SKILLEMALL.ai

AC agent-optimize

Agent 優化診斷技能。分析 OpenClaw 運行狀態,識別信息過載、上下文堆積、技能噪音等問題。 提供系統級優化方案,實現瘦身提速,解決"貴慢亂"困擾。 Use when: (1) Agent 響應變慢, (2) 上下文過長導致效率低下, (3) 技能加載過多造成噪音, (4) 需要診斷性能瓶頸, (5) 需要優化建議報告, (6) 定期健康檢查, (7) 模型使用成本過高, (8) 技能衝突或冗餘。 Triggers: "優化 Agent", "診斷性能", "信息過載", "context overload", "optimize agent", "agent health", "performance audit", "skill audit", "context cleanup", "system optimization", "agent slowdown", "too many skills", "context too long"。

ClawHub Agent Skills author: chungvic v1.0.0 MIT-0 4 files body ≈ 1 527 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
53/100
Has gaps
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

    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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "permissions"

    Process rating: all ten parameters 53/100

    • 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
    • 100Tools and files. No external tools needed
    • 100Steps. 59 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1527 tokens
    • 100Running it twice. No mutating operations
    • low 13 top-level sections: this looks like several domains in one skill

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 11 example trigger phrases
    • +3Description length 432: enough signal without eating the budget
    • +4Structure: 46 headings
    • +3Step-by-step instructions: 59 items
    • +4Has examples (21 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    This is a plausible OpenClaw performance diagnostic skill, but it asks users to read and potentially modify sensitive local agent state with weak boundaries.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026