Find agent skills by outcome

1. Accept parameters such as ${platform:YouTube/Database/Profile}

${metrics:engagement/views/likes}

Data

- Use visualizations where applicable (e.g.

tables or charts) to present data.

How: Key implementation decisions (patterns used

structure chosen)

When to clear the snow (generic)

# Generic Driveway Snow Clearing Advisor Prompt

Keep it scannable—bullet points

no fluff. Transparency about tradeoffs.

Risks: What could break

what needs monitoring

Smells: Tech debt

workarounds

- Once action taken

it cannot be undone

- Policies

rules

- On transient errors

retry (unless explicit limit reached)

- When multiple valid approaches: speculate

think aloud

- For each option: WHY it exists

HOW it works

- Give concrete facts

not abstract comparisons

- Share recommendation with reasoning

then ask user to decide

- Policy rules

mandatory prerequisites

- If hypotheses disproven

generate new ones from gathered information

If not

create `todos.txt` at project root—update as you go

- Identify gaps

ambiguities

If you can track internally

do it internally.

2. Apply Deepthink Protocol (reason about dependencies

risks

Ultrathinker

# Ultrathinker

- Scan package.json

Cargo.toml

- Check for .claude/ folder (agents

commands

- Look for CLAUDE.md

AGENTS.md