AugmentClaude
Notes

Remnic Recall

By joshuaswarren· joshuaswarren/remnic· 0

Search your conversation memory by natural-language query to retrieve prior context.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.
    Install
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    This copies the whole skill folder into ~/.claude/skills/remnic-recall-joshuaswarren/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
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  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

Search Remnic memories by natural-language query. Trigger phrases include "what do you remember about", "recall anything on", "have we discussed".

What this skill does

When to use

Use when the user or the current task needs prior context from Remnic. This is the default first step for any non-trivial Claude Code turn that could benefit from memory.

Triggers:

  • "What do you remember about …"
  • "Have we talked about …"
  • /remnic:recall <query> slash command invocation.
  • A new task begins and the agent wants background.

Inputs

  • query (required) — natural-language question or topic string.
  • Optional: caller-supplied budget hint (brief, deep).

Procedure

  1. Build a concise natural-language query from the user's message. Prefer the user's own wording.
  2. Call remnic_recall with that query; request 3–8 results unless the caller specified otherwise.
  3. Filter results for topical relevance.
  4. Present 1–5 bullet points summarizing the relevant memories, attributed when useful.
  5. If nothing relevant came back, say so plainly and suggest remnic-remember if there is something worth storing now.

Efficiency plan

  • One broad recall beats several narrow ones.
  • Reuse recall results within the same turn — do not re-query the same topic.
  • Skip recall for trivially local tasks.

Pitfalls and fixes

  • Pitfall: Quoting irrelevant recalls just because they came back. Fix: Filter for relevance before surfacing.
  • Pitfall: Over-narrowing the query. Fix: Start broad; refine only when the first pass was noisy.
  • Pitfall: Showing raw memory payloads. Fix: Summarize in the user's own terms.

Verification checklist

  • remnic_recall was called with a natural-language query.
  • Results were filtered for relevance before being shown.
  • Summary is ≤ 5 bullets unless the user asked for more.
  • Canonical remnic_recall was used over legacy engram_recall.

Tool names: canonical name is remnic_recall. The legacy engram_recall alias remains accepted during v1.x.

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