AD auto-sec-blogger
AI-powered security blog automation system (identical to github.com/rebugui/intelligence-agent). Collects news from Google News, arXiv, HackerNews → generates blog posts with GLM-4.7 → publishes to Notion → auto-deploys to GitHub Pages via Git. Features Human-in-the-Loop approval workflow. Use when you want to automate blog writing, news collection, or content generation with the exact functionality of the original intelligence-agent repository. Triggers: "블로그 글 작성", "보안 뉴스 발행", "깃헙 블로그 발행", "intelligence agent", "지능형 에이전트", "자동 글쓰기".
As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 25. 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: AI-powered security blog automation system (identical to github.co… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 39/100
- 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
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (auto-sec-blogger) differs from the folder (auto-sec-blogger-repo)
- 60Tools and files. Uses tools (git, python) that frontmatter does not declare
- 100Steps. 42 steps
- 100Execution cost. Instruction body is 1568 tokens
- low 15 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -36 of 20 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 540: enough signal without eating the budget
- +4Structure: 43 headings
- +3Step-by-step instructions: 42 items
- +4Has examples (23 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.