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BC topview-skill

Generate, Edit, Collaborate. Access all mainstream AI models in one toolkit. Simply describe your vision to create videos, images, and avatars—zero manual operations.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 31 files body ≈ 5 999 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate, Edit, Collaborate.

As a process C 58/100 · Has gaps — weak spots: result and completion, consistency, running it twice

GeneratorAI and agentsMedia and videotype 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
81/100
safety, quality, tests
Safety 60%
92
Quality 40%
64
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Running it twice w 4
30
Consistency w 8
40
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: openclaw-master-skills

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

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 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 · 4

✓ No critical or high findings

Medium and low: 4
  • medium Broad scope meta-agent-memory-dump references/user.md
    Agent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokens
    references/user.md
  • low Secrets in code secret-high-entropy-token references/avatar4.md:41
    High-entropy token-like string (may be an id, hash or a credential)
    --voice LaaH…tW6
  • low Secrets in code secret-password-literal scripts/auth.py:48
    Hard-coded password / key literal (may be an example)
    api_key = api_keys[0] if api_keys else ""
  • low Secrets in code secret-password-literal scripts/auth.py:135
    Hard-coded password / key literal (may be an example)
    api_key = api_keys[0] if api_keys else "(none)"

Files scanned: 31. 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 body-long SKILL.md body ≈ 5999 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 29 mutating operations with no state check
  • 40Consistency. Frontmatter name (topview-skill) differs from the folder (topviewskill)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5999 tokens
  • 85Steps. 81 steps, 2 vague phrases
  • 100Failures and branches. 6 branches, has a failure section
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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
  • -31 of 11 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 166: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 81 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (11 of 11)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.