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.

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