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BD tender-bid-decision-analysis

投标决策分析助手。当用户给出一个具体的招标项目（公告链接/项目标题/招标文件），并希望进行投标决策相关分析时，必须使用此SKILL：该不该投/值不值得投、投标决策、标前分析、竞争对手预测（谁会来投标）、中标概率评估、报价参考/建议报价、采购方倾向分析（有没有关系户/控标信号）、废标风险评估。基于全网招中标历史数据输出带结论的结构化决策报告，另附可分享的HTML版。即使用户没有提到「投标决策」，只要涉及某个具体标该不该投、投标评估、标前调查、竞争分析、报价参考等需求，都应使用本SKILL。

ClawHub Agent Skills v1.0.4 7 files body ≈ 1 596 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerProcurementtype 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
88/100
safety, quality, tests
Safety 60%
97
Quality 40%
75
Run on models
none yet
Process rating
D
46/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: tender-bid-decision-analysis (ClawHub)

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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Exfiltration net-credential-use references/auto-register.md:195
    Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
    **如果当前 api_key 来自 `$ZLBX_API_KEY`**：跳过 SID 流程，提示用户访问 `https://ai.zhiliaobiaoxun.com/?ch=s72` 手动登录充值。
    quoted
  • low Obfuscation obf-base64-blob scripts/render_report.py:71
    Long base64-looking blob (quoted — discussed, not commanded)
    _LOGO_B64 = "iVBO…B5x
    quoted
  • low Secrets in code secret-high-entropy-token scripts/render_report.py:71
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    _LOGO_B64 = "iVBO…B5x
    quoted

Files scanned: 7. 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 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. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1596 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 246: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
The skill’s bid-analysis purpose is real, but it needs review because it collects a stable device fingerprint, stores API credentials, preserves signed access links, and generates local HTML reports with a security flaw.
LLM: suspicious (high)