BB contentclaw
Turn papers, podcasts, and case studies into publish-ready social posts, infographics, and diagrams. Discovers trending topics via Exa, generates content with spec-first recipes, and creates images with fal.ai. Trigger on: "make a post from this", "turn this into content", "generate content", "discover topics", content recipes, brand graphs.
As a process B 67/100 · Nearly there — weak spots: result and completion, consistency, 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
-
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: 44. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5153 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 67/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 20 mutating operations with no state check
- 40Consistency. Frontmatter name (contentclaw) differs from the folder (content-claw)
- 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 5153 tokens
- 85Steps. 147 steps, 1 vague phrases
- 100Tools and files. Tools declared in frontmatter
- 100Failures and branches. 26 branches, has a failure section
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (25 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
- -44 reference files, but SKILL.md never points to them: the model will not open them
- -32 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 343: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 147 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.