brainstorming — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited brainstorming (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Turn rough ideas into fully formed designs through natural collaborative dialogue.
Before proposing anything:
One question at a time - Don't overwhelm with multiple questions. If a topic needs more exploration, break it into multiple questions.
Once you understand the problem:
Example:
For caching, I see three approaches:
1. In-memory cache (recommended) - Simple, no dependencies,
works well for your scale. Trade-off: lost on restart.
2. Redis - Shared across instances, persistent.
Trade-off: Extra infrastructure.
3. File-based - Simple, persistent.
Trade-off: Slower, file locking complexity.
I'd recommend #1 unless you need persistence or multi-instance.
What matters most to you?Once you believe you understand what you're building:
Example section:
## Data Flow
When a user submits a form:
1. Frontend validates input, shows inline errors
2. POST to /api/submit with JSON body
3. Backend validates again (never trust client)
4. Store in database, return ID
5. Frontend shows success with link to view
Does this flow make sense, or should we handle any edge cases differently?Documentation:
Implementation:
| Principle | Why |
|---|---|
| One question at a time | Don't overwhelm |
| Multiple choice preferred | Easier to answer than open-ended |
| YAGNI ruthlessly | Remove unnecessary features from all designs |
| Explore alternatives | Always propose 2-3 approaches before settling |
| Incremental validation | Present design in sections, validate each |
| Be flexible | Go back and clarify when something doesn't make sense |
Understanding scope:
Clarifying constraints:
Multiple choice:
User: "I need a way to export reports"
You: "Before I propose anything, let me understand the requirements.
What format do you need the exports in?
(a) CSV - simple, works in Excel
(b) PDF - formatted, good for sharing
(c) Both
(d) Something else"
User: "CSV is fine"
You: "Got it. A few more questions:
How large are these reports typically?
(a) Small - under 1000 rows
(b) Medium - 1000-100k rows
(c) Large - over 100k rows"
User: "Medium usually, sometimes large"
You: "For that size, I'd recommend streaming the CSV generation
rather than building it all in memory. Here's the approach:
## Export Flow
1. User clicks Export button
2. Backend starts streaming response with CSV headers
3. Query database in batches of 1000 rows
4. Stream each batch to response as CSV
5. Client downloads as file
This handles large files without memory issues.
Does this approach work for you?"Understand before you build.
Ask questions. Propose options. Validate incrementally.
Don't start coding until the design is clear.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.