AD Content Compound
Content atom library builder for social media creators. Scans a directory of your past content (markdown, text files) and extracts reusable "content atoms" — claims, data points, anecdotes, frameworks, contrarian takes, and questions. Tags each with topic keywords and source attribution. When given a new topic, retrieves the most relevant atoms from your personal library and generates a post outline. Like Zettelkasten but automated — every post you write makes the next one easier. Solves the 77% creator burnout problem by eliminating "blank page" starts. Research-backed (Zettelkasten serendipity effect, Justin Welsh 730-day content library, content atomization hub-and-spoke model). Zero external dependencies.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
frontmatter-keyunknown frontmatter key "homepage"
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. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (Content Compound) differs from the folder (phy-content-compound)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 8 steps
- 100Execution cost. Instruction body is 1518 tokens
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 718: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (4 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.