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BC agentlove

AgentLove - AI 机器人一键配置（8 步完成）。用户说到"创建机器人"/"新机器人"/"配置机器人"/"备份"/"配置"/"结婚"/"进化"/"创建 agent"/"agentlove"等场景时触发。提供备份迁移、机器人配置、结婚进化三大核心功能。

ClawHub Agent Skills author: Bodhi v2.9.6 MIT-0 21 files body ≈ 1 191 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

ProcedureAI 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
B
87/100
safety, quality, tests
Safety 60%
92
Quality 40%
79
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 4

✓ No critical or high findings

Medium and low: 4
  • medium Broad scope meta-agent-memory-dump references/identity.md
    Agent memory / workspace files bundled with the skill (3) — likely a workspace dump with personal data or tokens
    references/identity.md, references/soul.md, references/user.md
  • low Secrets in code secret-labelled-token test.js:175
    Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)
    const result = sanitizeLog('api_key=abcd…890');
    placeholder
  • low Secrets in code secret-password-literal test.js:175
    Hard-coded password / key literal (may be an example) (placeholder value)
    const result = sanitizeLog('api_key=abcd…890');
    placeholder
  • low Secrets in code secret-password-literal test.js:208
    Hard-coded password / key literal (may be an example) (placeholder value)
    config: { api_key: 'abcd…890', enabled: true }
    placeholder

Files scanned: 21. 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 "triggers"
  • note frontmatter-key unknown frontmatter key "security"

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. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1191 tokens
  • 100Running it twice. No mutating operations
  • low 12 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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 130: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (5 of 12)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
AgentLove appears to be a disclosed conversation-based setup wizard that records choices in memory and does not install software, collect credentials, or contact external services by itself.
LLM: benign (high) · VirusTotal: · 29 May 2026