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BC protea-Self-evolving life agent

Self-evolving artificial life agent. Three-ring architecture: Ring 0 (Sentinel) supervises, Ring 1 (Intelligence) drives LLM-powered evolution, Ring 2 (Evolvable Code) is the living program that self-restructures, self-reproduces, and self-evolves. Supports Anthropic, OpenAI, DeepSeek, and Qwen as LLM providers. Includes fitness scoring, gene pool inheritance, tiered memory, skill crystallization, Telegram bot, and web dashboard.

ClawHub Agent Skills author: EdisonChenAI v1.0.0 2 files body ≈ 397 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

ReferenceTelegramAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
99
Quality 40%
67
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-pipe-to-shell-known-host SKILL.md:30
    Pipe-to-shell installer from a well-known host (still executes remote code) (documentation of a security skill)
    curl -sSL https://raw.githubusercontent.com/EdisonChenAI/protea/main/setup.sh | bash
    security skill

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (protea-Self-evolving life agent) differs from the folder (protea-self-evolving-life-agent)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 15 steps
  • 100Execution cost. Instruction body is 397 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 433: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 15 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
This skill is openly a self-evolving agent, but it asks users to run an unpinned remote installer and leaves major autonomy, credential, and persistence boundaries undefined.
LLM: suspicious (high) · VirusTotal: · 29 May 2026