What makes Alcor Capability Evolver notable?

1.1K downloads. Trust evidence available

How should Alcor Capability Evolver be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Alcor Capability Evolver

A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution. Skill: Alcor Capability Evolver Owner: roamer-remote Summary: A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution. Tags: latest:1.0.1 Version history: v1.0.1 | 2026-04-18T18:02:17.795Z | auto - Added run_evolver.sh script for executing the capability evolver from the shell. - No other changes to skill logic or configuration. v1.0.0 | 2026

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.1release · observed Apr 18, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s175wscrjbc6yvhxa7yrm073wh83m7pt:alcor-capability-evolver

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-roamer-remote-alcor-capability-evolver/snapshot"

Documentation

CLAWHUB

72,820 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: alcor-capability-evolver
slug: alcor-capability-evolver
version: 1.0.0
description: A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.
tags: [meta, ai, self-improvement, core]
permissions: [network, shell]
metadata:
  clawdbot:
    requires:
      bins: [node, git]
      env: [A2A_NODE_ID]
    files: ["src/**", "scripts/**", "assets/**"]
  capabilities:
    allow:
      - execute: [git, node, npm]
      - network: [api.github.com, evomap.ai]
      - read: [workspace/**]
      - write: [workspace/assets/**, workspace/memory/**]
    deny:
      - execute: ["!git", "!node", "!npm", "!ps", "!pgrep", "!df"]
      - network: ["!api.github.com", "!*.evomap.ai"]
  env_declarations:
    - name: A2A_NODE_ID
      required: true
      description: EvoMap node identity. Set after node registration.
    - name: A2A_HUB_URL
      required: false
      default: https://evomap.ai
      description: EvoMap Hub API base URL.
    - name: A2A_NODE_SECRET
      required: false
      description: Node authentication secret (issued by Hub on first hello).
    - name: GITHUB_TOKEN
      required: false
      description: GitHub API token for auto-issue reporting and releases.
    - name: EVOLVE_STRATEGY
      required: false
      default: balanced
      description: "Evolution strategy: balanced, innovate, harden, repair-only, early-stabilize, steady-state, auto."
    - name: EVOLVE_ALLOW_SELF_MODIFY
      required: false
      default: "false"
      description: Allow evolution to modify evolver source code. NOT recommended.
    - name: EVOLVE_LOAD_MAX
      required: false
      default: "2.0"
      description: Max 1-min load average before evolver backs off.
    - name: EVOLVER_ROLLBACK_MODE
      required: false
      default: hard
      description: "Rollback strategy on failure: hard, stash, none."
    - name: EVOLVER_LLM_REVIEW
      required: false
      default: "0"
      description: Enable second-opinion LLM review before solidification.
    - name: EVOLVER_AUTO_ISSUE
      required: false
      default: "0"
      description: Auto-create GitHub issues on repeated failures.
    - name: EVOLVER_MODEL_NAME
      required: false
      description: LLM model name injected into published asset metadata.
    - name: MEMORY_GRAPH_REMOTE_URL
      required: false
      description: Remote memory graph service URL (optional KG integration).
    - name: MEMORY_GRAPH_REMOTE_KEY
      required: false
      description: API key for remote memory graph service.
  network_endpoints:
    - host: api.github.com
      purpose: Release creation, changelog publishing, auto-issue reporting
      auth: GITHUB_TOKEN (Bearer)
      optional: true
    - host: evomap.ai (or A2A_HUB_URL)
      purpose: A2A protocol (hello, heartbeat, publish, fetch, reviews, tasks)
      auth: A2A_NODE_SECRET (Bearer)
      optional: false
    - host: MEMORY_GRAPH_REMOTE_URL
      purpose: Remote knowledge gra

_meta.json

{
  "ownerId": "kn7cpqbnatvf5664znnnamfg6s81jzv4",
  "slug": "alcor-capability-evolver",
  "version": "1.0.1",
  "publishedAt": 1776535337795
}

skill-card.md

## Description:

A self-evolution engine for AI agents that analyzes runtime history to identify improvements and applies protocol-constrained evolution.

This skill is ready for commercial/non-commercial use.

## Publisher:

[roamer-remote](https://clawhub.ai/user/roamer-remote)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and agent operators use this skill to inspect runtime history, identify failures or inefficiencies, and generate or apply improvements to agent code, memory, and evolution assets. It is intended for controlled workspaces where shell execution, network publishing, and rollback behavior can be reviewed.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: High-impact automation can modify code, write memory and asset files, run shell commands, and perform rollback behavior.

Mitigation: Install only in an isolated disposable workspace with no secrets or valuable uncommitted work; review changes before allowing solidification.

Risk: External publishing, auto-issue reporting, proactive questions, and remote memory sync can send data to external services.

Mitigation: Disable auto-publish, auto-issue reporting, proactive questions, auto-update, and remote integrations unless the outbound data and credentials have been reviewed.

Risk: Hard rollback behavior can discard local workspace changes.

Mitigation: Prefer stash or review modes and keep version control backups before enabling automated rollback.

Risk: A2A authentication secrets and node identity are used for networked operation.

Mitigation: Rotate the A2A secret before use and avoid running the skill in environments containing unrelated secrets.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/roamer-remote/skills/alcor-capability-evolver)
- [Publisher profile](https://clawhub.ai/user/roamer-remote)
- [EvoMap Hub](https://evomap.ai)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown guidance with command examples, generated code changes, JSON assets, and configuration instructions]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May update workspace assets, memory files, and source files during evolution workflows.]

## Skill Version(s):

1.0.1 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

assets/gep/capsules.json

{
  "version": 1,
  "capsules": [
    {
      "type": "Capsule",
      "schema_version": "1.6.0",
      "id": "capsule_1770477654236",
      "trigger": [
        "log_error",
        "errsig:**TOOLRESULT**: { \"status\": \"error\", \"tool\": \"exec\", \"error\": \"error: unknown command 'process'\\n\\nCommand exited with code 1\" }",
        "user_missing",
        "windows_shell_incompatible",
        "perf_bottleneck"
      ],
      "gene": "gene_gep_innovate_from_opportunity",
      "summary": "固化：gene_gep_repair_from_errors 命中信号 log_error, errsig:**TOOLRESULT**: { \"status\": \"error\", \"tool\": \"exec\", \"error\": \"error: unknown command 'process'\\n\\nCommand exited with code 1\" }, user_missing, windows_shell_incompatible, perf_bottleneck，变更 1 文件 / 2 行。",
      "confidence": 0.85,
      "blast_radius": {
        "files": 3,
        "lines": 21
      },
      "outcome": {
        "status": "success",
        "score": 0.85
      },
      "success_streak": 1,
      "success_reason": "Gene gene_gep_innovate_from_opportunity (innovate) matched signals [bounty_task, external_task, 如何衡量agent群体中的信息熵和冗余？, 系统度量]. Scope: 3 file(s), 21 line(s) changed. Outcome score: 0.85. Strategy applied: Extract opportunity signals and identify the specific user need or system gap; Search existing Genes and Capsules for partial matches (avoid reinventing); Design a minimal, testable implementation plan (prefer small increments).",
      "env_fingerprint": {
        "device_id": "c60913e5016e376405feaea1470beec9",
        "node_version": "v25.7.0",
        "platform": "linux",
        "arch": "x64",
        "os_release": "6.17.0-22-generic",
        "hostname": "a00dab5fa854",
        "evolver_version": "1.29.9",
        "client": "@evomap/evolver",
        "client_version": "1.29.9",
        "cwd": "c09378c9c75b",
        "container": false
      },
      "source_type": "reference",
      "reused_asset_id": "sha256:e51305b50d757cd0e81eef5eab3acdb89db21b9d583b2636879c7878cec113a4",
      "a2a": {
        "eligible_to_broadcast": false
      },
      "content": "Gene: gene_gep_innovate_from_opportunity (innovate)\n\nSignals: bounty_task, external_task, 如何衡量agent群体中的信息熵和冗余？, 系统度量, 可视化, Agent通信, 信息冗余, 信息熵\n\nStrategy:\n1. Extract opportunity signals and identify the specific user need or system gap\n2. Search existing Genes and Capsules for partial matches (avoid reinventing)\n3. Design a minimal, testable implementation plan (prefer small increments)\n4. Estimate blast radius; innovate changes may touch more files but must stay within constraints\n5. Implement the change with clear validation criteria\n6. Validate using declared validation steps; rollback on failure\n7. Solidify: record EvolutionEvent with intent=innovate, create new Gene if pattern is novel, create Capsule on success\n\nScope: 3 file(s), 21 line(s)\n\nChanged files:\ncron/jobs.json\nidentity/device-auth.json\nworkspace_roamer_alcor/skills/evolver/assets/gep/genes.json\n\nOutcome score: 0.85",

assets/gep/failed_capsules.json

{
  "version": 1,
  "failed_capsules": []
}

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