AC forensics-automation
Automated Linux forensic collection and archival. Generate comprehensive system forensic reports (users, network, logs, processes, packages, disk usage, etc.) and automatically upload to Google Drive or email results. Use when you need to: (1) Quickly collect forensic data from a Linux system, (2) Archive forensic reports to Google Drive, (3) Automate forensic collection + sharing in one command, or (4) Build forensic automation into security workflows.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Automated Linux forensic collection and archival. Generate compreh… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (forensics-automation) differs from the folder (linux-forensics-automation)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 33 steps
- 100Execution cost. Instruction body is 1673 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 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 457: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 33 items
- +4Has examples (22 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.