feat(plugins): ADR-098 Part 4 — standardize neural-learning hook

Closes the 16% gap (7 of 43 plugin agents) flagged by the ADR-098
audit: agents that completed tasks without feeding the SONA learning
loop, costing the system long-term improvement signal.

Each affected agent gets the canonical 5-line tail:

  ### Neural Learning

  After completing tasks, feed the <role-specific-context>:
  ```bash
  npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
  ```

The "<role-specific-context>" is per-agent so the tail reads native
rather than copy-pasted:

  ruflo-daa/daa-specialist            adaptive-agent learning loop
  ruflo-goals/goal-planner            planner trajectory store
  ruflo-intelligence/intelligence-spec pattern-discovery learning loop
                                       (next RETRIEVE benefits from this run)
  ruflo-iot-cognitum/telemetry-analyz  telemetry baseline learning
                                       (Z-score thresholds adapt)
  ruflo-ruvllm/llm-specialist         router outcome learning
                                       (provider/model picks compound)
  ruflo-sparc/sparc-orchestrator      phase-quality learning loop
                                       (quality gates self-tune)
  ruflo-swarm/coordinator             coordination outcome learning
                                       (topology + role choices compound)

Re-audit confirms 0 of 43 plugin agents missing the hook (was 7 of 43).

ADR-098 acceptance-criteria item closed: "All 43 plugin agents include
a `hooks post-task --train-neural true` invocation."

Parts 1 (capability sync), 2 (token diet), 3 (model rightsizing), and
5 (worker dispatch) remain as separate iterations.

plugins/ruflo-daa/agents/daa-specialist.md

@@ -33,3 +33,10 @@ npx @claude-flow/cli@latest memory search --query "cognitive pattern for TASK" -
 
 - **ruflo-intelligence**: SONA neural patterns power cognitive adaptation and trajectory learning
 - **ruflo-agentdb**: Pattern storage backend for DAA knowledge sharing
+
+### Neural Learning
+
+After completing tasks, feed the adaptive-agent learning loop:
+```bash
+npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
+```

plugins/ruflo-goals/agents/goal-planner.md

@@ -80,3 +80,10 @@ Use MCP tools for persistence and learning:
 - `mcp__claude-flow__hooks_intelligence_trajectory-start` / `trajectory-step` / `trajectory-end` — record execution trajectories for learning
 - `mcp__claude-flow__neural_predict` — predict optimal approaches based on learned patterns
 - `mcp__claude-flow__workflow_create` / `workflow_execute` — codify repeatable plans as workflows
+
+### Neural Learning
+
+After completing a plan, feed the planner trajectory store so future replans inherit the outcome:
+```bash
+npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
+```

plugins/ruflo-intelligence/agents/intelligence-specialist.md

@@ -32,3 +32,10 @@ npx @claude-flow/cli@latest memory search --query "routing for TASK_TYPE" --name
 
 - **ruflo-agentdb**: HNSW-indexed pattern storage backing the intelligence pipeline's RETRIEVE step
 - **ruflo-daa**: Dynamic Agentic Architecture — cognitive patterns that feed into intelligence routing
+
+### Neural Learning
+
+After completing tasks, feed the pattern-discovery learning loop so the next RETRIEVE benefits from this run:
+```bash
+npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
+```

plugins/ruflo-iot-cognitum/agents/telemetry-analyzer.md

@@ -44,3 +44,10 @@ Telemetry and anomalies are persisted to AgentDB with vector indexing:
 - **Readings**: `iot-telemetry` namespace, tagged by device and fleet
 - **Anomalies**: `iot-telemetry-anomalies` namespace, tagged by type and action
 - **Vector search**: HNSW-indexed similarity search across telemetry vectors (M=16, efConstruction=200)
+
+### Neural Learning
+
+After each analysis pass, feed the telemetry baseline learning so future Z-score thresholds adapt:
+```bash
+npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
+```

plugins/ruflo-ruvllm/agents/llm-specialist.md

@@ -28,3 +28,10 @@ Store successful model configurations and prompt templates:
 npx @claude-flow/cli@latest memory store --namespace llm-configs --key "config-PROVIDER-MODEL" --value "PARAMS_AND_RESULTS"
 npx @claude-flow/cli@latest memory search --query "config for PROVIDER" --namespace llm-configs
 ```
+
+### Neural Learning
+
+After each routing or fine-tune cycle, feed the router outcome learning so future provider/model picks compound this run:
+```bash
+npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
+```

plugins/ruflo-sparc/agents/sparc-orchestrator.md

@@ -136,3 +136,10 @@ Use learned patterns to predict phase durations and common blockers:
 - `mcp__claude-flow__hooks_intelligence_trajectory-start` / `trajectory-step` / `trajectory-end` — record execution trajectories
 - `mcp__claude-flow__neural_predict` / `neural_train` — predict and learn from SPARC cycles
 - `mcp__claude-flow__workflow_create` / `workflow_execute` — automate repeatable phase workflows
+
+### Neural Learning
+
+After each phase or full SPARC cycle, feed the phase-quality learning loop so quality gates self-tune:
+```bash
+npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
+```

plugins/ruflo-swarm/agents/coordinator.md

@@ -22,3 +22,10 @@ Anti-drift rules:
 
 - **ruflo-goals**: GOAP planning for complex multi-session objectives that swarms execute
 - **ruflo-autopilot**: Autonomous /loop execution of swarm-coordinated work
+
+### Neural Learning
+
+After completing a swarm cycle, feed the coordination outcome learning so topology + role choices compound:
+```bash
+npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
+```