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djalmajr/skills

aim-query

Manually recall durable knowledge from an ai-memory instance via the MCP (semantic/hybrid search, recent pages, prose catch-up).

First seen May 28, 2026

Installation

$ npx skills add djalmajr/skills --skill aim-query
View on skills.sh ↗ GitHub repository ↗

Summary

  • Manually recall durable knowledge from an ai-memory instance via the MCP (semantic/hybrid search, recent pages, prose catch-up).
  • Use when the user asks to search the memory, 'have we done/decided X', 'what's the state of Y', or 'catch me up' — and you want an explicit recall rather than waiting for the auto-handoff.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 4
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents codex
More metadata
short-description
Manual recall from ai-memory

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,436 B
  • docs SUMMARY.md 336 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 60 installs

SKILL.md

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.)

  1. 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 globalscopehits), separate from the repo's own project. ai-memory's auto-recall is per-(workspace, project) and will not surface them — pull them explicitly: memoryreadpage 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.

  1. Choose the tool for the question:

- memoryquery — targeted semantic recall ("have we decided X", before proposing design). - memoryrecent — "what's been going on lately" (N most-recent pages). - memoryexplore — open-ended "catch me up" (prose digest; takes an optional focus). - memorybriefing / memory_status — structured snapshot / lifetime counts.

  1. 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.