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refactor: replace 3-Sets diagnostic with focused attention filters and memory

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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)
This commit is contained in:
Christian Nennemann
2026-04-02 18:36:10 +00:00
parent ed821097de
commit cccaf86995
5 changed files with 103 additions and 156 deletions
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skills/agent-diagnostic/SKILL.md
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---
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.

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---
name: attention-filters
description: Use when spawning archetype agents to decide what context each agent receives. Reduces token waste and sharpens focus by passing only relevant artifacts.
---
# Attention Filters
Each archetype needs different context. Pass only what's relevant — not everything.
| Archetype | Receives | Does NOT Receive |
|-----------|----------|-----------------|
| Explorer | Task description, codebase access | Prior proposals or reviews |
| Creator | Explorer's research + task description | Implementation details |
| Maker | Creator's proposal | Explorer's research, reviews |
| Guardian | Maker's git diff + proposal risk section | Explorer's research |
| Skeptic | Creator's proposal (focus: assumptions) | Git diff details |
| Trickster | Maker's git diff only | Everything else |
| Sage | Proposal + implementation + diff | Explorer's raw research |
## Why This Matters
- **Token cost:** A Guardian reading the Explorer's 2000-word research wastes ~2600 tokens on irrelevant context
- **Focus:** An agent with too much context drifts from its archetype's concern
- **Shadow prevention:** Over-loading context encourages rabbit-holing (Explorer) and scope creep (Maker)
## In Practice
When spawning a Check-phase agent, include only the filtered context in the prompt:
```
# Guardian receives:
"Review these changes: <git diff output>
The proposal identified these risks: <risks section only>
Verdict: APPROVED or REJECTED with findings."
# NOT:
"Here is the full research, the full proposal, the full implementation,
the full git log, and everything else we have..."
```

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---
name: memory
description: Use after orchestration to record learnings, and before orchestration to load project-specific context. Persistent knowledge that makes future orchestrations smarter.
---
# Persistent Memory
ArcheFlow learns across orchestrations. After each run, extract what was discovered. Before the next run, load it so agents start smarter.
## What to Record
Store in `.archeflow/memory/project.md`:
```markdown
## Project Context
- Language: TypeScript, strict mode
- Package manager: pnpm
- Test runner: vitest
- CI: GitHub Actions (no DB access in CI)
## Risk Map
- auth/ — well-tested (95% coverage), normal risk
- payment/ — no tests, elevated risk, Guardian should be thorough
- legacy/api-v1/ — deprecated, do not modify
## Model Notes
- Type-heavy modules need Sonnet minimum (Haiku produced incomplete types)
- Standard CRUD reviews work fine with Haiku
## Shadow History
- Explorer rabbit-holed in monorepo (cycle 1 of orchestration on 2026-03-28)
→ added 10-file cap to Explorer prompt for this project
```
## What NOT to Record
- Anything derivable from code or git history
- Temporary state from current orchestration
- Opinions or predictions
- Anything that changes every week
## When to Read
Before orchestration, check if `.archeflow/memory/project.md` exists. If it does, inject relevant sections into agent prompts:
- Explorer gets: Project Context
- Guardian gets: Risk Map
- Maker gets: Project Context
- All agents get: Model Notes (for self-calibration)
## When to Write
After orchestration completes, update memory if anything new was learned:
- New project conventions discovered by Explorer
- Risk areas identified by Guardian
- Model tier adjustments needed (Haiku insufficient, or Opus unnecessary)
- Shadow patterns that recurred
Update existing entries — don't append endlessly. Memory should be a current snapshot, not a changelog.

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skills/using-archeflow/SKILL.md
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- **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:attention-filters** — What context each archetype receives
- **archeflow:memory** — Persistent learnings across orchestrations
- **archeflow:autonomous-mode** — Unattended sessions
- **archeflow:custom-archetypes** / **workflow-design** — Extending ArcheFlow