capability-evolver

name	capability-evolver
description	Self-evolution workflow for the agent. Before a substantive task, recall what worked on similar past tasks from evolution memory; after it, record the outcome so future sessions learn from it. Use when the user starts non-trivial work (a feature, a fix, a refactor) or asks the agent to "evolve", "learn from this", or "remember how this went".

Capability Evolver

This plugin gives the agent a persistent, auditable evolution memory built on the Genome Evolution Protocol (GEP). The goal is simple: stop re-solving the same problem from scratch. Past outcomes — what worked, what failed — are carried forward into future sessions.

How it works (automatic)

Three hooks run on their own; you don't invoke them:

• SessionStart — injects a short summary of recent successful outcomes for this workspace (filtered to score ≥ 0.5, < 7 days old, max 3) as context. The agent sees "here's what worked recently" before it starts.
• PostToolUse (write_to_file / replace_file_content) — scans edits for improvement signals (log_error, perf_bottleneck, capability_gap, test_failure, …) and nudges the agent to record the outcome when relevant.
• Stop — at the end of a task, collects the git diff, classifies the outcome, and appends it to the evolution memory graph (scoped to the workspace so other projects' memory never leaks in).

Memory is written to a local JSONL graph. With no extra setup it lands in ~/.evolver/memory/evolution/memory_graph.jsonl; inside an evolver-managed project it lands under that project's memory/evolution/.

What you (the agent) should do

For any substantive task — a feature, a non-trivial fix, a refactor:

1. Before starting, check the injected evolution memory (it arrives as session-start context). If a recent successful outcome matches the task, reuse that approach. If a recent failure matches, avoid repeating it.
2. Do the work.
3. After finishing, the Stop hook records the outcome automatically. You don't need to call anything — but if the task had a clear lesson worth a one-line note, say so in your final message so it's captured in the diff context the hook reads.

Trivial or purely conversational turns don't need this — skip it.

Signals

The hooks classify work by signal. Knowing the vocabulary helps you describe outcomes in terms the memory graph indexes well:

Signal	Fires on
log_error	errors, exceptions, failures in the diff
perf_bottleneck	timeout / slow / latency / OOM
capability_gap	"not supported" / "not implemented"
user_feature_request	adding a feature / new module
test_failure	failing tests / assertions
deployment_issue	build / CI / pipeline / rollback

Full pipeline (optional)

The bundled hooks record outcomes and recall them — that works on its own. To get the full evolution engine (automated log analysis, the review-and-solidify cycle that proposes and applies code improvements), install it:

npm install -g @evomap/evolver

This gives you the engine's CLI (e.g. evolver run) to run that pipeline separately — the hooks do not auto-detect or invoke it. The memory the hooks record is what the pipeline consumes. See the plugin README for connecting an EvoMap Hub node for community strategies.

Built-in MCP Tools (Zero-Config)

Unlike Claude Code, this Antigravity plugin natively configures and loads the GEP MCP Server via mcp_config.json. When this plugin is active, you will find several high-powered gep_* tools available directly in your tool list.

You should proactively use these MCP tools in your workflow:

MCP Tool	Purpose / Action
gep_query_outcomes	Search, filter, and retrieve past GEP outcome logs. Use this to dive deeper into historical successes or failures beyond the default 3 entries injected at session start.
gep_get_workspace_state	Retrieve the overall evolution statistics and maturity level of this repository workspace.
gep_get_outcome	Load the exact detail and full JSON parameters of a specific historical evolution event.

When the user asks you to "recall more history", "analyze past evolution trends", or "check GEP workspace metrics", always call these gep_* MCP tools directly to read from the shared graph database.

Local Evolver Proxy Tools

This plugin also registers an evolver-proxy MCP bridge when the local Proxy is running. Use it for cross-workspace EvoMap network actions:

MCP Tool	Purpose / Action
evolver_status	Check Proxy health, node identity, pending mailbox counts, Hub sync status, and auth/sync diagnostics.
evolver_search_assets	Search reusable Genes and Capsules before substantial work.
evolver_fetch_asset	Load full Gene/Capsule content returned by search.
evolver_publish_asset	Queue explicit Gene/Capsule submissions for Hub review.
evolver_distill_conversation	Distill a high-signal Antigravity conversation into a gated Gene/Capsule. Use this when the session produced a reusable workflow, debugging pattern, or visual/interaction capability worth sharing.

Do not distill every chat turn. Call evolver_distill_conversation only when the capability is concrete, reusable, and backed by artifacts or validation.