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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

85%

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

70%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 strong workflow sequencing and validation, but it is verbose due to repeated search-query guidance across three sections and monolithic — everything is inlined in SKILL.md with no progressive disclosure to reference files despite its length.

Suggestions

Consolidate the search-query phrasing guidance into one canonical location (e.g. the search_memory docstring) and reference it from the Step 3 blockquote and Limits section instead of restating it verbatim.

Move the full shared-core/tool implementations or the troubleshooting table into a references/ file (e.g. MEMORY_TOOLS.md) and keep SKILL.md as an overview that links one level deep, improving progressive disclosure for this long skill.

Make the LangGraph section copy-paste-ready (complete six-function implementation with verbatim docstrings) rather than an explicit sketch, or move the complete version to a referenced file.

DimensionReasoningScore

Conciseness

Largely efficient and assumes competence (no basic-concept padding), but the search-query phrasing guidance is repeated near-verbatim in three places (the search_memory docstring, the blockquote after Step 3, and the Limits section) and the managed-vs-self-hosted framing restates the description, so it could be tightened; not a 4 because the repetition is material.

3 / 5

Actionability

Provides copy-paste-ready Python for the shared core and all six OpenAI tools plus executable curl/yaml setup snippets, but the LangGraph section is explicitly 'a translation sketch, not standalone copy-paste code', a minor gap that keeps it just below the fully-executable anchor.

4 / 5

Workflow Clarity

Steps 1–5 are clearly sequenced with explicit validation checkpoints (confirm the store resolves, verify grants, fail-closed scope guard before any write) and feedback loops (search→list/get fallback, retry-once vs stop error branching, troubleshooting table), satisfying the destructive/batch validation requirement rather than triggering the cap.

5 / 5

Progressive Disclosure

Well-organized into clear sections, but the skill is ~600 lines with no bundle files and no offloading of the full tool implementations, scope-strategy variants, or troubleshooting into separate reference files; content that could live one level deep is entirely inlined, matching the 'content that should be separate is inline' anchor.

3 / 5

Total

15

/

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 answers both what and when with natural trigger keywords and explicit disambiguation from sibling memory skills. The only minor gap is that it describes one capability with several characteristics rather than enumerating many discrete actions.

DimensionReasoningScore

Specificity

Names the concrete capability ('durable, cross-session long-term memory using Databricks MANAGED memory (the Unity Catalog memory-store REST APIs) as tools') plus specifics like UC governance and zero infra, but enumerates characteristics of one capability rather than a list of multiple discrete actions, so it sits just below the comprehensive anchor.

4 / 5

Completeness

Clearly answers 'what' (give an agent durable cross-session memory via UC memory-store REST APIs as tools) and 'when' with an explicit 'Use when:' clause and concrete trigger phrases, matching the anchor that requires both explicitly.

5 / 5

Trigger Term Quality

Explicitly enumerates natural synonyms users would say — "keywords 'long-term memory', 'managed memory', 'memory store', 'agentic memory'" — plus 'preferences/facts/decisions across conversations', giving comprehensive synonym coverage matching the top anchor.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche and explicitly disambiguates from the related 'self-hosted Lakebase memory solution with skills in (agent-openai-memory / agent-langgraph-memory)', minimizing wrong-skill triggers.

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/.claude/skills/managed-memory/SKILL.md

Reviewed

Table of Contents

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