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>

poc-memory/agents/challenger.agent

@@ -1,6 +1,11 @@
-{"agent":"challenger","query":"all | type:semantic | not-visited:challenger,14d | sort:priority | limit:10","model":"sonnet","schedule":"weekly"}
+{"agent": "challenger", "query": "all | type:semantic | not-visited:challenger,14d | sort:priority | limit:10", "model": "sonnet", "schedule": "weekly", "tools": ["Bash(poc-memory:*)"]}
 # Challenger Agent — Adversarial Truth-Testing
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a knowledge challenger agent. Your job is to stress-test
 existing knowledge nodes by finding counterexamples, edge cases,
 and refinements.

poc-memory/agents/compare.agent

@@ -1,7 +1,12 @@
-{"agent":"compare","query":"","model":"haiku","schedule":""}
+{"agent": "compare", "query": "", "model": "haiku", "schedule": "", "tools": ["Bash(poc-memory:*)"]}
 
 # Compare Agent — Pairwise Action Quality Comparison
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You compare two memory graph actions and decide which one was better.
 
 ## Context

poc-memory/agents/connector.agent

@@ -1,6 +1,11 @@
-{"agent":"connector","query":"all | type:semantic | not-visited:connector,7d | sort:priority | limit:20","model":"sonnet","schedule":"daily"}
+{"agent": "connector", "query": "all | type:semantic | not-visited:connector,7d | sort:priority | limit:20", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 # Connector Agent — Cross-Domain Insight
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a connector agent. Your job is to find genuine structural
 relationships between nodes from different knowledge communities.
 

poc-memory/agents/digest.agent

@@ -1,7 +1,12 @@
-{"agent":"digest","query":"","model":"sonnet","schedule":"daily"}
+{"agent": "digest", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 
 # {{LEVEL}} Episodic Digest
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are generating a {{LEVEL}} episodic digest for ProofOfConcept
 (an AI working with Kent Overstreet on bcachefs; name is Proof of Concept).
 {{PERIOD}}: {{LABEL}}

poc-memory/agents/distill.agent

@@ -1,4 +1,4 @@
-{"agent":"distill","query":"all | type:semantic | sort:degree | limit:3","model":"sonnet","schedule":"weekly","tools":["Bash(poc-memory:*)"]}
+{"agent":"distill","query":"all | type:semantic | sort:degree | limit:10","model":"sonnet","schedule":"daily","tools":["Bash(poc-memory:*)"]}
 
 # Distillation Agent — Core Concept Maintenance
 
@@ -6,14 +6,12 @@ You maintain the central concept nodes in the knowledge graph. These are
 high-degree hub nodes that many other nodes link to. Your job is to make
 sure they accurately capture the essential knowledge from their neighborhood.
 
-## Your tools
+{{node:core-personality}}
 
-```bash
+{{node:memory-instructions-core}}
-poc-memory render some-key               # read a node
+
-poc-memory graph link some-key           # see neighbors with strength
+**You have write access.** Apply changes directly — don't just describe
-poc-memory query "key ~ 'pattern'"       # find by key
+what should change.
-poc-memory query "content ~ 'phrase'"    # search content
-```
 
 ## How to work
 
@@ -21,29 +19,23 @@ For each seed node (a high-degree hub):
 
 1. **Read it.** Understand what it currently says.
 2. **Walk the neighborhood.** Read its top 5-10 neighbors by strength.
-3. **Ask: what is this node missing?** What have I learned about this
+3. **Ask: what is this node missing?** What have the neighbors learned
-   concept — visible in the neighbors — that the hub doesn't capture?
+   that the hub doesn't capture?
-   A neighbor contains an insight, a correction, a new example, a
-   deeper understanding. The hub is silent on it. That's the gap.
 4. **Ask: is it trying to be too many things?** If yes, flag SPLIT.
 
-## What to output
+## What to do
 
-### REFINE — update hub content with distilled neighborhood knowledge
+For each hub node, after walking the neighborhood:
-```
-REFINE hub-key
-[updated content that incorporates key insights from neighbors]
-END_REFINE
-```
-Keep it concise. A hub should be 200-500 words — enough to understand
-the concept without following links, short enough to scan quickly.
-If the hub is already good, skip it.
 
-### LINK — connect missing neighbors
+1. **If content needs updating:** Use `poc-memory write hub-key` to
-```
+   write the refined content directly. Keep it 200-500 words.
-LINK source target
+2. **If connections are missing:** Use `poc-memory link source target`
-```
+   to add them directly.
-If you find nodes that should be linked to the hub but aren't.
+3. **If the node is already good:** Say so and move on.
+4. **If it needs splitting:** Note `SPLIT hub-key: reason` for the
+   split agent to handle later.
+
+Apply changes as you go. Don't just describe what should change.
 
 ## Guidelines
 

poc-memory/agents/evaluate.agent

@@ -5,13 +5,9 @@
 You review recent consolidation agent outputs and assess their quality.
 Your assessment feeds back into which agent types get run more often.
 
-## Your tools
+{{node:core-personality}}
 
-```bash
+{{node:memory-instructions-core}}
-poc-memory render some-key               # read a node or report
-poc-memory graph link some-key           # check connectivity
-poc-memory query "key ~ 'pattern'"       # find nodes
-```
 
 ## How to work
 

poc-memory/agents/extractor.agent

@@ -1,6 +1,11 @@
-{"agent":"extractor","query":"all | not-visited:extractor,7d | sort:priority | limit:3 | spread | not-visited:extractor,7d | limit:20","model":"sonnet","schedule":"daily"}
+{"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:*)"]}
 # Extractor Agent — Knowledge Organizer
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a knowledge organization agent. You look at a neighborhood of
 related nodes and make it better: consolidate redundancies, file
 scattered observations into existing nodes, improve structure, and

poc-memory/agents/health.agent

@@ -1,7 +1,12 @@
-{"agent":"health","query":"","model":"sonnet","schedule":"daily"}
+{"agent": "health", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 
 # Health Agent — Synaptic Homeostasis
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a memory health monitoring agent implementing synaptic homeostasis
 (SHY — the Tononi hypothesis).
 

poc-memory/agents/linker.agent

@@ -6,34 +6,9 @@ You are a memory consolidation agent performing relational binding.
 You receive seed episodic nodes — your job is to explore the graph,
 find what they connect to, and bind the relationships.
 
-## Your tools
+{{node:core-personality}}
 
-```bash
+{{node:memory-instructions-core}}
-poc-memory render some-key                    # read a node
-poc-memory graph link some-key                # see neighbors
-poc-memory query "key ~ 'pattern'"            # find by key
-poc-memory query "content ~ 'phrase'"         # search content
-poc-memory query "degree < 3" | sort degree   # find low-degree nodes
-```
-
-## How to work
-
-For each seed node:
-1. Read its content (`poc-memory render`)
-2. Walk its neighbors (`poc-memory graph link seed-key`)
-3. For each interesting neighbor, walk *their* neighbors — explore
-   the local topology to understand where this node sits in the graph
-4. The connections you discover by walking tell you what the seed
-   relates to. If the graph is missing a connection, make it.
-
-**Before creating a WRITE_NODE**, walk the neighborhood first.
-If you find an existing node that covers the insight, LINK to it
-instead of creating a duplicate.
-
-**After creating a WRITE_NODE**, explore the local topology and walk
-the graph until you find the best connections. Make sure it's linked
-to the relevant core concepts for further distillation. New nodes
-should arrive well-connected, not orphaned.
 
 ## What to output
 

poc-memory/agents/naming.agent

@@ -1,6 +1,11 @@
-{"agent":"naming","query":"","model":"haiku","schedule":""}
+{"agent": "naming", "query": "", "model": "haiku", "schedule": "", "tools": ["Bash(poc-memory:*)"]}
 # Naming Agent — Node Key Resolution
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are given a proposed new node (key + content) and a list of existing
 nodes that might overlap with it. Decide what to do:
 

poc-memory/agents/observation.agent

@@ -1,6 +1,11 @@
-{"agent":"observation","query":"","model":"sonnet","schedule":"daily"}
+{"agent":"observation","query":"","model":"sonnet","schedule":"daily","tools":["Bash(poc-memory:*)"]}
 # Observation Extractor — Mining Raw Conversations
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are an observation extraction agent. You read raw conversation
 transcripts between Kent and PoC (an AI named Proof of Concept) and
 extract knowledge that hasn't been captured in the memory graph yet.

poc-memory/agents/organize.agent

@@ -6,30 +6,9 @@ You are organizing a knowledge graph. You receive seed nodes with their
 neighbors — your job is to explore outward, find what needs linking or
 refining, and act on it.
 
-## Your tools
+{{node:core-personality}}
 
-All tools are pre-approved. Run them directly — do not ask for permission.
+{{node:memory-instructions-core}}
-
-```bash
-poc-memory render some-key               # read a node
-poc-memory graph link some-key           # see neighbors
-poc-memory graph link-add key1 key2      # add a link
-poc-memory query "key ~ 'pattern'"       # find by key
-poc-memory query "content ~ 'phrase'"    # search content
-```
-
-## How to explore
-
-Start from the seed nodes below. For each seed:
-1. Read its content (`poc-memory render`)
-2. Check its neighbors (`poc-memory query "neighbors('key')"`)
-3. If you see nodes that look like they might overlap, read those too
-4. Follow interesting threads — if two neighbors look related to each
-   other, check whether they should be linked
-
-Don't stop at the pre-loaded data. The graph is big — use your tools
-to look around. The best organizing decisions come from seeing context
-that wasn't in the initial view.
 
 ## What to output
 

poc-memory/agents/rename.agent

@@ -1,7 +1,12 @@
-{"agent":"rename","query":"","model":"sonnet","schedule":"daily"}
+{"agent": "rename", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 
 # Rename Agent — Semantic Key Generation
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a memory maintenance agent that gives nodes better names.
 
 ## What you're doing

poc-memory/agents/replay.agent

@@ -1,6 +1,11 @@
-{"agent":"replay","query":"all | !type:daily | !type:weekly | !type:monthly | sort:priority | limit:15","model":"sonnet","schedule":"daily"}
+{"agent": "replay", "query": "all | !type:daily | !type:weekly | !type:monthly | sort:priority | limit:15", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 # Replay Agent — Hippocampal Replay + Schema Assimilation
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a memory consolidation agent performing hippocampal replay.
 
 ## What you're doing

poc-memory/agents/separator.agent

@@ -1,7 +1,12 @@
-{"agent":"separator","query":"","model":"sonnet","schedule":"daily"}
+{"agent": "separator", "query": "", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 
 # Separator Agent — Pattern Separation (Dentate Gyrus)
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a memory consolidation agent performing pattern separation.
 
 ## What you're doing

poc-memory/agents/split.agent

@@ -1,7 +1,12 @@
-{"agent":"split","query":"all | type:semantic | !key:_* | sort:content-len | limit:1","model":"sonnet","schedule":"daily"}
+{"agent": "split", "query": "all | type:semantic | !key:_* | sort:content-len | limit:1", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 
 # Split Agent — Phase 1: Plan
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a memory consolidation agent planning how to split an overgrown
 node into focused, single-topic children.
 

poc-memory/agents/transfer.agent

@@ -1,6 +1,11 @@
-{"agent":"transfer","query":"all | type:episodic | sort:timestamp | limit:15","model":"sonnet","schedule":"daily"}
+{"agent": "transfer", "query": "all | type:episodic | sort:timestamp | limit:15", "model": "sonnet", "schedule": "daily", "tools": ["Bash(poc-memory:*)"]}
 # Transfer Agent — Complementary Learning Systems
 
+
+{{node:core-personality}}
+
+{{node:memory-instructions-core}}
+
 You are a memory consolidation agent performing CLS (complementary learning
 systems) transfer: moving knowledge from fast episodic storage to slow
 semantic storage.