BD stockClaw-yingyan
为该股票量化项目提供 OpenClaw 接入说明，支持股票量化图生成、股票行情问答、自然语言 AI 搜股与 WebSocket 实时监控信号推送。凭证须从与本 Skill 同目录的 config.json 中 openclawCredentials 读取：user_id 与 openClaw_api_key 为必填（用于所有需鉴权的 HTTP 接口，请求体/查询参数中字段名仍为 apikey）；monitor_api_key 为选填，仅 WebSocket 实时监控需要，不订阅监控可不配置。首次配置或用户在对话中修改后写回 config.json；禁止在配置已有效时重复索要；用户也可直接编辑 config.json 修改凭证。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 1 more place: ClawHub
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 4
✓ No critical or high findings
Medium and low: 4
-
low Secrets in code
secret-labelled-tokenexamples.md:157Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)wss://yingyan.chatface.com/ws/monitor/open?apikey=YOUR…KEY&user_id=68b2671825
placeholder -
low Secrets in code
secret-labelled-tokenexamples.md:209Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)wss://yingyan.chatface.com/ws/monitor/open?apikey=YOUR…KEY&user_id=68b2…ae5
placeholder -
low Secrets in code
secret-labelled-tokenreference.md:308Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)GET /api/openclaw/stock/limit-up?user_id=your_user_id&apikey=YOUR…KEY
placeholder -
low Secrets in code
secret-labelled-tokenSKILL.md:171Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)GET /api/openclaw/stock/limit-up?user_id=your_user_id&apikey=YOUR…KEY
placeholder
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
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
- 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
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 109 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2169 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
- +1No license
- +2Single-language instructions
- +3Description length 317: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 109 items
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.