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DF attribuly-dtc-analyst

A comprehensive AI marketing partner for DTC ecommerce. Combines multiple diagnostic and optimization skills powered by Attribuly first-party data.

ClawHub Agent Skills author: Alex@Attribuly v2026.4.8 MIT-0 17 files body ≈ 4 216 tokens Open the sourceclawhub.ai↗ analyzed 11 d ago

A comprehensive AI marketing partner for DTC ecommerce.

As a process F 26/100 · No process to follow — References files that are not bundled: references/[skill-name].md

AnalyzerShopifyData 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
D
65/100
safety, quality, tests
Safety 60%
65
Quality 40%
65
Run on models
none yet
Process rating
F
26/100
No process to follow
References files that are not bundled: references/[skill-name].md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.

Broad scope 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 asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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 · 7

✓ No critical or high findings

Medium and low: 7
  • medium Dangerous commands cmd-shell-rc README.ja.md:142
    Writes to a shell startup file
    echo 'export ATTRIBULY_API_KEY="att_your_actual_key"' >> ~/.bashrc
  • medium Dangerous commands cmd-shell-rc README.ja.md:149
    Writes to a shell startup file
    echo 'export ATTRIBULY_API_KEY="att_your_actual_key"' >> ~/.zshrc
  • medium Dangerous commands cmd-shell-rc README.md:187
    Writes to a shell startup file
    echo 'export ATTRIBULY_API_KEY="att_your_actual_key"' >> ~/.bashrc
  • medium Dangerous commands cmd-shell-rc README.md:194
    Writes to a shell startup file
    echo 'export ATTRIBULY_API_KEY="att_your_actual_key"' >> ~/.zshrc
  • medium Dangerous commands cmd-shell-rc README.zh-CN.md:142
    Writes to a shell startup file
    echo 'export ATTRIBULY_API_KEY="att_your_actual_key"' >> ~/.bashrc
  • medium Dangerous commands cmd-shell-rc README.zh-CN.md:149
    Writes to a shell startup file
    echo 'export ATTRIBULY_API_KEY="att_your_actual_key"' >> ~/.zshrc
  • medium Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: ATTRIBULY_API_KEY — verify each one is needed for the stated purpose
    required_environment_variables: ATTRIBULY_API_KEY

Files scanned: 17. 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")
  • warning missing-ref reference to a missing file: references/[skill-name].md
  • note frontmatter-key unknown frontmatter key "env"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "source"

Process rating: all ten parameters 26/100

Will not run. References files that are not bundled: references/[skill-name].md
  • 0Tools and files. 1 referenced file(s) missing: references/[skill-name].md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 9 mutating operations with no state check
  • 40Consistency. Frontmatter name (attribuly-dtc-analyst) differs from the folder (attribuly)
  • 70Execution cost. Instruction body is 4216 tokens
  • 100Steps. 56 steps
  • low 10 top-level sections: this looks like several domains in one skill

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
  • -213 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 147: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (12 of 12)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: suspicious
This appears to be a legitimate Attribuly marketing analytics skill, but it needs review because it combines sensitive ad/store data access with broad automatic triggers, raw Google/Meta query paths, and weak API-key handling guidance.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026