SKILLEMALL.ai

BD meta-evolver

递归自进化元引擎。在单技能学习器(skill-self-improve)之上,构建「全局能力感知 → 自定策略 → 自找资源 → 自改自身 → 元反思」的循环,使技能生态持续自我增强、逼近并超越一线大模型能力。 当希望让 agent 长期自主迭代、自动发现并填补能力缺口、自己制定进化路线时使用。

ClawHub Agent Skills author: qq435912743 v1.0.14 MIT-0 8 files body ≈ 409 tokens Open the sourceclawhub.ai analyzed 35 h ago

递归自进化元引擎。在单技能学习器(skill-self-improve)之上,构建「全局能力感知 → 自定策略 → 自找资源 → 自改自身 → 元反思」的循环,使技能生态持续自我增强、逼近并超越一线大模型能力。 当希望让 agent 长期自主迭代、自动发现并填补能力缺口、自己制定进化路线时使用。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 0

✓ No critical or high findings

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "visibility"

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 409 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
  • -35 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 148: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 13 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 openly aims to self-improve and manage other skills, but it can scan local folders and rewrite many installed skills without strong user approval controls.
LLM: suspicious (high) · 14 Aug 2026