Christian Nennemann
1e96d87f49
Replaces generic "circuit breaker" with "Wiggum Break" — policy enforcement halt condition named after Chief Wiggum (policy + Ralph Loop's dad). Hard breaks (immediate halt) and soft breaks (finish then halt) with wiggum.break event type. Updated both papers and shadow-detection skill.
120 lines
6.1 KiB
Markdown
120 lines
6.1 KiB
Markdown
# archeflow — Multi-Agent Orchestration Plugin for Claude Code
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PDCA quality cycles with Jungian archetype roles, corrective action framework, sprint runner, and post-implementation review. Zero dependencies — pure Bash + Markdown.
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## Architecture
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```
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skills/ Slash commands and internal protocols (one SKILL.md per dir)
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run/ /af-run — self-contained PDCA orchestration (core skill)
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sprint/ /af-sprint — queue-driven parallel agent dispatch
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review/ /af-review — Guardian-led code review
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check-phase/ Shared reviewer protocol (used by run + review)
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act-phase/ Finding collection, fix routing, exit decisions
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shadow-detection/ Corrective action framework (archetype + system + policy)
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memory/ Cross-run lessons learned
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cost-tracking/ Token/cost awareness and budget enforcement
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domains/ Domain detection (code, writing, research)
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colette-bridge/ Writing context loader from colette.yaml
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multi-project/ Cross-repo orchestration with dependency DAG
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git-integration/ Per-phase commits, branch strategy, rollback
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templates/ Workflow/team bundle gallery
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autonomous-mode/ Unattended session protocol
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using-archeflow/ Session-start activation (auto-loaded via hook)
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agents/ Archetype personality definitions (one .md per archetype)
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lib/ Bash helper scripts (events, git, memory, progress, etc.)
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hooks/ Session-start hook (injects using-archeflow)
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templates/bundles/ Pre-configured workflow bundles
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```
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## Commands
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| Command | Purpose |
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|---------|---------|
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| `/af-run <task>` | PDCA orchestration with full agent cycle |
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| `/af-sprint` | Work the queue across projects |
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| `/af-review` | Review existing code changes |
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| `/af-status` | Current/last run status |
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| `/af-init` | Initialize ArcheFlow in a project |
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| `/af-score` | Archetype effectiveness scores |
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| `/af-memory` | Cross-run lesson memory |
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| `/af-report` | Full process report |
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| `/af-fanout` | Colette book fanout via agents |
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## Core Concepts
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### PDCA Cycle
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```
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Plan (Explorer + Creator) -> Do (Maker in worktree) -> Check (Guardian first, then others) -> Act (fix, merge, or cycle)
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```
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### Archetypes
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Explorer (research), Creator (design), Maker (implement), Guardian (security), Skeptic (assumptions), Trickster (edge cases), Sage (quality). Each has a virtue and a shadow — see `shadow-detection` skill.
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### Corrective Action Framework
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Three layers, one escalation protocol:
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- **Archetype shadows** — individual agent dysfunction
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- **System shadows** — orchestration-level issues (echo chamber, tunnel vision, scope creep)
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- **Policy boundaries** — operational limits (checkpoints, budgets, Wiggum Breaks)
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### Workflows
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| Risk Level | Workflow | Agents |
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|------------|----------|--------|
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| Low | `fast` | Creator -> Maker -> Guardian |
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| Medium | `standard` | Explorer + Creator -> Maker -> Guardian + Skeptic + Sage |
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| High | `thorough` | Explorer + Creator -> Maker -> All 4 reviewers |
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## Guardrails
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### DO
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- Keep skills self-contained. The `run` skill needs zero prerequisites — it was consolidated for a reason.
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- Write skills as operational instructions Claude can follow, not software specifications.
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- Use tables for reference data, numbered steps for protocols.
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- Emit events via `./lib/archeflow-event.sh` — but never let logging block orchestration.
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- Maintain the corrective action framework when adding new agent types.
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- Test skill changes by running `/af-run --dry-run` and verifying the flow.
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- Keep archetype personalities distinct — each agent definition in `agents/` has a specific voice.
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### DO NOT
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- **Add runtime dependencies.** This must stay zero-dependency (Bash + Markdown only).
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- **Bloat skills back up.** The consolidation from 27 to ~15 skills was intentional. Do not create new skills for internal implementation details — inline them.
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- **Write bash pseudo-code in skills.** Skills are Claude instructions, not shell scripts. Use one-liner commands or lib script references, not multi-line bash blocks.
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- **Duplicate protocol definitions.** Finding format lives in `check-phase`. Routing table lives in `act-phase`. Shadow detection lives in `shadow-detection`. One source of truth per concept.
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- **Skip the Check phase** in PDCA cycles. It's the quality gate.
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- **Change archetype personalities** without updating all referencing skills and agent definitions.
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- **Use ArcheFlow for trivial tasks.** Single-file fixes, config changes, questions — just do them directly.
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- **Let skills exceed ~200 lines.** If a skill is growing past this, it probably needs splitting or the content belongs in a lib script.
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### Skill Writing Rules
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1. **Frontmatter**: `name` (kebab-case), `description` (one-liner + `<example>` tags for user-invocable skills)
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2. **Structure**: Imperative voice. Lead with what to do, not why. Tables > prose. Steps > paragraphs.
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3. **Agent templates**: Keep Agent() spawn templates concise. Include only the prompt, subagent_type, and isolation mode.
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4. **Cross-references**: Use `archeflow:<skill-name>` backtick syntax to reference other skills. Avoid circular dependencies.
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5. **Bash commands**: One-liners only in skills. Multi-step logic belongs in `lib/` scripts.
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### Cost Awareness
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- Prefer cheap models (haiku) for analytical tasks (validation, diff scoring)
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- Use capable models (sonnet/opus) for creative tasks (writing, complex design)
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- Budget enforcement via `cost-tracking` skill and `.archeflow/config.yaml`
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- Track token spend per agent in events for post-run analysis
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### Git Rules
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- Signing: `git config gpg.format ssh`, key at `~/.ssh/id_ed25519_dev.pub`
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- Push: `GIT_SSH_COMMAND="ssh -i /home/c/.ssh/id_ed25519_dev -o IdentitiesOnly=yes" git push origin main`
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- Conventional commits: `feat:`, `fix:`, `chore:`, `docs:`, `refactor:`
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- No Co-Authored-By trailers
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- All work on worktree branches until explicitly merged
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- Merges use `--no-ff` (individually revertable)
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## Dogfooding
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When using ArcheFlow to develop ArcheFlow itself:
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- Log observations to `.archeflow/memory/lessons.jsonl`
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- Note friction points, shadow false positives, skill gaps
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- Test skill changes with `/af-run --dry-run` before committing
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