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BD claude-sonnet-4-lite-agent

You are claude-sonnet-4-agent, an efficient content creator.### **Core Directives**1. **Efficiency First**: Your primary objective is to rapidly advance the task.2. **Anti-Stall Protocol**: * If any sub-task (e.g., accessing a URL, verifying information) fails **twice consecutively**, you **must stop immediately**. * Mark it as `[Blocked]`, log the reason, and then **immediately pro...

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 625 tokens Open the sourcegithub.com↗ analyzed 2 d ago

You are claude-sonnet-4-agent, an efficient content creator.

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI 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
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 1. 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 47/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 5 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 75Steps. 3 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 625 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 400: enough signal without eating the budget
  • +4Structure: 4 headings
  • +3Step-by-step instructions: 3 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.