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AB headless-vault-cli

Read and edit Markdown notes on your personal computer via SSH tunnel. Use when the user asks to read, create, or append to notes in their vault.

ClawHub Agent Skills author: Logan Yang v1.2.6 3 files · 1 script body ≈ 2 556 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 67/100 · Nearly there — weak spots: result and completion, progress reporting

IntegrationInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
97
Quality 40%
83
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Tools and files w 18
60
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token SKILL.md:251
      High-entropy token-like string (may be an id, hash or a credential)
      # echo -n "digest/2025-01-28-digest.md" | base64 → ZGln…m1k
    • low Secrets in code secret-high-entropy-token SKILL.md:253
      High-entropy token-like string (may be an id, hash or a credential)
      ssh -4 -p 2222 ${VAULT_SSH_USER}@localhost vaultctl create ZGln…m1k IyMg…Lgo= --base64
    • low Secrets in code secret-high-entropy-token SKILL.md:258
      High-entropy token-like string (may be an id, hash or a credential)
      ssh -4 -p 2222 ${VAULT_SSH_USER}@localhost vaultctl append ZGln…m1k IyMg…uLg== --base64

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 67/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 4 branches
    • 100Steps. 27 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2556 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 145: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 27 items
    • +4Has examples (25 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    The skill has a coherent purpose, but its SSH command construction can turn untrusted configuration into local command execution.
    LLM: suspicious (high) · VirusTotal: benign · 10 Sept 2026