BB contentclaw
Automated content generation engine. Transform source material (papers, podcasts, case studies) into platform-ready content using recipes and brand graphs. Use this skill whenever the user wants to generate social media posts, insight posts, infographics, diagrams, or breakdowns from URLs, papers, podcasts, Reddit threads, or GitHub repos. Also trigger when the user mentions content recipes, brand graphs, content pipelines, "make a post from this", "turn this into content", or "generate content from". Requires uv, FAL_KEY (image generation), and EXA_API_KEY (topic discovery) in .env.
Automated content generation engine.
As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice
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 asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
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medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Bash Read Write Edit Glob Grep AskUserQuestion
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low Dangerous commands
cmd-pipe-to-shell-known-hostSKILL.md:56Pipe-to-shell installer from a well-known host (still executes remote code) (quoted — discussed, not commanded)- Linux/macOS (alternative): `curl -LsSf https://astral.sh/uv/install.sh | sh` (review the script at https://astral.sh/uv/install.sh before running)
quoted
Files scanned: 24. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6534 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 69/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 51 mutating operations with no state check
- 60Steps. 185 steps, 5 vague phrases
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6534 tokens
- 100Tools and files. Tools declared in frontmatter
- 100Failures and branches. 10 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 16 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (34 tags): a typed call is more reliable
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
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 590: enough signal without eating the budget
- +4Structure: 45 headings
- +3Step-by-step instructions: 185 items
- +4Has examples (2 code blocks)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.