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AB edith-augment-code

Use Augment Code (Auggie CLI) to analyze, generate, and modify code through Edith smart glasses or OpenClaw. Triggers when the user asks to build, code, debug, analyze, or prototype something.

ClawHub Agent Skills author: samdickson22 v1.0.0 MIT-0 3 files body ≈ 552 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 78/100 · Nearly there — weak spots: running it twice

AnalyzerSoftware developmenttype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
78/100
Nearly there
Running it twice w 4
30
Tools and files w 18
60
Result and completion w 14
60
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 · 0

    ✓ No critical or high findings

    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 78/100

    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) 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
    • 100Steps. 16 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 552 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)
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 192: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 16 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: clean
    This is a transparent wrapper for the Auggie coding CLI; it has normal external coding-agent risks but no evidence of hidden or malicious behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026