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AC us-tech-giant-analyst

This skill should be used when a user asks to analyze a US-listed technology giant such as Apple, Microsoft, Nvidia, Amazon, Alphabet, Meta, or Tesla. It produces a deep, data-rich, and visually structured investment-and-competitive analysis with charts and valuation tables, covering business model, market share, growth prospects, technology moat, financial reports, and stock price trend. Trigger when the user asks to analyze, evaluate, compare, or profile a big-tech company's fundamentals, earnings, competitive position, or stock performance.

ClawHub Agent Skills author: TTGGJJ v1.0.1 MIT-0 3 files body ≈ 1 692 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

This skill should be used when a user asks to analyze a US-listed technology giant such as Apple, Microsoft, Nvidia, Amazon, Alphabet, Meta, or Tesla.

As a process C 62/100 · Has gaps — weak spots: inputs and preconditions, consistency, progress reporting

AnalyzerData and analyticsFinanceInfrastructuretype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Consistency w 8
40
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

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 62/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (us-tech-giant-analyst) differs from the folder (financialstatement)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 48 steps
    • 100Execution cost. Instruction body is 1692 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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 549: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 48 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This skill is a disclosed public-data research assistant for US tech-company analysis and does not request privileged access or hidden actions.
    LLM: benign (high) · VirusTotal: · 30 Aug 2026