feedback loops for both SDKs. Its main weaknesses are repeated prose (the search-guidance and scope-invariant paragraphs appear multiple times) and a monolithic structure with no reference-file split for its substantial detail.

Suggestions

De-duplicate the search-query guidance: state it once in the search_memory docstring and reference it from the Step 3 blockquote and the Limits section instead of repeating the paragraph verbatim.

Externalize bulk detail into one-level-deep reference files (e.g. references/api.md for the REST endpoint reference, references/langgraph.md for the full LangGraph tool implementations) so SKILL.md reads as an overview with well-signaled pointers.

Consolidate the scope/fail-closed invariant into one canonical statement (Scope strategy or Notes) and have Concepts and Step 4 reference it, to cut repeated restatements.

DimensionReasoningScore

Conciseness

The body is high-signal technical detail (REST shapes, error branching, full tool docstrings) but repeats the search-query guidance paragraph ~verbatim three times (search_memory docstring, the Step 3 blockquote, and Limits & errors) and restates the scope/fail-closed invariant four times across Concepts, Step 4, Scope strategy, and Notes — material that could be tightened by referencing once.

3 / 5

Actionability

Provides complete copy-paste Python for all six tools, exact curl payloads, granular error branching, and wiring snippets for both SDKs; the only gap is the LangGraph section, which is explicitly a 'translation sketch, not standalone copy-paste code', keeping it just short of 5.

4 / 5

Workflow Clarity

A clear Step 1–5 sequence plus Test and Troubleshooting, with checkpoints (confirm the store resolves, verify grants, fail-closed scope guard) and feedback loops (NOT_FOUND→list_memories, ALREADY_EXISTS→update_memory, transient→retry once); validation is mostly explicit but a few checkpoints are implicit rather than called out as verify steps.

4 / 5

Progressive Disclosure

No bundle files exist and all content lives inline in a ~620-line monolithic SKILL.md; the full API reference, complete tool implementations, and scope-strategy variants are content that could be split into one-level-deep reference files, but only sibling-skill pointers (quickstart, run-locally, deploy) are referenced rather than this skill's own detail. Internal section organization is good, so it sits above 2, but the <50-line simple-skill exception does not apply.

3 / 5

Total

14

/

20

Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that clearly states what the skill does, when to use it with natural trigger keywords, and how it differs from related self-hosted memory skills. The only minor gap is that it describes one core capability with mechanism detail rather than a list of multiple discrete actions.

DimensionReasoningScore

Specificity

Names the domain and concrete mechanism ('Unity Catalog memory-store REST APIs as tools', 'governed by UC with no infra the customer needs to run', works for 'OpenAI Agents SDK or LangGraph templates') with several specific attributes, but frames one capability rather than enumerating multiple discrete actions, so it stops short of 5.

4 / 5

Completeness

Explicitly answers both what ('Give an agent durable, cross-session long-term memory using Databricks MANAGED memory ... as tools') and when ('Use when: the agent should remember ... keywords ...'), with concrete trigger phrases.

5 / 5

Trigger Term Quality

Provides an explicit keyword list ('long-term memory', 'managed memory', 'memory store', 'agentic memory') alongside the natural phrase 'remember a user's ... preferences/facts/decisions across conversations', giving comprehensive synonym coverage a user would actually say.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (Databricks managed/UC memory-store) and explicitly disambiguates from sibling skills: 'This is separate from the self-hosted Lakebase memory solution with skills in (agent-openai-memory / agent-langgraph-memory).'

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (624 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

databricks/app-templates

Path

agent-langgraph-advanced/.claude/skills/managed-memory/SKILL.md

Reviewed

Table of Contents

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