CD npa-valuation-engine
法拍与不良资产全栈估值定价引擎：折价口径对齐、折价可修复性打分、三情景 IRR/MOIC、投标上限反推，并生成可审计的 xlsx 估值模型。触发词：法拍、不良资产、NPL、资产包、投标上限、折价率、IRR、MOIC、处置、尽调、债权转让、AMC、回收率、抵押物、个贷不良、对公不良。
法拍与不良资产全栈估值定价引擎：折价口径对齐、折价可修复性打分、三情景 IRR/MOIC、投标上限反推，并生成可审计的 xlsx 估值模型。触发词：法拍、不良资产、NPL、资产包、投标上限、折价率、IRR、MOIC、处置、尽调、债权转让、AMC、回收率、抵押物、个贷不良、对公不良。
As a process D 46/100 · 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.
- 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-install-from-urlSKILL.md:78Installs a package from an untrusted URL / archivepip install -i https://pypi.tuna.tsinghua.edu.cn/simple openpyxl numpy_financial
Files scanned: 39. 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 "display_name" - note
frontmatter-keyunknown frontmatter key "display_name_en" - note
frontmatter-keyunknown frontmatter key "description_zh" - note
frontmatter-keyunknown frontmatter key "description_en"
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 (python) that frontmatter does not declare
- 100Steps. 42 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2188 tokens
- 100Running it twice. No mutating operations
- low 12 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
- +2Single-language instructions
- +3Description length 141: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 42 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (27 of 27)
- +3All 6 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.