CD multi-project-quote-board
多项目（同批供应商、同一询价口径）报价对比看板生成器，输出「中英双语可切换的自包含 HTML 看板 + 结构一致的 Excel」。适用于一次询价覆盖 2 个及以上子项目（如葡萄牙 Worten Chafiras & Leiria 两个店、西班牙多处点位），需同时呈现回标状态、逐项目明细、工期门槛判定、条款对比、待澄清清单、分项目价格排序与「满足工期前提下价格最低优先」的定标建议，且**明确不做跨项目合计**的场景。数据层与渲染层严格分离，HTML 与 Excel 共用同一份数据层，口径永不发叉。
多项目（同批供应商、同一询价口径）报价对比看板生成器，输出「中英双语可切换的自包含 HTML 看板 + 结构一致的 Excel」。适用于一次询价覆盖 2 个及以上子项目（如葡萄牙 Worten Chafiras & Leiria…
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
✓ No critical or high findings
Medium and low: 1
-
medium Dangerous commands
cmd-eval-dynamicscripts/gen_excel.py:25Dynamic code execution from decoded/untrusted inputexec(compile(_data_src, "gen_board_data", "exec"), ns)
Files scanned: 4. 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") - note
frontmatter-keyunknown frontmatter key "agent_created"
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 (multi-project-quote-board) differs from the folder (quotationalyasize)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 21 steps
- 100Execution cost. Instruction body is 1935 tokens
- 100Running it twice. No mutating operations
- low 11 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
- +1No license
- +2Single-language instructions
- +3Description length 250: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 21 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.