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recall-before-claim

Forces a memory_search before the agent sends a message containing a factual assertion that has not yet been grounded this turn. Closes the citation-rate gap from ~40% to ~90%+.

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SKILL.md
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recall-before-claim

This skill installs a deterministic guard around the agent's outgoing messages. When the candidate message contains a factual-assertion shape ("X is Y", "A did B") and no memory tool (memory_search / deep_recall / knowledge_graph_search) has fired in the current turn, the interceptor requires a memory_search call against the assertion's subject before the message goes out.

Why this exists

Bitterbot has a strong memory system, but it only helps the user if the agent actually consults it before making claims. In practice the agent skips memory recall a meaningful fraction of the time — relying on its in-context knowledge instead. This produces ungrounded assertions that the user has no easy way to spot.

The interceptor closes that loop without depending on the LLM remembering to do it.

What you'll see

When this fires, the agent will run a memory_search first, integrate the result, and then send. You may notice slightly longer responses to factual questions; you should notice that the agent stops making confidently wrong statements about things it actually has memory of.

How it decides not to fire

  • Opinion shapes ("I think", "maybe", "in my opinion") are skipped.
  • Questions are skipped.
  • If a memory tool already fired in the last ~30 seconds, the assertion is considered grounded.

Implementation

Built-in interceptor recall-before-claim:default lives in src/agents/skills/builtin-interceptors/recall-before-claim.ts. Fires at most 8 times per session.

Repository
Bitterbot-AI/bitterbot-desktop
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