SKILLEMALL.ai

BD self-play-coevolution

自我博弈对抗进化:让同一个智能体轮流扮演 proposer(提案者)与 critic(批判者),两边互相找茬、互相修补, 逐轮把提案质量与批判敏锐度一起推高,彼此越迭代越强。这是一线大模型几乎不具备、且决定能否可证明地变强的能力。 当用户要求自我博弈、对抗共进化、proposer/critic 闭环、越迭代越强、GAN 式自训练时使用。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 6 files body ≈ 530 tokens Open the sourceclawhub.ai analyzed 3 d ago

自我博弈对抗进化:让同一个智能体轮流扮演 proposer(提案者)与 critic(批判者),两边互相找茬、互相修补, 逐轮把提案质量与批判敏锐度一起推高,彼此越迭代越强。这是一线大模型几乎不具备、且决定能否可证明地变强的能力。 当用户要求自我博弈、对抗共进化、proposer/critic…

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

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
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: 6. 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 (bash) that frontmatter does not declare
  • 100Steps. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 530 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 169: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (2 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The skill’s core self-play demo is harmless, but it also adds persistent local learning and preference storage that is broader than its stated purpose and lacks clear consent or retention controls.
LLM: suspicious (high) · 14 Aug 2026