SKILLEMALL.ai
GitHub ↗ RU Sign in

AD daymade-sector-research

A股行业投研工作流：全板块 Top N 涨幅标的计算、公告窗口检索（周/月）、市场情绪证据分级判断，并以 Agent Team 并行执行 + fresh-context 对抗验证。 触发场景（命中任一即读本 skill）： - 「XX 行业 Top N / Top 10 标的」（如「医药行业今天的 Top 10 标的」） - 「这些标的最近一周 / 一个月发过哪些公告」 - 「判断 XX 行业 / 板块现在的市场情绪怎么样」 - 「用 Agent Team / 多 agent 做行业投研」或要求对行业研究结论做对抗性验证 - 需要「基于证据、基于数据」的板块级研究交付（成分股 → 行情 → 公告 → 情绪 → 验证 → 报告） 核心能力：东财 push2 成分股 → 新浪实时快照涨幅排序（scripts/top_n_pipeline.py）；巨潮 cninfo + 东财公告双源交叉，沪深+北交所统一通道（scripts/ann_query.py）；情绪证据 L1（一手行情）/L2（带时间戳媒体）/L3（未核实标题）分级；Agent Team 并行编排与验证纪律。全程国内公开数据源，无付费依赖；Gangtise 官方 skill 为可选增强（需积分，缺失时走公开源 pivot）。 最高纪律（用户原话）：没有十足把握的事，宁可标注不确定，不可给错误答案。

daymade/claude-code-skills Agent Skills author: daymade 8 files body ≈ 1 020 tokens Open the sourcegithub.com↗ analyzed 2 h ago

A股行业投研工作流：全板块 Top N 涨幅标的计算、公告窗口检索（周/月）、市场情绪证据分级判断，并以 Agent Team 并行执行 + fresh-context 对抗验证。 触发场景（命中任一即读本 skill）： - 「XX 行业 Top N / Top 10 标的」（如「医药行业今天的 Top 10…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
D
43/100
Unfinished process
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.
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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 43/100

  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1020 tokens

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 587: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.