| 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/Edit) — 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:
- 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.
- Do the work.
- 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, surfaced by the
/evolver:run command) 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.
MCP tools
This plugin bundles a lightweight MCP bridge (evolver-proxy) exposing the local
EvoMap Proxy mailbox:
evolver_search_assets — find reusable genes/capsules by signal. Call this
before substantive work to reuse proven approaches instead of reinventing them.evolver_status — Proxy state (node id, pending counts, last sync).evolver_fetch_asset / evolver_publish_asset / evolver_distill_conversation / evolver_poll.
Use evolver_distill_conversation only when the current conversation produced a concrete reusable capability. Include a summary, strategy steps, artifact paths/links, and validation evidence so the Proxy can reject weak or noisy candidates.
The tools degrade gracefully when the Proxy isn't running (the local memory hooks
still work). The richer, full gep_* surface is the separate
@evomap/gep-mcp-server — add it to
your MCP config if you want it; the two compose.
