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AB agent-runtime-impact-research

Research and analyze major Agent harness, runtime, coding-agent, skills, subagent, and plugin releases. Use when a model or platform launches an Agent execution layer and the user needs verified facts, architecture explanation, ecosystem impact, and implications for agent platforms and coding agents. Produces a fact table, runtime flow explanation, layered impact analysis, and a source register with judgment and evidence visibly separated. 中文摘要：研究 Agent 运行时、编码代理、Skills、插件与子代理的重大发布。产出经核实的 事实表、架构解析、分层影响分析与来源登记，判断与证据分离。触发词：Agent 运行时分析、 Agent 发布影响评估、Harness 架构解读、插件生态影响.

ClawHub Agent Skills author: haiyangchen v1.0.2 MIT-0 4 files body ≈ 1 922 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Research and analyze major Agent harness, runtime, coding-agent, skills, subagent, and plugin releases.

As a process B 70/100 · Nearly there — weak spots: inputs and preconditions, running it twice

AnalyzerAI 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
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
B
70/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
When it triggers w 12
50
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 "slug"
    • note frontmatter-key unknown frontmatter key "displayName"
    • note frontmatter-key unknown frontmatter key "description_zh"
    • note frontmatter-key unknown frontmatter key "description_en"
    • note frontmatter-key unknown frontmatter key "agent_created"
    • note frontmatter-key unknown frontmatter key "not_for"

    Process rating: all ten parameters 70/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 45 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1922 tokens
    • 100Progress reporting. Reports progress

    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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 572: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 45 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This is a transparent research-writing skill for analyzing agent runtime releases, with only ordinary web research expected and no hidden local access or persistence.
    LLM: benign (high) · VirusTotal: · 24 Aug 2026