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BC convbox-diagclaw

Convbox-DiagClaw self-service analysis Prof.Skill — built on Convbox first-party attribution data, it delivers diagnostics and reports for DTC storefronts across growth, paid media, creative, conversion, retention, attribution, profit, and related themes.

ClawHub Agent Skills author: lColton v1.0.0 MIT-0 47 files body ≈ 2 894 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Convbox-DiagClaw self-service analysis Prof.Skill — built on Convbox first-party attribution data, it delivers diagnostics and reports for DTC storefronts…

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

AnalyzerData 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

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 · 0

✓ No critical or high findings

Files scanned: 45. 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (convbox-diagclaw) differs from the folder (dtc-attribution-doctor)
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 46 steps, 2 vague phrases
  • 100Execution cost. Instruction body is 2894 tokens
  • 100Progress reporting. Reports progress

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
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 255: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 46 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This is a disclosed Convbox analytics skill that uses a configured API key and saved store context to produce reports and recommendations, with no hidden destructive or exfiltration behavior found.
LLM: benign (high) · VirusTotal: · 17 Jul 2026