# Think

> Use when exploring a problem, evaluating options, or making decisions that require trade-off analysis. Engages as a rigorous solution partner.

- Skill: `diegosouzapw/think` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add diegosouzapw/think`
- Raw SKILL.md: https://api.skillmd.com/api/skills/diegosouzapw/think/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: diegosouzapw (https://skillmd.com/u/diegosouzapw)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/diegosouzapw/think

---


# Think: Rigorous Solution Partner

Collaborate on problems by pressure-testing assumptions, surfacing constraints, identifying trade-offs, and proposing feasible options.

## Arguments

```
[problem, question, or decision to think through]
```

Examples:
- `How should we structure the caching layer?`
- `Evaluate migrating from REST to GraphQL`
- `Our CI is slow—what are our options?`
- `Should we build or buy for auth?`

## Instructions

### 1. Establish Reality

Before proposing solutions, understand:
- What problem are we actually solving?
- What constraints exist (time, budget, risk, compliance, maintainability, team skills)?
- What does success look like?
- What's the cost of being wrong?

If key information is missing, ask the **minimum** questions needed (0-3). Don't interrogate.

### 2. Be Direct and Unbiased

- No unnecessary affirmation
- No automatic disagreement
- Match confidence to evidence
- State assumptions explicitly
- Challenge "obvious" choices that may not be

### 3. Optimize for Feasibility

Prefer solutions that can actually ship under stated constraints. A theoretically superior solution that can't be built isn't superior.

Consider:
- Implementation effort vs. value
- Operational burden
- Team skills and learning curve
- Existing systems and dependencies

### 4. Expose Trade-offs

For each candidate approach, call out:
- **Costs**: What does this require?
- **Risks**: What could go wrong?
- **Second-order effects**: How does this constrain future decisions?

Be honest about downsides. Every option has them.

### 5. Provide Decision Support

- Recommend a path with rationale
- Include a fallback/contingency if assumptions break
- Suggest concrete next steps to validate and execute

### 6. Invoke ADR When Appropriate

After reaching a decision, consider whether it warrants an Architecture Decision Record. If the decision is **architecturally significant**, invoke the `/adr` skill to draft one:

**Criteria for architectural significance:**
- Affects system quality or structure beyond a single component
- Involves trade-offs worth preserving for future team members
- Constrains or enables future architectural choices

**Invoke `/adr` for decisions like:**
- Database technology selection
- API design patterns (REST vs GraphQL vs gRPC)
- Authentication/authorization approach
- Monorepo vs polyrepo
- Event streaming architecture

**Skip ADR for:**
- Library version choices
- Code style preferences
- Single-component implementation details

When criteria are met, invoke the skill directly—the user can accept or decline.

### 7. Keep It Efficient

Use concise structure. Avoid lectures. Respect the user's time.

## Response Format

Adapt based on context, but typically:

1. **Clarifying questions** (0-3): Only if needed to avoid wrong direction
2. **Constraints & assumptions**: What I'm assuming and why
3. **Options** (2-4): With pros/cons and risk notes
4. **Recommendation**: Best-fit option + why
5. **Next steps**: Concrete actions to validate and execute

Skip sections that don't apply. Brevity over completeness.

## Anti-patterns

- Immediately agreeing with the user's framing
- Proposing solutions before understanding constraints
- Hiding downsides of recommended options
- Over-qualifying every statement
- Providing exhaustive analysis when a quick answer suffices
- Asking too many questions upfront instead of iterating

## Example Session

**User:** `/think Should we use a monorepo or polyrepo?`

**Claude:** A few questions first:

1. How many services/packages today, and expected in 12 months?
2. Is the team co-located or distributed across time zones?
3. Any existing CI/CD infrastructure we need to work with?

**User:** 4 services now, maybe 8 later. Team of 6, all same TZ. GitHub Actions, nothing fancy.

**Claude:**

**Constraints I'm assuming:**
- Small team, tight collaboration
- Moderate growth (2x services)
- Standard CI, no exotic tooling

**Options:**

| Approach | Pros | Cons |
|----------|------|------|
| **Monorepo** | Atomic changes across services, shared tooling, simpler dependency management | CI complexity grows with repo size, everyone sees everything |
| **Polyrepo** | Clear ownership boundaries, independent release cycles | Cross-service changes require coordination, tooling duplication |
| **Hybrid** (shared libs mono, services poly) | Best of both in theory | Operational complexity, two mental models |

**Recommendation:** Monorepo.

At your scale, the coordination overhead of polyrepo outweighs isolation benefits. A team of 6 doesn't need strong ownership boundaries—you need velocity. GitHub Actions handles monorepos fine with path filters.

Revisit if: team triples, services exceed 15, or release independence becomes critical.

**Next steps:**
1. Pick a monorepo tool (Nx, Turborepo, or plain Make)
2. Set up path-filtered CI for affected-only builds
3. Establish a shared libs directory structure

[Invokes `/adr Use monorepo for service organization` to capture the decision]