refactor: replace 3-Sets diagnostic with focused attention filters and memory
The 3-Sets framework doesn't transfer well to agents — all three dimensions are fully visible and controllable config, not hidden human psychology. Removed the branding, kept the practical bits: - attention-filters: what context each archetype receives (token savings) - memory: persistent learnings across orchestrations (project knowledge)
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21
README.md
21
README.md
@@ -88,7 +88,8 @@ archeflow/
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│ ├── do-phase/ # Maker implementation rules
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│ ├── check-phase/ # Reviewer protocols (all 4)
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│ ├── shadow-detection/ # Recognizing and correcting dysfunction
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│ ├── agent-diagnostic/ # 3-Sets analysis for agent configuration
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│ ├── attention-filters/ # What context each archetype receives
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│ ├── memory/ # Persistent learnings across orchestrations
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│ ├── autonomous-mode/ # Unattended overnight sessions
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│ ├── custom-archetypes/ # Creating domain-specific roles
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│ └── workflow-design/ # Designing custom workflows
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@@ -144,22 +145,6 @@ pdca:
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act: { exit_when: all_approved, max_cycles: 2 }
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```
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## Agent Diagnostic — The 3-Sets Lens
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Before orchestrating, ArcheFlow diagnoses each agent across three dimensions:
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| Set | Question | Fix |
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|-----|----------|-----|
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| **Tool-Set** | Does the agent have the right capabilities? | Add missing tools, remove noisy ones |
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| **Skill-Set** | Is the model tier matched to the task? | Adjust model, don't compensate with tools |
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| **Mind-Set** | Is the archetype prompt focused and aligned? | Sharpen prompt, don't compensate with model |
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**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.
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**The alignment principle:** Three modestly configured agents that are aligned outperform three individually excellent but misaligned agents.
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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.
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## Philosophy
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ArcheFlow is built on three beliefs:
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@@ -170,8 +155,6 @@ ArcheFlow is built on three beliefs:
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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.
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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.
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## License
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MIT
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@@ -1,136 +0,0 @@
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---
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name: agent-diagnostic
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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.
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---
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# Agent Diagnostic — 3-Sets Analysis
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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.
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## The Three Sets
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| Set | What It Is | Agent Equivalent |
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|-----|-----------|-----------------|
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| **Tool-Set** | What the agent can access | File read/write, git, bash, MCP servers |
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| **Skill-Set** | What the agent's model can do | Haiku (fast/cheap), Sonnet (balanced), Opus (deep reasoning) |
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| **Mind-Set** | How the agent approaches the task | Archetype definition, system prompt focus |
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## Pre-Orchestration Diagnostic
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Before each orchestration, run a quick check per agent:
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### Tool-Set Check
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- Does the agent have the tools it needs for its role?
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- Explorer needs: file read, grep, git log — NOT file write
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- Maker needs: file read/write, git, bash, test runner
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- Guardian needs: file read, git diff — NOT file write
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- Does the agent have tools it DOESN'T need? Remove them. Excess tools create noise and distraction.
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**Bottleneck signal:** Agent can't perform its core task due to missing capability.
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**Fix:** Add the missing tool. Don't upgrade the model — it won't compensate.
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### Skill-Set Check
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- Is the model tier matched to the cognitive demand?
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- Research, filtering, pattern matching → Haiku (cheap, fast)
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- Design, code generation, structured review → Sonnet (balanced)
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- Holistic judgment, complex trade-offs, architecture → Opus (deep, expensive)
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| Archetype | Default Tier | Why |
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|-----------|-------------|-----|
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| Explorer | Haiku | Pattern matching and synthesis — breadth over depth |
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| Creator | Sonnet | Design requires reasoning but not deep judgment |
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| Maker | Sonnet | Code generation is Sonnet's sweet spot |
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| Guardian | Sonnet | Security review needs structured reasoning |
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| Skeptic | Sonnet | Assumption challenging needs analytical depth |
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| Trickster | Haiku | Edge case generation is fast, creative work |
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| Sage | Sonnet | Quality review needs good judgment; Opus only for large changes |
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**Bottleneck signal:** Agent produces shallow output on complex tasks, or expensive model on simple tasks.
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**Fix:** Adjust model tier. Don't add more tools — they won't compensate for reasoning limits.
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### Mind-Set Check
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- Is the archetype prompt focused on the right concern?
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- Does the prompt contain contradictions? ("Be thorough" + "Be fast")
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- Is the shadow definition specific enough to be detectable?
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- Is the prompt appropriately sized? (Under 500 words — longer prompts dilute focus)
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**Bottleneck signal:** Agent produces generic output, misses its archetype's core concern, or falls into shadow immediately.
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**Fix:** Sharpen the prompt. Don't upgrade the model — a vague prompt stays vague on any model.
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## The Chain Principle
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The weakest set determines the result:
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```
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Tool-Set: 90 Skill-Set: 90 Mind-Set: 30 → Output: ~30
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```
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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.
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**Always fix the weakest set first.**
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## The Alignment Principle
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Three agents with modest but aligned configurations outperform three individually excellent but misaligned agents.
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Signs of misalignment:
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- Explorer researches topics the Creator doesn't use in the proposal (Mind-Set mismatch)
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- Maker has tools the proposal doesn't reference (Tool-Set excess)
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- Guardian reviews at threat level inappropriate to the context (Mind-Set miscalibration)
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- Expensive model on a task that doesn't need it (Skill-Set waste)
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## Post-Orchestration Learning
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After each orchestration, extract learnings to `.archeflow/memory/`:
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### What to Record
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**Tool-Set learnings:**
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- "This project uses pnpm, not npm" → future Makers know
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- "The test runner is vitest, not jest" → future Makers and Sages know
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- "No database access in CI" → future Guardians adjust threat model
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**Skill-Set learnings:**
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- "Complex type inference in this codebase requires Sonnet minimum" → future routing
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- "Haiku was sufficient for all Check phase reviews in this project" → cost savings
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**Mind-Set learnings:**
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- "Guardian was paranoid on auth module — auth tests are comprehensive, calibrate to normal risk" → future calibration
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- "Explorer rabbit-holed in the monorepo — add 10-file cap for this codebase" → future shadow tuning
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### Memory Format
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Write to `.archeflow/memory/<category>.md`:
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```markdown
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## Tool-Set
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- Package manager: pnpm (not npm)
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- Test runner: vitest
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- CI: GitHub Actions, no DB access in CI
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## Skill-Set
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- Type-heavy modules need Sonnet minimum
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- Standard CRUD routes work fine with Haiku review
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## Mind-Set
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- Auth module: well-tested, normal risk level (don't over-guard)
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- Payment module: no tests, elevated risk (Guardian should be thorough)
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```
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Keep entries factual and specific. No opinions, no predictions. Update after each orchestration — don't append endlessly, revise what changed.
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## Attention Filters
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Each archetype reads only what's relevant from shared context:
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| Archetype | Reads | Ignores |
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|-----------|-------|---------|
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| Explorer | Task description, codebase | Prior proposals |
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| Creator | Explorer's research, task description | Implementation details |
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| Maker | Creator's proposal | Explorer's research, reviews |
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| Guardian | Maker's git diff, proposal's risk section | Explorer's research |
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| Skeptic | Creator's proposal (assumptions) | Git diff details |
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| Trickster | Maker's git diff only | Everything else |
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| Sage | Proposal + implementation + diff | Explorer's raw research |
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When spawning agents, pass only the relevant artifacts — not everything. This reduces context window waste and sharpens focus.
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39
skills/attention-filters/SKILL.md
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39
skills/attention-filters/SKILL.md
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@@ -0,0 +1,39 @@
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---
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name: attention-filters
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description: Use when spawning archetype agents to decide what context each agent receives. Reduces token waste and sharpens focus by passing only relevant artifacts.
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---
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# Attention Filters
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Each archetype needs different context. Pass only what's relevant — not everything.
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| Archetype | Receives | Does NOT Receive |
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|-----------|----------|-----------------|
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| Explorer | Task description, codebase access | Prior proposals or reviews |
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| Creator | Explorer's research + task description | Implementation details |
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| Maker | Creator's proposal | Explorer's research, reviews |
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| Guardian | Maker's git diff + proposal risk section | Explorer's research |
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| Skeptic | Creator's proposal (focus: assumptions) | Git diff details |
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| Trickster | Maker's git diff only | Everything else |
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| Sage | Proposal + implementation + diff | Explorer's raw research |
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## Why This Matters
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- **Token cost:** A Guardian reading the Explorer's 2000-word research wastes ~2600 tokens on irrelevant context
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- **Focus:** An agent with too much context drifts from its archetype's concern
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- **Shadow prevention:** Over-loading context encourages rabbit-holing (Explorer) and scope creep (Maker)
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## In Practice
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When spawning a Check-phase agent, include only the filtered context in the prompt:
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```
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# Guardian receives:
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"Review these changes: <git diff output>
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The proposal identified these risks: <risks section only>
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Verdict: APPROVED or REJECTED with findings."
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# NOT:
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"Here is the full research, the full proposal, the full implementation,
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the full git log, and everything else we have..."
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```
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60
skills/memory/SKILL.md
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60
skills/memory/SKILL.md
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---
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name: memory
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description: Use after orchestration to record learnings, and before orchestration to load project-specific context. Persistent knowledge that makes future orchestrations smarter.
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---
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# Persistent Memory
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ArcheFlow learns across orchestrations. After each run, extract what was discovered. Before the next run, load it so agents start smarter.
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## What to Record
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Store in `.archeflow/memory/project.md`:
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```markdown
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## Project Context
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- Language: TypeScript, strict mode
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- Package manager: pnpm
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- Test runner: vitest
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- CI: GitHub Actions (no DB access in CI)
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## Risk Map
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- auth/ — well-tested (95% coverage), normal risk
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- payment/ — no tests, elevated risk, Guardian should be thorough
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- legacy/api-v1/ — deprecated, do not modify
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## Model Notes
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- Type-heavy modules need Sonnet minimum (Haiku produced incomplete types)
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- Standard CRUD reviews work fine with Haiku
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## Shadow History
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- Explorer rabbit-holed in monorepo (cycle 1 of orchestration on 2026-03-28)
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→ added 10-file cap to Explorer prompt for this project
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```
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## What NOT to Record
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- Anything derivable from code or git history
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- Temporary state from current orchestration
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- Opinions or predictions
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- Anything that changes every week
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## When to Read
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Before orchestration, check if `.archeflow/memory/project.md` exists. If it does, inject relevant sections into agent prompts:
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- Explorer gets: Project Context
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- Guardian gets: Risk Map
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- Maker gets: Project Context
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- All agents get: Model Notes (for self-calibration)
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## When to Write
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After orchestration completes, update memory if anything new was learned:
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- New project conventions discovered by Explorer
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- Risk areas identified by Guardian
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- Model tier adjustments needed (Haiku insufficient, or Opus unnecessary)
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- Shadow patterns that recurred
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Update existing entries — don't append endlessly. Memory should be a current snapshot, not a changelog.
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@@ -46,6 +46,7 @@ Act → All approved? Merge. Issues? Cycle back to Plan.
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- **archeflow:orchestration** — Step-by-step execution guide
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- **archeflow:plan-phase** / **do-phase** / **check-phase** — Phase protocols
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- **archeflow:shadow-detection** — Recognizing dysfunction
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- **archeflow:agent-diagnostic** — 3-Sets analysis (Tool-Set, Skill-Set, Mind-Set) for agent configuration
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- **archeflow:attention-filters** — What context each archetype receives
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- **archeflow:memory** — Persistent learnings across orchestrations
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- **archeflow:autonomous-mode** — Unattended sessions
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- **archeflow:custom-archetypes** / **workflow-design** — Extending ArcheFlow
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