AC sixtyfour
People and company intelligence via the Sixtyfour AI API. AI research agents that read the live web — not static databases — to return structured, confidence-scored profiles. Use when you need to: (1) enrich a lead with full profile data (name, title, email, phone, LinkedIn, tech stack, funding, pain points — up to 50 custom fields), (2) research a company (team size, tech stack, funding rounds, hiring signals, key people), (3) find someone's professional or personal email address, (4) find phone numbers, (5) score/qualify leads against custom criteria with reasoning, (6) search for people or companies via natural language query, or (7) run batch enrichment workflows via API. NOT for: general web browsing, tasks unrelated to people/company data, or non-enrichment research.
As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice
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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 63/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (sixtyfour) differs from the folder (sixtyfour-skill)
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 4 steps
- 100Execution cost. Instruction body is 3155 tokens
- low 13 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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 783: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 4 items
- +3Output format is stated explicitly
- +4Has examples (14 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.