feat: add 3-Sets agent diagnostic and attention filters

New skill: agent-diagnostic — applies the 3-Sets framework (Tool-Set, Skill-Set, Mind-Set) to agent orchestration: - Pre-orchestration diagnostic: check each agent's configuration across three dimensions, fix the weakest set first - Chain principle: weakest set caps output (Opus + bad prompt = waste) - Alignment principle: modest aligned agents beat excellent misaligned ones - Attention filters: each archetype reads only relevant artifacts - Post-orchestration learning: extract learnings to persistent memory structured by the three sets Based on the 3-Sets Method diagnostic framework.
2026-04-02 18:32:18 +00:00
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--- a/README.md
+++ b/README.md
@@ -88,6 +88,7 @@ archeflow/
 │   ├── do-phase/                    # Maker implementation rules
 │   ├── check-phase/                 # Reviewer protocols (all 4)
 │   ├── shadow-detection/            # Recognizing and correcting dysfunction
+│   ├── agent-diagnostic/            # 3-Sets analysis for agent configuration
 │   ├── autonomous-mode/             # Unattended overnight sessions
 │   ├── custom-archetypes/           # Creating domain-specific roles
 │   └── workflow-design/             # Designing custom workflows
@@ -143,6 +144,22 @@ pdca:
  act: { exit_when: all_approved, max_cycles: 2 }
 ```

+## Agent Diagnostic — The 3-Sets Lens
+
+Before orchestrating, ArcheFlow diagnoses each agent across three dimensions:
+
+| Set | Question | Fix |
+|-----|----------|-----|
+| **Tool-Set** | Does the agent have the right capabilities? | Add missing tools, remove noisy ones |
+| **Skill-Set** | Is the model tier matched to the task? | Adjust model, don't compensate with tools |
+| **Mind-Set** | Is the archetype prompt focused and aligned? | Sharpen prompt, don't compensate with model |
+
+**The chain principle:** The weakest set caps the output. An Opus model with a vague prompt wastes money. A Haiku with a focused archetype and the right tools outperforms it at 1/50th the cost.
+
+**The alignment principle:** Three modestly configured agents that are aligned outperform three individually excellent but misaligned agents.
+
+Based on the [3-Sets Method](https://git.xorwell.de/chris/workspace) — an integrative diagnostic framework for Tool-Set, Skill-Set, and Mind-Set alignment.
+
 ## Philosophy

 ArcheFlow is built on three beliefs:
@@ -153,6 +170,8 @@ ArcheFlow is built on three beliefs:

 3. **Autonomy needs structure.** Agents left to their own devices produce mediocre results. Agents given clear roles, typed communication, and quality gates produce exceptional work — even overnight, even unattended.

+4. **Fix the weakest set, not the strongest.** Don't upgrade the model when the problem is a bad prompt. Don't add tools when the problem is wrong model tier. Diagnose first, then invest where it matters.
+
 ## License

 MIT
--- a/skills/agent-diagnostic/SKILL.md
+++ b/skills/agent-diagnostic/SKILL.md
@@ -0,0 +1,136 @@
+---
+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.
--- a/skills/using-archeflow/SKILL.md
+++ b/skills/using-archeflow/SKILL.md
@@ -46,5 +46,6 @@ Act   → All approved? Merge. Issues? Cycle back to Plan.
 - **archeflow:orchestration** — Step-by-step execution guide
 - **archeflow:plan-phase** / **do-phase** / **check-phase** — Phase protocols
 - **archeflow:shadow-detection** — Recognizing dysfunction
+- **archeflow:agent-diagnostic** — 3-Sets analysis (Tool-Set, Skill-Set, Mind-Set) for agent configuration
 - **archeflow:autonomous-mode** — Unattended sessions
 - **archeflow:custom-archetypes** / **workflow-design** — Extending ArcheFlow