---
name: agent-diagnostic
description: Use before orchestration to diagnose agent configuration, and after orchestration to extract learnings. Applies the 3-Sets diagnostic (Tool-Set, Skill-Set, Mind-Set) to optimize agent alignment.
---

# Agent Diagnostic — 3-Sets Analysis

Before spawning agents, diagnose their configuration across three dimensions. The weakest dimension caps the agent's output. Alignment across dimensions matters more than excellence in any single one.

## The Three Sets

| Set | What It Is | Agent Equivalent |
|-----|-----------|-----------------|
| **Tool-Set** | What the agent can access | File read/write, git, bash, MCP servers |
| **Skill-Set** | What the agent's model can do | Haiku (fast/cheap), Sonnet (balanced), Opus (deep reasoning) |
| **Mind-Set** | How the agent approaches the task | Archetype definition, system prompt focus |

## Pre-Orchestration Diagnostic

Before each orchestration, run a quick check per agent:

### Tool-Set Check
- Does the agent have the tools it needs for its role?
- Explorer needs: file read, grep, git log — NOT file write
- Maker needs: file read/write, git, bash, test runner
- Guardian needs: file read, git diff — NOT file write
- Does the agent have tools it DOESN'T need? Remove them. Excess tools create noise and distraction.

**Bottleneck signal:** Agent can't perform its core task due to missing capability.
**Fix:** Add the missing tool. Don't upgrade the model — it won't compensate.

### Skill-Set Check
- Is the model tier matched to the cognitive demand?
- Research, filtering, pattern matching → Haiku (cheap, fast)
- Design, code generation, structured review → Sonnet (balanced)
- Holistic judgment, complex trade-offs, architecture → Opus (deep, expensive)

| Archetype | Default Tier | Why |
|-----------|-------------|-----|
| Explorer | Haiku | Pattern matching and synthesis — breadth over depth |
| Creator | Sonnet | Design requires reasoning but not deep judgment |
| Maker | Sonnet | Code generation is Sonnet's sweet spot |
| Guardian | Sonnet | Security review needs structured reasoning |
| Skeptic | Sonnet | Assumption challenging needs analytical depth |
| Trickster | Haiku | Edge case generation is fast, creative work |
| Sage | Sonnet | Quality review needs good judgment; Opus only for large changes |

**Bottleneck signal:** Agent produces shallow output on complex tasks, or expensive model on simple tasks.
**Fix:** Adjust model tier. Don't add more tools — they won't compensate for reasoning limits.

### Mind-Set Check
- Is the archetype prompt focused on the right concern?
- Does the prompt contain contradictions? ("Be thorough" + "Be fast")
- Is the shadow definition specific enough to be detectable?
- Is the prompt appropriately sized? (Under 500 words — longer prompts dilute focus)

**Bottleneck signal:** Agent produces generic output, misses its archetype's core concern, or falls into shadow immediately.
**Fix:** Sharpen the prompt. Don't upgrade the model — a vague prompt stays vague on any model.

## The Chain Principle

The weakest set determines the result:

```
Tool-Set: 90  Skill-Set: 90  Mind-Set: 30  →  Output: ~30
```

An Opus model (Skill-Set: 100) with a vague prompt (Mind-Set: 30) wastes money. A Haiku model (Skill-Set: 60) with a perfectly focused archetype (Mind-Set: 90) and the right tools (Tool-Set: 80) produces better results at 1/50th the cost.

**Always fix the weakest set first.**

## The Alignment Principle

Three agents with modest but aligned configurations outperform three individually excellent but misaligned agents.

Signs of misalignment:
- Explorer researches topics the Creator doesn't use in the proposal (Mind-Set mismatch)
- Maker has tools the proposal doesn't reference (Tool-Set excess)
- Guardian reviews at threat level inappropriate to the context (Mind-Set miscalibration)
- Expensive model on a task that doesn't need it (Skill-Set waste)

## Post-Orchestration Learning

After each orchestration, extract learnings to `.archeflow/memory/`:

### What to Record

**Tool-Set learnings:**
- "This project uses pnpm, not npm" → future Makers know
- "The test runner is vitest, not jest" → future Makers and Sages know
- "No database access in CI" → future Guardians adjust threat model

**Skill-Set learnings:**
- "Complex type inference in this codebase requires Sonnet minimum" → future routing
- "Haiku was sufficient for all Check phase reviews in this project" → cost savings

**Mind-Set learnings:**
- "Guardian was paranoid on auth module — auth tests are comprehensive, calibrate to normal risk" → future calibration
- "Explorer rabbit-holed in the monorepo — add 10-file cap for this codebase" → future shadow tuning

### Memory Format

Write to `.archeflow/memory/<category>.md`:

```markdown
## Tool-Set
- Package manager: pnpm (not npm)
- Test runner: vitest
- CI: GitHub Actions, no DB access in CI

## Skill-Set
- Type-heavy modules need Sonnet minimum
- Standard CRUD routes work fine with Haiku review

## Mind-Set
- Auth module: well-tested, normal risk level (don't over-guard)
- Payment module: no tests, elevated risk (Guardian should be thorough)
```

Keep entries factual and specific. No opinions, no predictions. Update after each orchestration — don't append endlessly, revise what changed.

## Attention Filters

Each archetype reads only what's relevant from shared context:

| Archetype | Reads | Ignores |
|-----------|-------|---------|
| Explorer | Task description, codebase | Prior proposals |
| Creator | Explorer's research, task description | Implementation details |
| Maker | Creator's proposal | Explorer's research, reviews |
| Guardian | Maker's git diff, proposal's risk section | Explorer's research |
| Skeptic | Creator's proposal (assumptions) | Git diff details |
| Trickster | Maker's git diff only | Everything else |
| Sage | Proposal + implementation + diff | Explorer's raw research |

When spawning agents, pass only the relevant artifacts — not everything. This reduces context window waste and sharpens focus.