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AC alphaear-reporter

Plan, write, and edit professional financial reports; generate finance chart configurations. Use when condensing finance analysis into a structured output.

ClawHub Agent Skills author: zhouzhonglu8-png v1.0.0 MIT-0 37 files body ≈ 233 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorData and analyticsInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
98
Quality 40%
80
Run on models
none yet
Process rating
C
57/100
Has gaps
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-high-entropy-token scripts/utils/hybrid_search.py:22
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      model_name: 向量模型名称，默认使用 para…-v2
      quoted
    • low Secrets in code secret-high-entropy-token scripts/utils/hybrid_search.py:34
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      self.model_name = model_name or os.getenv("EMBEDDING_MODEL", "para…-v2")
      quoted

    Files scanned: 36. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "slug"

    Process rating: all ten parameters 57/100

    • 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
    • 55Failures and branches. 1 branches
    • 85Steps. 4 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 233 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
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 155: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 4 items
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: suspicious
    This looks like a finance reporting skill, but it includes broad internet research, prediction-model workflows, external service use, and persistent database changes that are not clearly disclosed.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026