agent-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.
66
79%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.claude/skills/agent-langgraph-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') without listing specific concrete actions like storing/retrieving memories, managing conversation buffers, or configuring persistence backends. The description also uses second person ('your agent') which is a minor style issue but not penalized as heavily as first person.
Suggestions
Add more specific concrete actions to the 'what' portion, e.g., 'Store and retrieve conversation history, manage user preference persistence, configure checkpointing for long-running agent sessions'
Narrow the trigger term 'state' to reduce conflict risk with unrelated state management skills, e.g., 'agent state' or 'conversation state'
| 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., specific APIs, data structures, or operations). It's more about categories than concrete actions. | 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 | The terms 'state' and 'remember' are somewhat generic and could overlap with other skills (e.g., state management in UI frameworks, or general knowledge recall). However, the combination with 'memory', 'conversation history', and 'checkpointing' narrows the scope reasonably well. | 2 / 3 |
Total | 10 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured, highly actionable skill that provides clear step-by-step guidance for adding memory capabilities to a LangGraph agent. Its main strength is the executable code examples, comprehensive troubleshooting table, and clear workflow with a setup checklist. Its weakness is moderate verbosity — the document is long with some redundancy between sections, and the inline content could benefit from splitting detailed patterns into a separate reference file.
Suggestions
Consider moving the 'Key Principles' section (patterns 1-5) into a separate reference file like PATTERNS.md to reduce the main skill's length while preserving the actionable setup steps.
Remove the 'Pre-Built Memory Templates' section near the bottom since it duplicates the note at the very top of the document.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some redundancy (e.g., the setup checklist largely repeats information already covered in the step-by-step sections, and the 'Key Principles' section explains patterns at length that could be more tightly presented). The troubleshooting table and testing examples are useful but add significant length. Some sections like 'Pre-Built Memory Templates' repeat the same link mentioned at the top. | 2 / 3 |
Actionability | The skill provides fully executable code examples throughout — from pyproject.toml configuration to Python integration code, curl commands for testing, and even a one-liner for table initialization. The configuration file examples are copy-paste ready with clear placeholder markers. | 3 / 3 |
Workflow Clarity | The multi-step process is clearly sequenced with a 'Quick Setup Summary' table showing all 4 files to modify, followed by numbered steps. The first-time setup checklist provides explicit validation checkpoints, and the troubleshooting table covers common failure modes with solutions. The table initialization step is clearly marked as critical and first-time-only. | 3 / 3 |
Progressive Disclosure | The skill references external skills (lakebase-setup, deploy, run-locally) and external templates appropriately, but the SKILL.md itself is quite long (~250+ lines) with substantial inline content. The reference to 'examples/memory_tools.py' is good progressive disclosure, but no bundle files were provided to verify it exists. Some content (like the full Key Principles section) could potentially be split into a separate reference file. | 2 / 3 |
Total | 10 / 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
1c88215
- Reviewed
Table of Contents
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