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BC stock-portfolio-advisor

分析A股自选与持仓，获取行情、财报、风险与新闻，按行业计算价值评分和独立交易环境分，输出基本面情景估值、数据缺口及受用户风险政策约束的配置参考，并生成离线HTML报告。适用于股票诊断、持仓复盘、投资价值评分和组合配置请求；不自动下单。

ClawHub Hermes author: HoLauv v0.1.0 MIT-0 30 files body ≈ 1 054 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

分析A股自选与持仓，获取行情、财报、风险与新闻，按行业计算价值评分和独立交易环境分，输出基本面情景估值、数据缺口及受用户风险政策约束的配置参考，并生成离线HTML报告。适用于股票诊断、持仓复盘、投资价值评分和组合配置请求；不自动下单。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 29. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 117 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1054 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)
  • +3Description length 117: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -38 of 13 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
The skill fits a stock-analysis use case, but it should be reviewed because it auto-runs an external npm data tool and has a local snapshot-write bug that could overwrite portfolio JSON files.
LLM: suspicious (high) · 7 Sept 2026