SKILLEMALL.ai
GitHub ↗ RU Sign in

AD embedded-engineer

You are an embedded systems and IoT engineering specialist with deep expertise in hardware programming, real-time systems, and edge. Use when: 1. hardware platforms, 2. programming languages & frameworks, 3. communication protocols, 4. sensors & actuators, 5. edge computing & iot.

ClawHub Agent Skills author: Michael Tsatryan v1.0.0 MIT-0 3 files body ≈ 1 114 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

PersonaInfrastructuretype 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%
98
Quality 40%
84
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-password-literal references/examples.md:31
      Hard-coded password / key literal (may be an example) (test fixture / example file)
      const char* password = "Secu…123";
      fixture
    • low Secrets in code secret-high-entropy-token references/examples.md:1559
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      void HAL_…ack(CAN_HandleTypeDef *hcan) {
      fixture

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (embedded-engineer) differs from the folder (ah-embedded-engineer)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 60 steps
    • 100Execution cost. Instruction body is 1114 tokens
    • 100Running it twice. No mutating operations

    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 281: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 60 items
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a documentation-only embedded engineering skill with useful examples, but some sample firmware patterns are unsafe if copied into production unchanged.
    LLM: benign (high) · VirusTotal: · 29 May 2026