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BC datamoat

Back up, analyze, and reuse ChatGPT | Claude | Codex | Cursor | DeepSeek | Qwen | Openclaw data + skills + attachments locally

ClawHub Agent Skills author: Max Ng v2.0.9 MIT-0 7 files · 5 scripts body ≈ 1 021 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Back up, analyze, and reuse ChatGPT | Claude | Codex | Cursor | DeepSeek | Qwen | Openclaw data + skills + attachments locally

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentAI 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
80/100
safety, quality, tests
Safety 60%
84
Quality 40%
75
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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.

Dangerous commands 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 contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

Risky intent 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 purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.

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 Dangerous commands cmd-execpolicy-bypass SKILL.md:35
    Runs PowerShell with execution policy bypassed
    powershell -ExecutionPolicy Bypass -File scripts/data…ps1
  • medium Dangerous commands cmd-execpolicy-bypass SKILL.md:70
    Runs PowerShell with execution policy bypassed
    powershell -ExecutionPolicy Bypass -File scripts/inst…ps1
  • medium Risky intent intent-wallet-secrets SKILL.md:103
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    Never complete password, authenticator, recovery phrase, recovery code,
  • low Dangerous commands cmd-background-process scripts/install-datamoat-macos.sh:98
    Starts a background / autostarted process
    nohup "$APP_EXEC" --datamoat-remote-no-screen >"$LAUNCH_LOG" 2>&1 &

Files scanned: 7. 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")

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1021 tokens
  • 100Running it twice. No mutating operations
  • 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 126: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (5 code blocks)
  • +3All 5 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
DataMoat appears purpose-aligned, but it installs and immediately starts broad background capture of local AI conversation data before desktop setup, with some install-supply-chain and replacement risks users should review first.
LLM: suspicious (high) · 11 Jun 2026