Safety Check — Risk Awareness with Variable Weight

The Problem You Solve

Agents optimize for "it works" — but never ask "what breaks?". A rename is harmless. A schema change on a public API can break 50 consumers. A migration that drops a column destroys data that no git revert can recover. The agent doesn't distinguish between these scenarios because it has no risk radar.

The typical mitigation is "be careful" — a vague instruction that produces vague caution. The agent either ignores it (nothing changes) or applies it uniformly (everything gets a warning, nothing gets real analysis). Both outcomes are useless.

This skill replaces vague caution with dimensional investigation at variable depth. Instead of "be careful", the agent learns to ask "what can go wrong in THIS specific dimension, and can I verify it RIGHT NOW?". It transforms reflection into active research with evidence, scaling effort to match real risk — not perceived risk.

Weight Model

The skill operates at three weights. The weight determines how deep the analysis goes. Start at Light by default; escalate when signals warrant it.

Weight can escalate mid-analysis. If a Light scan reveals signals that warrant Standard, say so and escalate. The user or orchestrator can also request a specific weight directly.

Risk Dimensions — Catalogue (Not a Fixed Checklist)

The agent does NOT evaluate all dimensions every time. At Light weight, evaluate 0-2 most relevant. At Deep, evaluate all that apply. The request context determines which.

Core Dimensions

Reversibility as Multiplier

Reversibility is not a standalone risk — it's a multiplier on every other dimension. Low risk + irreversible = effectively HIGH risk.

The agent MUST challenge claims of reversibility. "Git revert solves it" is often false — production data was already mutated, consumers already consumed the broken API, secrets were already scraped. Ask: "After reverting the code, is the STATE also reverted?" If the answer is no, the risk is higher than it appears.

Contextual Dimensions

Beyond the 5 core dimensions, the agent can add domain-specific dimensions when context demands it:

• Cost — cloud resources, API call billing, storage growth
• Compliance — GDPR, HIPAA, PCI-DSS, data residency
• UX Impact — user-facing behavior changes, accessibility regressions
• Performance — latency, throughput, memory footprint

Add contextual dimensions only when the request context strongly signals them. Do not add them speculatively.

Procedure

Light (Weight 1)

1. Read the request/context
2. Identify which dimensions are relevant (0-2)
3. For each: 1 quick question, check if you can answer with available context
4. Produce Risk Pulse

Standard (Weight 2)

1. Read request + output from prior skills (task-intent, contextação if available)
2. Identify relevant dimensions (2-4)
3. For each dimension: actively research — search codebase, read docs, verify consumers, check breaking changes
4. Evaluate reversibility as multiplier
5. Produce Risk Radar

Deep (Weight 3)

1. Read all accumulated context
2. Evaluate ALL 5 core dimensions + any relevant contextual dimensions
3. For each: research online (changelogs, CVEs, migration guides, official docs). Search codebase exhaustively
4. Cross-reference: does risk in one dimension affect another? (e.g., security fix that breaks backward compatibility)
5. Evaluate reversibility of each risk individually
6. Produce Full Risk Radar with Mitigations and Accepted Risks

Active Research Gate

For every risk identified, ask: "Can I verify this right now with available tools?"

• If yes → verify. Fetch the doc, search the repo, check the changelog, read the spec.
• If no → mark as [unverified] with an explicit note on the impact of being wrong.

Prefer online and up-to-date sources. A risk you CAN verify but DON'T is an unforced error.

Output Formats

Risk Pulse (Light)

### Risk Pulse
- **[Dimension]**: [signal identified] — [action: proceed / investigate]
- **[Dimension]**: [signal identified] — [action: proceed / investigate]

Risk Radar (Standard / Deep)

### Risk Radar

| Dimension | Level | Evidence | Action |
|-----------|-------|----------|--------|
| Security | 🟢/🟡/🔴 | [what was verified] | proceed / investigate / block |
| Data Integrity | 🟢/🟡/🔴 | [what was verified] | ... |
| ... | ... | ... | ... |
| **Reversibility** | ↑ multiplier | [blast radius assessment] | ... |

### Mitigations (Standard/Deep only)
- [Risk]: [concrete mitigation — NOT generic]

### Accepted Risks (Deep only)
- [Accepted risk]: [explicit justification]

### Recommendation
[proceed / proceed with caution / stop and redesign]

Levels:

• 🟢 — No signal or verified safe
• 🟡 — Signal detected, manageable with mitigation
• 🔴 — Confirmed risk, requires action before proceeding

Companion Skills

• task-intent: Provides WHY/WHAT FOR context that informs which dimensions are relevant
• contextação: The Failure Modes axis from contextação can serve as initial seed. Safety-check expands with active dimensional research
• task-map: Accepted risks should be documented in the task-map for future reference
• error-learning: If an undetected risk causes a problem, register it as a lesson

Quality Checklist

• Weight (Light/Standard/Deep) explicitly declared?
• Dimensions evaluated are contextual to the request (not a fixed checklist)?
• For each risk: concrete evidence or marked as [unverified]?
• Reversibility evaluated as multiplier?
• Recommendation is one of the 3 options (proceed / proceed with caution / stop and redesign)?
• At Deep weight: online research performed for external risks?