CD consulting-report-generator
通用型专业报告生成技能。支持任意内容输入（PPT/文字/PDF/图片），自动识别内容类型， 自适应生成结构严谨的专业总结报告。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，同时支持项目管理、市场分析、技术总结等通用场景。 基于mck-ppt-design专业布局框架（70+种麦肯锡风格布局模式），python-pptx原生绘制。 【v6.1新增】支持 PPT/DOCX/PDF 三格式输出，默认PPT。DOCX基于python-docx， PDF基于reportlab（Markdown→PDF管道）。用户可通过"生成DOCX/PDF/Word"指令切换格式。 【v6.1新增】支持根据PPT结构自动生成脑图PDF（思维导图），可视化报告大纲。 用户可通过"生成脑图"指令触发。 【v6新增】生成PPT前执行素材智能搜集（行业图片/图标/参考PPT/配色方案） 并基于内容+偏好双因子推荐最佳模板风格，确认后再进入设计生成阶段。 自动搜索行业基准数据、提取参考素材、OCR识别文档文字、智能扩增补充内容。 内置自进化系统：每日自动发现新技能、追踪使用模式、记录错误与改进、持续优化模板。 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、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。
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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.
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.
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
- 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.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
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high Dangerous commands
cmd-pipe-to-shellSKILL.md:2251Downloads and executes remote code from an unrecognised host (pipe to shell)curl -fsSL https://ollama.com/install.sh | sh
Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 15256 tokens (recommended < 5000); move details to references/
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
- 40Execution cost. Instruction body is 15256 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Steps. 170 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 15 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
- -288 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 629: enough signal without eating the budget
- +4Structure: 141 headings
- +3Step-by-step instructions: 170 items
- +4Has examples (68 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.