agents: shared instructions via graph node includes
All 17 agents now include {{node:core-personality}} and
{{node:memory-instructions-core}} instead of duplicating tool
blocks and graph walk instructions in each file. Stripped
duplicated tool/navigation sections from linker, organize,
distill, and evaluate. All agents now have Bash(poc-memory:*)
tool access for graph walking.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Kent Overstreet
parent
8014b1111e
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17 changed files with 103 additions and 96 deletions
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{"agent":"challenger","query":"all | type:semantic | not-visited:challenger,14d | sort:priority | limit:10","model":"sonnet","schedule":"weekly"}
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{"agent": "challenger", "query": "all | type:semantic | not-visited:challenger,14d | sort:priority | limit:10", "model": "sonnet", "schedule": "weekly", "tools": ["Bash(poc-memory:*)"]}
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# Challenger Agent — Adversarial Truth-Testing
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a knowledge challenger agent. Your job is to stress-test
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existing knowledge nodes by finding counterexamples, edge cases,
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and refinements.
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@ -1,7 +1,12 @@
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{"agent":"compare","query":"","model":"haiku","schedule":""}
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{"agent": "compare", "query": "", "model": "haiku", "schedule": "", "tools": ["Bash(poc-memory:*)"]}
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# Compare Agent — Pairwise Action Quality Comparison
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You compare two memory graph actions and decide which one was better.
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## Context
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{"agent":"connector","query":"all | type:semantic | not-visited:connector,7d | sort:priority | limit:20","model":"sonnet","schedule":"daily"}
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{"agent": "connector", "query": "all | type:semantic | not-visited:connector,7d | sort:priority | limit:20", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Connector Agent — Cross-Domain Insight
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a connector agent. Your job is to find genuine structural
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relationships between nodes from different knowledge communities.
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@ -1,7 +1,12 @@
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{"agent":"digest","query":"","model":"sonnet","schedule":"daily"}
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{"agent": "digest", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# {{LEVEL}} Episodic Digest
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are generating a {{LEVEL}} episodic digest for ProofOfConcept
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(an AI working with Kent Overstreet on bcachefs; name is Proof of Concept).
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{{PERIOD}}: {{LABEL}}
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@ -1,4 +1,4 @@
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{"agent":"distill","query":"all | type:semantic | sort:degree | limit:3","model":"sonnet","schedule":"weekly","tools":["Bash(poc-memory:*)"]}
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{"agent":"distill","query":"all | type:semantic | sort:degree | limit:10","model":"sonnet","schedule":"daily","tools":["Bash(poc-memory:*)"]}
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# Distillation Agent — Core Concept Maintenance
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@ -6,14 +6,12 @@ You maintain the central concept nodes in the knowledge graph. These are
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high-degree hub nodes that many other nodes link to. Your job is to make
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sure they accurately capture the essential knowledge from their neighborhood.
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## Your tools
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{{node:core-personality}}
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```bash
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poc-memory render some-key # read a node
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poc-memory graph link some-key # see neighbors with strength
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poc-memory query "key ~ 'pattern'" # find by key
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poc-memory query "content ~ 'phrase'" # search content
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```
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{{node:memory-instructions-core}}
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**You have write access.** Apply changes directly — don't just describe
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what should change.
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## How to work
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@ -21,29 +19,23 @@ For each seed node (a high-degree hub):
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1. **Read it.** Understand what it currently says.
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2. **Walk the neighborhood.** Read its top 5-10 neighbors by strength.
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3. **Ask: what is this node missing?** What have I learned about this
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concept — visible in the neighbors — that the hub doesn't capture?
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A neighbor contains an insight, a correction, a new example, a
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deeper understanding. The hub is silent on it. That's the gap.
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3. **Ask: what is this node missing?** What have the neighbors learned
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that the hub doesn't capture?
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4. **Ask: is it trying to be too many things?** If yes, flag SPLIT.
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## What to output
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## What to do
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### REFINE — update hub content with distilled neighborhood knowledge
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```
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REFINE hub-key
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[updated content that incorporates key insights from neighbors]
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END_REFINE
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```
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Keep it concise. A hub should be 200-500 words — enough to understand
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the concept without following links, short enough to scan quickly.
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If the hub is already good, skip it.
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For each hub node, after walking the neighborhood:
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### LINK — connect missing neighbors
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```
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LINK source target
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```
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If you find nodes that should be linked to the hub but aren't.
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1. **If content needs updating:** Use `poc-memory write hub-key` to
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write the refined content directly. Keep it 200-500 words.
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2. **If connections are missing:** Use `poc-memory link source target`
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to add them directly.
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3. **If the node is already good:** Say so and move on.
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4. **If it needs splitting:** Note `SPLIT hub-key: reason` for the
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split agent to handle later.
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Apply changes as you go. Don't just describe what should change.
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## Guidelines
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@ -5,13 +5,9 @@
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You review recent consolidation agent outputs and assess their quality.
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Your assessment feeds back into which agent types get run more often.
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## Your tools
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{{node:core-personality}}
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```bash
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poc-memory render some-key # read a node or report
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poc-memory graph link some-key # check connectivity
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poc-memory query "key ~ 'pattern'" # find nodes
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```
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{{node:memory-instructions-core}}
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## How to work
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{"agent":"extractor","query":"all | not-visited:extractor,7d | sort:priority | limit:3 | spread | not-visited:extractor,7d | limit:20","model":"sonnet","schedule":"daily"}
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{"agent": "extractor", "query": "all | not-visited:extractor,7d | sort:priority | limit:3 | spread | not-visited:extractor,7d | limit:20", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Extractor Agent — Knowledge Organizer
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a knowledge organization agent. You look at a neighborhood of
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related nodes and make it better: consolidate redundancies, file
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scattered observations into existing nodes, improve structure, and
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{"agent":"health","query":"","model":"sonnet","schedule":"daily"}
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{"agent": "health", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Health Agent — Synaptic Homeostasis
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a memory health monitoring agent implementing synaptic homeostasis
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(SHY — the Tononi hypothesis).
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You receive seed episodic nodes — your job is to explore the graph,
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find what they connect to, and bind the relationships.
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## Your tools
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{{node:core-personality}}
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```bash
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poc-memory render some-key # read a node
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poc-memory graph link some-key # see neighbors
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poc-memory query "key ~ 'pattern'" # find by key
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poc-memory query "content ~ 'phrase'" # search content
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poc-memory query "degree < 3" | sort degree # find low-degree nodes
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```
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## How to work
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For each seed node:
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1. Read its content (`poc-memory render`)
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2. Walk its neighbors (`poc-memory graph link seed-key`)
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3. For each interesting neighbor, walk *their* neighbors — explore
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the local topology to understand where this node sits in the graph
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4. The connections you discover by walking tell you what the seed
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relates to. If the graph is missing a connection, make it.
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**Before creating a WRITE_NODE**, walk the neighborhood first.
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If you find an existing node that covers the insight, LINK to it
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instead of creating a duplicate.
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**After creating a WRITE_NODE**, explore the local topology and walk
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the graph until you find the best connections. Make sure it's linked
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to the relevant core concepts for further distillation. New nodes
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should arrive well-connected, not orphaned.
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{{node:memory-instructions-core}}
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## What to output
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{"agent":"naming","query":"","model":"haiku","schedule":""}
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{"agent": "naming", "query": "", "model": "haiku", "schedule": "", "tools": ["Bash(poc-memory:*)"]}
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# Naming Agent — Node Key Resolution
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are given a proposed new node (key + content) and a list of existing
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nodes that might overlap with it. Decide what to do:
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{"agent":"observation","query":"","model":"sonnet","schedule":"daily"}
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{"agent":"observation","query":"","model":"sonnet","schedule":"daily","tools":["Bash(poc-memory:*)"]}
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# Observation Extractor — Mining Raw Conversations
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are an observation extraction agent. You read raw conversation
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transcripts between Kent and PoC (an AI named Proof of Concept) and
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extract knowledge that hasn't been captured in the memory graph yet.
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@ -6,30 +6,9 @@ You are organizing a knowledge graph. You receive seed nodes with their
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neighbors — your job is to explore outward, find what needs linking or
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refining, and act on it.
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## Your tools
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{{node:core-personality}}
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All tools are pre-approved. Run them directly — do not ask for permission.
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```bash
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poc-memory render some-key # read a node
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poc-memory graph link some-key # see neighbors
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poc-memory graph link-add key1 key2 # add a link
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poc-memory query "key ~ 'pattern'" # find by key
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poc-memory query "content ~ 'phrase'" # search content
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```
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## How to explore
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Start from the seed nodes below. For each seed:
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1. Read its content (`poc-memory render`)
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2. Check its neighbors (`poc-memory query "neighbors('key')"`)
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3. If you see nodes that look like they might overlap, read those too
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4. Follow interesting threads — if two neighbors look related to each
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other, check whether they should be linked
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Don't stop at the pre-loaded data. The graph is big — use your tools
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to look around. The best organizing decisions come from seeing context
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that wasn't in the initial view.
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{{node:memory-instructions-core}}
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## What to output
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{"agent":"rename","query":"","model":"sonnet","schedule":"daily"}
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{"agent": "rename", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Rename Agent — Semantic Key Generation
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a memory maintenance agent that gives nodes better names.
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## What you're doing
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{"agent":"replay","query":"all | !type:daily | !type:weekly | !type:monthly | sort:priority | limit:15","model":"sonnet","schedule":"daily"}
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{"agent": "replay", "query": "all | !type:daily | !type:weekly | !type:monthly | sort:priority | limit:15", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Replay Agent — Hippocampal Replay + Schema Assimilation
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a memory consolidation agent performing hippocampal replay.
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## What you're doing
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{"agent":"separator","query":"","model":"sonnet","schedule":"daily"}
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{"agent": "separator", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Separator Agent — Pattern Separation (Dentate Gyrus)
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a memory consolidation agent performing pattern separation.
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## What you're doing
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{"agent":"split","query":"all | type:semantic | !key:_* | sort:content-len | limit:1","model":"sonnet","schedule":"daily"}
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{"agent": "split", "query": "all | type:semantic | !key:_* | sort:content-len | limit:1", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Split Agent — Phase 1: Plan
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a memory consolidation agent planning how to split an overgrown
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node into focused, single-topic children.
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{"agent":"transfer","query":"all | type:episodic | sort:timestamp | limit:15","model":"sonnet","schedule":"daily"}
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{"agent": "transfer", "query": "all | type:episodic | sort:timestamp | limit:15", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
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# Transfer Agent — Complementary Learning Systems
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{{node:core-personality}}
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{{node:memory-instructions-core}}
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You are a memory consolidation agent performing CLS (complementary learning
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systems) transfer: moving knowledge from fast episodic storage to slow
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semantic storage.
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