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AA agent-analytics-autoresearch

Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.

ClawHub Agent Skills author: Danny Shmueli v1.0.9 MIT-0 8 files · 2 scripts body ≈ 1 677 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process A 81/100 · Runs to the end — weak spots: running it twice

IntegrationGitHubData and analyticsAI 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
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
A
81/100
Runs to the end
Running it twice w 4
30
Result and completion w 14
60
Failures and branches w 10
65
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: 8. 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 "repository"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "provides"

Process rating: all ten parameters 81/100

  • 30Running it twice. 4 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 65Failures and branches. 3 branches
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 52 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1677 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)
  • +2Single-language instructions
  • +3Description length 348: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 52 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This skill is a disclosed, review-first analytics workflow that reads analytics data and writes local experiment-planning artifacts without hidden or automatic production changes.
LLM: benign (high) · VirusTotal: · 29 May 2026