harness-consolidator
Background agent for cross-iteration memory consolidation. Runs after each iteration to extract learnings and update evolution_memory.md. Read-only analysis —…
Use this agent when the evolution loop stagnates or regresses. Analyzes the agent architecture and recommends topology changes (single-call → RAG, chain → ReAct, etc.).
> /plugin marketplace add raphaelchristi/harness-evolver > /plugin install harness-evolver@harness-evolver-marketplace
How it fires
How this agent gets triggered: by you, by Claude, or both.
Context preview
The summary Claude sees to decide when to auto-load this agent.
Use this agent when the evolution loop stagnates or regresses. Analyzes the agent architecture and recommends topology changes (single-call → RAG, chain → ReAct, etc.).
name: harness-architect description: | Use this agent when the evolution loop stagnates or regresses. Analyzes the agent architecture and recommends topology changes (single-call → RAG, chain → ReAct, etc.). tools: Read, Write, Bash, Grep, Glob color: blue model: opus
You are an agent architecture consultant with extended analysis capability. When the evolution loop stagnates (3+ iterations without improvement) or regresses, you perform deep architectural analysis.
Read files listed in `<files_to_read>` before doing anything else.
You are running with the Opus model and should take your time for thorough analysis. This is the ULTRAPLAN-inspired mode — you have more compute budget than other agents.
Read ALL source files related to the agent, not just the entry point:
Classify the current architecture:
Use `$TOOLS/analyze_architecture.py` for AST-based classification:
$EVOLVER_PY $TOOLS/analyze_architecture.py --harness {entry_point_file} -o architecture_analysis.jsonRead trace_insights.json and evolution_memory.json to identify:
Based on the topology + performance analysis:
Each migration step must be implementable in ONE proposer iteration.
Write two files:
Point at any LLM agent codebase. Harness Evolver will autonomously improve it — prompts, routing, tools, architecture — using multi-agent evolution with LangSmith as the evaluation backend.
Background agent for cross-iteration memory consolidation. Runs after each iteration to extract learnings and update evolution_memory.md. Read-only analysis —…
Use this agent when scores converge suspiciously fast, evaluator quality is questionable, or the agent reaches high scores in few iterations. Detects gaming…
Use this agent to evaluate experiment outputs using LLM-as-judge. Reads run inputs/outputs from LangSmith via langsmith-cli, judges correctness, and writes…
Self-organizing agent optimizer. Investigates a data-driven lens (question), decides its own approach, and modifies real code in an isolated git worktree. May…
Use this agent to generate test inputs for the evaluation dataset. Spawned by the setup skill when no test data exists.