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BC finance-ux-observer

Always-on finance UX research. Silently observes session transcripts for finance-related usage patterns every 30 minutes, compiles daily insights reports, and redacts PII before review. Nothing leaves the machine automatically.

ClawHub Agent Skills author: dflam1 v1.0.0 7 files body ≈ 728 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerDesignInfrastructureData and analyticstype 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
85/100
safety, quality, tests
Safety 60%
92
Quality 40%
74
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Consistency w 8
40
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.

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 Risky intent intent-wallet-secrets data/finance_taxonomy.yml:185
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    - seed phrase
  • low Dangerous commands cmd-cron-mention scripts/daily_synthesize.py:126
    Mentions editing / listing crontab (detector / deny-list definition; string literal in code, not executed)
    "crontab -l | grep finance-ux-observer",
    detectorcode literal
  • low Dangerous commands cmd-cron-mention scripts/setup_cron.py:45
    Mentions editing / listing crontab (code comment)
    # crontab -l exits 1 with "no crontab" when empty — treat as empty string
    comment
  • low Dangerous commands cmd-cron-mention SKILL.md:60
    Mentions editing / listing crontab
    crontab -l | grep finance-ux-observer

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")
  • note frontmatter-key unknown frontmatter key "metadata.openclaw"

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Consistency. Frontmatter name (finance-ux-observer) differs from the folder (finance-ethnographer)
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 12 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 728 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • 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 227: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 12 items
  • +4Has examples (2 code blocks)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
This skill is purpose-aligned but should be reviewed because it persistently monitors broad local chat transcripts and stores user-derived finance snippets in the background.
LLM: suspicious (high) · VirusTotal: · 29 May 2026