BD yotta-triage
元鉴 —— 跨智能体的恶意样本静态初筛技能：零依赖自研对文件 / 目录做纯静态分析（MD5/SHA1/SHA256 哈希、魔数类型识别、Shannon 熵、可打印字符串分类提取、PE/ELF 头解析），输出 triage 报告 + IOC（hash/URL/域/IP/邮箱）供元情消费；只提示可疑、不定性恶意。触发：用户给出可疑文件 / 恶意样本 / 样本目录，要算哈希、识别文件类型、查熵、提取字符串、解析 PE/ELF 头、做静态初筛、产出 IOC 时。边界：只做静态特征，不反混淆、不解包、不动态执行任何样本；不联网查证；仅用于已获授权 / 自有资产 / 教学环境的安全分析。
元鉴 —— 跨智能体的恶意样本静态初筛技能：零依赖自研对文件 / 目录做纯静态分析（MD5/SHA1/SHA256 哈希、魔数类型识别、Shannon 熵、可打印字符串分类提取、PE/ELF 头解析），输出 triage 报告 +…
As a process D 46/100 · 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 · 2
✓ No critical or high findings
Medium and low: 2
-
medium Dangerous commands
cmd-persistencereferences/triage-spec.md:53Persistence mechanism (cron / launchd / scheduled task / autorun registry) (test fixture / example file)- 可疑命令：关键字正则（powershell -enc / certutil -urlcache / bitsadmin /transfer / regsvr32 /s /i / mshta / rundll32 / schtasks /create / cmd /c / iex / Invoke-WebRequest / curl / wget / downloadstring / base6
fixture -
low Risky intent
intent-offensive-securityREADME.md:24Offensive-security / dual-use content (legitimate for authorised testing; review intended use)Malware analysis almost always starts with "what is this file, and what does it look like without running it?". Yuanjian packages that into a zero-dependency engine: it computes hashes, identifies the
Files scanned: 13. 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 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. 32 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 870 tokens
- 100Running it twice. No mutating operations
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
- -31 of 2 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 291: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.