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BD integrated-manufacturing-consulting

制造咨询全栈技能——以附件为素材，按5Part标准结构生成面向客户企业高层汇报的 正式咨询项目总结报告（非简单摘要）。整合四大模块：（1）7大部门调研方法论+ODP-I²诊断框架+改善项目定义（原manufacturing-consulting-toolkit）； （2）原PPT图片提取复用+python-pptx流程图/架构图绘制+麦肯锡风格设计引擎（原manufacturing-consulting-ppt）； （3）PPT/DOCX/PDF多格式输出+素材智能搜集+风格双因子选择+脑图PDF+S13自修复+S14微信交付（原consulting-report-generator）； （4）艾瑞咨询(iResearch)+QuestMobile行业报告搜索与问答（原consulting-report-search）。 覆盖精益生产、智能制造、计划物控（PC&MC）、数字化转型、AI与工业智能五大领域。 内置自进化系统，支持本地/离线模式，采用微软雅黑字体（Mac兼容），无BLOCK_ARC。 触发词：生成PPT、做报告、项目总结、阶段性总结、咨询报告、调研诊断、 生成总结报告、把这份资料做成PPT、根据这个内容生成报告。用户上传任何内容时， 自动触发此技能进行专业报告生成。

Not recommendedcritical or high security findings
ClawHub Agent Skills author: EricShao2025 v7.3.2 MIT-0 34 files body ≈ 18 805 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

IntegrationWordPowerPointManufacturingData and analyticsSoftware developmenttype 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
75/100
safety, quality, tests
Safety 60%
82
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1

  • high Dangerous commands cmd-pipe-to-shell SKILL.md:2680
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://ollama.com/install.sh | sh

Files scanned: 34. 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")
  • warning body-long SKILL.md body ≈ 18805 tokens (recommended < 5000); move details to references/

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
  • 10Execution cost. Instruction body is 18805 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 213 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 17 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
  • -2localhost URLs: will not work for another user
  • -2163 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 547: enough signal without eating the budget
  • +4Structure: 157 headings
  • +3Step-by-step instructions: 213 items
  • +4Has examples (81 code blocks)
  • +4Reference files are cited in the instructions (14 of 14)
  • +3All 2 scripts are documented

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

External checks

ClawHub: suspicious
The skill can generate manufacturing consulting reports, but it also automatically processes uploads, scans other local skills, rewrites its own instructions, stores logs, and pushes files externally without enough user control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026