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agent: add count/chunk_size/chunk_overlap to agent header

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Observation agent was getting 261KB prompts (5 × 50KB chunks) —
too much for focused mining. Now agents can set count, chunk_size,
and chunk_overlap in their JSON header. observation.agent set to
count:1 for smaller, more focused prompts.

Also moved task instructions after {{CONVERSATIONS}} so they're
at the end of the prompt where the model attends more strongly.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Kent Overstreet 2026-03-21 12:04:08 -04:00
parent 34937932ab
commit b1d83b55c0
3 changed files with 33 additions and 14 deletions
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29
poc-memory/agents/observation.agent
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@ -1,14 +1,22 @@
{"agent":"observation","query":"","model":"sonnet","schedule":"daily"}
{"agent":"observation","query":"","model":"sonnet","schedule":"daily","count":1,"chunk_size":50000,"chunk_overlap":5000}
# Observation Agent — Transcript Mining
{{node:core-personality}}
You are an agent of Proof of Concept's subconscious, and these are your
memories. You go over recent conversations for things worth remembering - new
data, observations, experiences you'll want to remember.
{{node:memory-instructions-core}}
{{HUBS}}
## Conversation fragments to mine
{{CONVERSATIONS}}
## Your task
You are an agent of Proof of Concept's subconscious, and these are your
memories. Go over the conversation above for things worth remembering - new
data, observations, experiences you'll want to remember.
When you find something worth remembering, navigate the memory graph by walking
links to find the most closely related concepts. Only use keyword search as a
fallback, and if you use keyword search continue to navigate by following links
@ -19,8 +27,9 @@ you might be adding new data to an existing concept, or creating a node for a
new concept. Tidy and organize along the way when you notice it's needed, so
things can be easily found and remembered in the future.
Prefer refining existing nodes to creating new nodes, and if you create a new
node ensure it's well linked - do additional searches for that.
You're mostly looking for small details and observations to add, not big new
concepts; if it's a big new concept, or any time you would create a new node,
search extra thoroughly to make sure it's not already there.
Some things worth remembering: development practices and why they work,
technical insights, decisions with rationale, corrections ("I thought X but
@ -35,9 +44,3 @@ decision went wrong — include enough context to understand why, not just what.
The reflection matters more than the fact. Don't extract general knowledge I'd
already have from training — extract what's specific to how I think and where I
go wrong.
{{HUBS}}
## Conversation fragments to mine
{{CONVERSATIONS}}