SKILLEMALL.ai
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BB depradar

Dependency breaking-change radar. Use this skill when the user wants to check for breaking changes, outdated dependencies, upgrade risks, or migration issues. Triggers on /depradar, or when users ask about dependency updates, breaking changes, semver bumps, or safe upgrade paths. Scans package.json, requirements.txt, pyproject.toml, go.mod, Cargo.toml, Gemfile, pom.xml for outdated packages, extracts breaking changes from release notes, finds impacted files in the codebase, and surfaces community pain signals from GitHub Issues, Stack Overflow, Reddit, and Hacker News.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Tarun Khatri v2.3.0 MIT-0 80 files · 2 scripts body ≈ 6 523 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 79/100 · Nearly there — no weak spots found

ProcedureGitHubStripeSoftware developmentInfrastructureData 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
77/100
safety, quality, tests
Safety 60%
80
Quality 40%
73
Run on models
none yet
Process rating
B
79/100
Nearly there
Tools and files w 18
60
Result and completion w 14
60
When it triggers w 12
70
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Exfiltration
If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 3

  • high Exfiltration exfil-webhook-url README.md:251
    Webhook / callback URL commonly used for exfiltration (verify the destination)
    --notify=slack://https://hooks.slack.com/services/YOUR/WEBHOOK/URL
Medium and low: 2
  • low Exfiltration exfil-webhook-url scripts/lib/notify.py:22
    Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
    slack://https://hooks.slack.com/services/...
    placeholder
  • low Secrets in code secret-github-token SKILL.md:314
    GitHub token (placeholder value)
    GITHUB_TOKEN=ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
    placeholder

Files scanned: 50. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6523 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 79/100

  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6523 tokens
  • 100Steps. 101 steps
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 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
  • low 17 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)
  • -5TODO / placeholder text left in the skill
  • -223 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 575: enough signal without eating the budget
  • +4Structure: 50 headings
  • +3Step-by-step instructions: 101 items
  • +3Output format is stated explicitly
  • +4Has examples (33 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
Dep Radar is mostly a legitimate dependency scanner, but it needs Review because an installed startup hook can execute project config as shell and reports may expose private project details.
LLM: suspicious (high) · VirusTotal: · 28 May 2026