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aim-query

aim-query

github.com/djalmajrAgent workflows470 installsUpdated 5w ago
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About aim-query

aim-query is a skill for retrieving explicit memories from an ai-memory instance. It guides agents in selecting a configured endpoint and scope, choosing a query method, and reporting relevant results with paths.

Key features

  1. 01Selects an ai-memory endpoint from configuration
  2. 02Supports project, multi-scope, and global recall
  3. 03Chooses among query, recent, explore, briefing, and status tools
  4. 04Reports matching paths with relevance notes

Use cases

  1. 01Checking prior decisions before proposing a design
  2. 02Reviewing recent activity in a project
  3. 03Retrieving shared conventions across projects

MCP support

It uses tools exposed by the configured ai-memory MCP server, including memory_query, memory_recent, memory_explore, memory_briefing, and memory_status.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/djalmajr/skills --skill aim-query

After installing, type/aim-queryin Claude Code to confirm it loaded.

Details

From the project README

aim-query

Explicit recall from an ai-memory instance (complements the automatic session-start handoff).

Steps

  1. Pick the instance. Read the configured ai-memory MCP server(s) from the repo's .mcp.json / opencode.json / .codex/config.toml. If more than one is configured, ask the user which endpoint to query. (Wired by aim-init.)
  2. Pick the scope. Default to the repo's .ai-memory.toml workspace/project. The user may widen it: a different project, a partial scope, or multi-scopes for cross-project recall. scopes carries { workspace, project } per entry, so recall can span other workspaces too (e.g. a shared rules project); global: true searches every project at once.
    • Shared / global rules live in a dedicated scope (the reserved default/_global scope, under _rules/; ≥1.9 the server unions it into default-scoped queries as global_scope_hits), separate from the repo's own project. ai-memory's auto-recall is per-(workspace, project) and will not surface them — pull them explicitly: memory_read_page with that workspace/project/path, or memory_query with scopes:[{ workspace, project }]. Do this when the task needs the agent's standing conventions (code-style, testing gates, workflow), not just repo-local knowledge.
  3. Choose the tool for the question:
    • memory_query — targeted semantic recall ("have we decided X", before proposing design).
    • memory_recent — "what's been going on lately" (N most-recent pages).
    • memory_explore — open-ended "catch me up" (prose digest; takes an optional focus).
    • memory_briefing / memory_status — structured snapshot / lifetime counts.
  4. Report the hits with their path + a one-line relevance note; offer to open a page (read its markdown via the MCP or /api/v1/.../pages/<path>).

Keep queries specific. If recall returns nothing, widen the scope or rephrase before concluding the knowledge isn't there.

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