BC prompting-co
Interact with The Prompting Company platform to monitor brand visibility across AI engines, manage tracked prompts, review and publish content drafts, and retrieve SOV and AI traffic analytics. Use when the user asks about brand performance, competitor analysis, prompt tracking, content approvals, or daily/weekly stats from their Prompting Company workspace.
As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, consistency, execution cost
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 · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 9070 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 64/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 40Consistency. Frontmatter name (prompting-co) differs from the folder (promptingco)
- 40Execution cost. Instruction body is 9070 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 85Steps. 106 steps, 1 vague phrases
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 6 branches, has a failure section
- 100Running it twice. Mutating operations check current state
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 360: enough signal without eating the budget
- +4Structure: 74 headings
- +3Step-by-step instructions: 106 items
- +3Output format is stated explicitly
- +4Has examples (71 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.