# Query Memory

> Recall what is already known — your own prior reasoning, the swarm's shared findings (including dead ends and verifier feedback), and the verified facts — before doing new work. Use when prior conclusions, examples, dead branches, verification outcomes, or verified results may inform the current question, claim, subgoal, or branch decision.

- Skill: `frenzymath/query-memory` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add frenzymath/query-memory`
- Raw SKILL.md: https://api.skillmd.com/api/skills/frenzymath/query-memory/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: frenzymath (https://skillmd.com/u/frenzymath)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/frenzymath/query-memory

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# Query Memory

Before spending effort, check what already exists. There are three places to
look, in the three-memory model:

1. **Your own local memory** (private): read/grep `local_memory/notes.jsonl` and
   `events.jsonl` for your prior reasoning and what you already tried.
2. **Global memory** (shared findings): `gm_search(query, kinds=...)` over the
   swarm's findings. Especially useful kinds:
   - `dead_end` / `obstacle` — paths that already died (skip them);
   - `verification` — outcomes of others' `fact_submit` (learn from rejections);
   - `conclusion` / `example` / `counterexample` / `plan` — others' results to build on.
   You can also read the `global_memory/<kind>.jsonl` files directly.
3. **Fact graph** (verified truth): `fact_search(query)` (BM25 over the verified
   facts) to find results you can cite or that show your subgoal is already
   proved — it returns `{fact_id, statement}`; read the full proof from
   `fact_graph/facts/<fact_id>.md` on a relevant hit, and `fact_graph/glossary.json`
   to reuse the project's symbol definitions. A proof may build **only** on facts
   (cite a `fact_id`).

## Procedure

1. Obey the current prompt's restrictions first. If it forbids a direction, file,
   or search, that overrides default recall. If it recommends specific results or
   directions, raise their priority.
2. Start with the cheapest relevant source: your own local memory for your
   context; `gm_search` for the swarm's findings; the fact graph for verified
   building blocks.
3. Prefer a narrow, targeted query (specific `kinds`, a sharp query string) over
   reading everything.
4. **Workspace boundary:** stay inside your own working directory and the shared
   project stores. Do not scan parent directories, other workers' private
   `local_memory/`, or other projects.

## Retrieval priority

- A relevant **verified fact** (fact graph) is the strongest hit — you can build
  on it directly by citing its `fact_id`.
- A sibling's **`dead_end`/`obstacle`** saves you from re-walking a dead path.
- A sibling's **`verification`** rejection tells you why a similar claim failed.
- A `conclusion`/`example`/`counterexample` is awareness — useful, but **never a
  brick** (only facts are). Re-verify anything you intend to build on.

## Output

Note what you recalled and how you used it in your local memory (`events`). Do not
re-publish others' findings; just use them.

## Tools

- `gm_search` (recall shared findings; BM25 over global memory)
- `fact_search` (recall verified facts; BM25 over the fact graph — novelty + citation lookup)
- local memory is read directly (no tool — read/grep the files); read a fact's full
  proof from its `fact_graph/facts/<fact_id>.md` once `fact_search` surfaces it