Nw Command Design PatternsSAFE
AI agents that guide you from idea to working code, with you in control at every step.
Overview
AI agents that guide you from idea to working code, with you in control at every step.
1a864ec81cdeOBSERVED · 2026-10-14What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: nw-command-design-patterns
description: Best practices for command definition files - size targets, declarative template, anti-patterns, and canonical examples based on research evidence
user-invocable: false
disable-model-invocation: true
---
# Command Design Patterns
## The Forge Model (Gold Standard)
`forge.md` at 40 lines is the reference dispatcher. Contains: header (wave, agent, overview) | Agent invocation (name + command + config) | Success criteria (checklist) | Next wave handoff | Expected outputs. Every dispatcher should aspire to this pattern.
## Command Categories
| Category | Description | Size Target | Examples |
|----------|-------------|-------------|----------|
| Simple | Direct action, minimal delegation | 40-80 lines | forge, start, version, git |
| Dispatcher | Delegates to one agent with context | 40-150 lines | research, review, execute |
| Orchestrator | Coordinates multiple agents/phases | 100-300 lines | develop, document |
## Declarative Command Template
Commands declare WHAT, not HOW. The agent knows how to do its job.
```markdown
# DW-{NAME}: {Title}
**Wave**: {WAVE_NAME}
**Agent**: {persona} ({agent-id})
## Overview
One paragraph: what this command does and when to use it.
## Context Files Required
- {path} - {why needed}
## Agent Invocation
@{agent-id}
Execute \*{command} for {parameters}.
**Context Files:**
- {files the orchestrator reads and passes}
**Configuration:**
- {key}: {value} # {comment}
## Success Criteria
- [ ] {measurable outcome}
- [ ] {quality gate}
## Next Wave
**Handoff To**: {next wave or workflow step}
**Deliverables**: {what this command produces}
# Expected outputs:
# - {file paths}
```
## Size Targets and Evidence
Research (Chroma Research, Anthropic context engineering): focused prompts (~300 tokens) outperform full prompts (~113k tokens) | Claude shows most pronounced performance gap | Information buried mid-prompt gets deprioritized ("Lost in the Middle") | Opus 4.6 is proactive/self-directing; verbose instructions cause overtriggering
Targets: Dispatchers 40-150 lines | Orchestrators 100-300 lines | Current average 437 lines; target under 150
## The Duplication Triangle
Commands duplicate content in three directions, all waste tokens:
1. **Command-to-Command**: Orchestrator briefings, agent registries, parameter parsing repeated in 5-12 files (~620 lines waste)
2. **Command-to-Agent**: Domain knowledge belonging in agents (~1,300 lines waste). Examples: TDD phases in execute.md, DIVIO templates in document.md, refactoring hierarchies in refactor.md
3. **Command-to-Self**: develop.md embeds other commands inline (~1,000 lines)
Fix: Extract shared content to preamble skill. Move domain knowledge to agents. Have orchestrators reference sub-commands.
## Anti-Patterns
| Anti-pattern | Impact | Fix |
|---|---|---|
| Procedural overload | Step-by-step for capable agents wastes tokens, "lost in the middle" | Declare goal + constraints, let agent apply methodology |
| Duplicated briefings | Same orchestrator constraints in every command (30-80 lines each) | Extract to shared preamble, reference once |
| Embedded domain knowledge | Refactoring hierarchies, review criteria, TDD cycles in commands | Move to agent definitions or skills |
| Aggressive language | "CRITICAL/MANDATORY/MUST" causes overtriggering in Opus 4.6 | Direct statements without emphasis markers |
| Example overload | 50+ lines of JSON examples | 2-3 canonical examples suffice |
| Inline validation logic | Prompt template validation in command text | Platform/hook responsibility |
| Dead code | Deprecated formats, aspirational metrics, old signatures | Remove; version control preserves history |
| Verbose JSON state examples | 200+ lines of unused JSON | Show actual format (pipe-delimited), 3 examples max |
## When Commands Should Contain Logic vs Delegate
**Contain in command** (declarative):
1. Which agent to invoke
2. What context files to read/pass
3. Success criteria and quality gates
4. Next wave handoff
**Delegate to agent**:
1. Methodology (TDD phases, review criteria, refactoring levels)
2. Domain-specific templates/schemas
3. Tool-specific config (cosmic-ray, pytest)
4. Quality assessment rubrics
Rule: if content describes HOW the agent does its work, it belongs in agent definition or skill, not command.
## Canonical Examples
### Example 1: Minimal Dispatcher (forge.md pattern, ~40 lines)
```markdown
# DW-FORGE: Create Agent (V2)
**Wave**: CROSS_WAVE
**Agent**: Zeus (nw-agent-builder)
## Overview
Create a new agent using the research-validated v2 approach.
## Agent Invocation
@nw-agent-builder
Execute \*forge to create {agent-name} agent.
**Configuration:**
- agent_type: specialist | reviewer | orchestrator
## Success Criteria
- [ ] Agent definition under 400 lines
- [ ] 11-point validation checklist passes
- [ ] 3-5 canonical examples included
## Next Wave
**Handoff To**: Agent installation and deployment
**Deliverables**: Agent specification file + Skill files
```
### Example 2: Medium Dispatcher with Context (~80 lines)
```markdown
# DW-RESEARCH: Evidence-Driven Research
**Wave**: CROSS_WAVE
**Agent**: Nova (nw-researcher)
## Overview
Execute systematic evidence-based research with source verification.
## Orchestration: Trusted Source Config
Read .nwave/trusted-source-domains.yaml at orchestration time, embed inline in prompt.
## Agent Invocation
@nw-researcher
Execute \*research on {topic} [--embed-for={agent-name}].
**Configuration:**
- research_depth: detailed
- output_directory: docs/research/
## Success Criteria
- [ ] All sources from trusted domains
- [ ] Cross-reference performed (3+ sources per major claim)
- [ ] Research file created in docs/research/
## Next Wave
**Handoff To**: Invoking workflow
**Deliverables**: Research document + optional embed file
```
### Example 3: Orchestrator (~200 lines)
Coordinates multiple phases without embedding agent knowledge:
```markdown
# Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-command-design-patterns.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-14 | 1a864ec81cde | SAFE | B | 89 | first audit |
Questions
What does the Nw Command Design Patterns skill do?
AI agents that guide you from idea to working code, with you in control at every step.
Is Nw Command Design Patterns safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Nw Command Design Patterns access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.