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AiDimag

AI Dimag

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Capture codebase knowledge the user states in chat—decisions, conventions, guardrails—into durable, reviewable memory.

Instructions

Capture a durable fact the USER just stated in chat. Call this IMMEDIATELY when the user shares codebase knowledge — don't wait for session end. ALWAYS trigger on: 'we use X because Y' (DECISION), 'never do X' (GUARDRAIL), 'we always X' (CONVENTION), 'we tried X, it failed' (FAILED_APPROACH), 'the architecture is...' (ARCHITECTURE). Skip task-specific requests like 'fix this bug'. User-stated facts are queued for review with high trust (HUMAN_ATTESTED evidence).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesBest-fit memory kind (e.g. user says 'never do X' → GUARDRAIL, 'we tried X, failed' → FAILED_APPROACH, 'we always X' → CONVENTION)
pathsNoRepo-relative paths the fact applies to (omit for repo-wide)
quoteNoThe user's own words, verbatim (preserves nuance for the reviewer)
symbolsNoSymbols (functions/classes) it applies to
agent_idNoYour agent identifier, e.g. 'claude-code'
statementYesThe fact, rephrased as a falsifiable claim about the codebase (e.g. 'Payments retries are handled in src/queue; handlers must be idempotent')
guardrail_levelNoFor kind=GUARDRAIL: never | always | ask-first
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait by noting that facts are 'queued for review with high trust (HUMAN_ATTESTED evidence)', implying a deferred write rather than an immediate direct save. It does not mention permissions or reversibility, but for a capture tool this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately long but every sentence delivers actionable information, from the lead action to triggers, exclusions, and post-capture behavior. The ALL-CAPS emphasis is slightly noisy but the structure is logical and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter tool with no output schema and no annotations, the description covers the full decision space: what to capture, when to invoke, which triggers map to which kinds, what to skip, and what happens after capture. No significant gaps remain for an agent to use this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3. The description adds value by mapping trigger phrases to specific kind enum values and clarifying the difference between statement and quote, going beyond what the schema descriptions provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Capture') and resource ('a durable fact the USER just stated in chat'), clearly distinguishing itself from siblings by emphasizing immediate capture of chat-stated knowledge and listing concrete trigger patterns. It is unambiguous what the tool does and when it applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit when-to-use ('IMMEDIATELY when the user shares codebase knowledge') and when-not-to-use ('Skip task-specific requests'), including concrete trigger examples. However, it does not name alternative sibling tools, so it misses the full 'alternatives' component of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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