How do I install the Aim Query skill?

Run npx skillmds@latest add djalmajr/aim-query in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Aim Query safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Aim Query?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Aim Query free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Aim Query?

djalmajr (@djalmajr) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Aim Query by djalmajr — Agent Skill

Name: Aim Query
Author: djalmajr

Aim Query

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.

djalmajr c6905c5 2.3 KB Updated Sep 17, 2026

File contents

aim-query

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

Steps

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.)
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.
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.
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.

---name: aim-querydescription: 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.---# aim-queryExplicit recall from an ai-memory instance (complements the automatic session-start handoff).## Steps1. **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 beforeconcluding the knowledge isn't there.

Frequently asked questions