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BD 深知可信投研（上市公司研究+政策标准洞察）

当用户需要上市公司研究、公司基本面分析、财报数据解读、行业对比、投资研究、政策对股票/行业的影响分析、补贴税收优惠核验、行业标准与准入门槛查询，或明确要求'研究一下某公司''这家公司怎么样''政策对它有什么影响''值不值得投、大概什么价位值得关注'等投研任务时，使用深知可信投研。本 Skill 用开源公开披露数据（akshare，免 Key）获取公司资料与财务指标（含现金流与历史估值），用深知可信搜索检索相关政策与标准原文，生成'金融事实+政策影响分析+投资决策整合（DCF 估值区间与决策矩阵，研究参考非投资建议）'投研报告，交付 Markdown 报告 + 可溯源 HTML + 数据快照三件套。深知检索能力通过环境变量 DKNOWC_API_KEY 注入，未开通时金融数据部分仍可用。

ClawHub Agent Skills author: DKnownAI v1.1.0 MIT-0 21 files body ≈ 1 192 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
B
82/100
safety, quality, tests
Safety 60%
94
Quality 40%
65
Run on models
none yet
Process rating
D
41/100
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 · 6

✓ No critical or high findings

Medium and low: 6
  • low Secrets in code secret-high-entropy-token reference/兴通股份_报告.data.json:316
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "id": "2026…zpg",
    quoted
  • low Secrets in code secret-high-entropy-token reference/兴通股份_报告.data.json:535
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "源网址": "https://flk.npc.gov.cn/./detail2.html?ZmY4…MDY%3D",
    quoted
  • low Secrets in code secret-high-entropy-token reference/兴通股份_报告.data.json:656
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "sourceUrl": "https://flk.npc.gov.cn/./detail2.html?ZmY4…MDY%3D",
    detector
  • low Secrets in code secret-high-entropy-token reference/兴通股份_报告.data.json:684
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "id": "2026…q0Z",
    quoted
  • low Secrets in code secret-high-entropy-token reference/比亚迪_报告.data.json:83
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "id": "2026…fzv",
    quoted
  • low Secrets in code secret-high-entropy-token reference/比亚迪_报告.data.json:466
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "id": "2026…4P7",
    quoted

Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "permissions"
  • note frontmatter-key unknown frontmatter key "secrets"

Process rating: all ten parameters 41/100

  • 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
  • 40Consistency. Frontmatter name (深知可信投研（上市公司研究+政策标准洞察）) differs from the folder (dknowc-trusted-investment-research)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 39 steps
  • 100Execution cost. Instruction body is 1192 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
  • -32 of 12 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 347: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: clean
This is a disclosed investment-research report skill that uses expected public finance data and optional policy-search registration, with some reliability and credential-handling caveats.
LLM: benign (high) · VirusTotal: · 3 Sept 2026