BD iflow-nb
iflow 知识库助手（iflow知识库），支持知识库管理、文件上传/URL导入、内容生成、联网搜索并导入知识库。 当用户提到知识库、资料库、收藏文章、保存链接、上传文件、导入网页、 生成报告、生成PPT、生成播客、生成思维导图、生成视频、分享知识库、 查看生成进度、搜论文并整理、查文献并生成报告、深度研究、搜索网页并存到知识库时，使用此 skill。 即使用户没有明确说"知识库"或"报告"，只要意图涉及文章收藏、知识整理、内容生成、 知识分享、日常记录（如"帮我记一下这篇文章""做个PPT""分享给同事""深度研究一下" "记一下今天吃饭50元""帮我记个笔记""存一下这个信息"），也应触发此 skill。 注意：如果用户只是想快速了解某个问题（如"XX是什么""帮我查一下XX"）而不涉及知识库存储或内容生成， 不应触发此 skill，Agent 应使用自身的搜索能力直接回答。
As a process D 47/100 · 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
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 Exfiltration
net-redirectable-api-keyscripts/iflow_common.py:29Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
Files scanned: 21. 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")
Process rating: all ten parameters 47/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70Execution cost. Instruction body is 4714 tokens
- 100Steps. 50 steps
- 100Consistency. Name and required fields are in place
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -31 of 7 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 395: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 50 items
- +4Has examples (21 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.