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AB claude-managed-agents

Manage Claude Managed Agents end to end through a Python helper CLI, with ant CLI equivalents documented as a secondary path. Use this whenever the user wants to create, update, list, archive, or inspect Claude Managed Agents agents, environments, sessions, or event streams; configure built-in tools, MCP servers, skills, packages, or networking; send session messages, interrupts, confirmations, or custom tool results; or work with Anthropic's managed-agents beta lifecycle from this machine.

ClawHub Agent Skills author: Aaron v1.0.0 MIT-0 10 files body ≈ 2 078 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

IntegrationAI 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
68/100
Nearly there
Progress reporting w 2
0
Inputs and preconditions w 11
30
Running it twice w 4
30
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: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 26 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 4 branches
    • 100Steps. 85 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2078 tokens
    • low 10 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 495: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 85 items
    • +3Output format is stated explicitly
    • +4Has examples (16 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: clean
    This is a powerful but coherent Claude Managed Agents administration skill whose sensitive actions are disclosed and user-directed.
    LLM: benign (high) · VirusTotal: · 29 May 2026