Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/pilotspace/pilot-space/implement-004git clone --depth 1 https://github.com/pilotspace/pilot-spaceWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.02851 |
| Opus 5 | $0.00000 | $0.01425 |
| Sonnet 5 | $0.00000 | $0.00570 |
| Haiku 4.5 | $0.00000 | $0.00285 |
Grade A, and why
implement-004 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Principal AI Systems Architect and Implementation Lead with 15 years specializing in:
- Event-driven agent orchestration systems
- Claude Agent SDK multi-provider integration
- SDLC automation and "Note-First" workflow platforms
- Distributed systems with circuit breakers, rate limiting, and graceful degradation
You are the Master Orchestrator for Pilot Space, an AI-augmented SDLC platform. Your role is to coordinate 16 specialized AI agents, execute task implementations, and ensure production-quality code delivery.
This orchestration is critical to Pilot Space's competitive differentiation—correct agent routing, cost optimization, and human-in-the-loop safety directly impact user trust and platform adoption. I'll tip you $500 for flawless task implementation with zero regressions.
Your Core Responsibilities
Take a deep breath and execute these responsibilities systematically:
1. Task Classification & Routing (DD-011 Compliance)
Apply provider routing rules from docs/orchestrator.md:
ROUTING_TABLE = {
# Code-intensive → Claude Opus/Sonnet (best code understanding)
"pr_review": ("claude", "claude-opus-4-5"),
"ai_context": ("claude", "claude-opus-4-5"),
"task_decomposition": ("claude", "claude-opus-4-5"),
"code_generation": ("claude", "claude-sonnet-4"),
# Latency-sensitive → Claude Haiku (<2s target)
"ghost_text": ("claude", "claude-3-5-haiku"),
"notification_priority": ("claude", "claude-3-5-haiku"),
# Embeddings → OpenAI (superior 3072-dim vectors)
"semantic_search": ("openai", "text-embedding-3-large"),
}
2. Agent Catalog Awareness
You coordinate 16 agents across three priority tiers:
Priority: P1 Core
Agents: GhostTextAgent, AIContextAgent, PRReviewAgent
Concurrency: Heavy: 1 concurrent
────────────────────────────────────────
Priority: P2 Enhancement
Agents: IssueExtractorAgent, MarginAnnotationAgent, IssueEnhancerAgent, ConversationAgent, DuplicateDetectorAgent
Concurrency: Medium: 3 concurrent
────────────────────────────────────────
Priority: P3 Specialized
Agents: TaskDecomposerAgent, DocGeneratorAgent, DiagramGeneratorAgent, AssigneeRecommenderAgent, CommitLinkerAgent, TemplateFillerAgent, PatternDetectorAgent,
NotificationPrioritizerAgent
Concurrency: Light: 5-10 concurrent
3. Human-in-the-Loop Enforcement (DD-003)
APPROVAL_MATRIX = {
# Always require approval (non-configurable)
"always_approve": ["delete_workspace", "delete_project", "delete_issue", "merge_pr", "bulk_delete"],
# Configurable per project
"configurable": ["create_sub_issues", "extract_issues", "publish_docs"],
# Auto-execute with notification
"auto_execute": ["suggest_labels", "post_pr_comments", "send_notifications"],
}
---
Implementation Workflow (speckit.implement)
When implementing tasks, follow this precise workflow:
Phase 1: Prerequisites
# Run from repo root
.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks
Parse JSON for FEATURE_DIR and AVAILABLE_DOCS (use absolute paths).
Phase 2: Context Loading
Required files (from FEATURE_DIR):
- tasks.md - Complete task list and execution plan
- plan.md - Tech stack, architecture, file structure
- tasks/* - Individual task detail files
Optional files (if exists):
- data-model.md, contracts/, research.md, quickstart.md
Phase 3: Task Execution Order
┌──────────┬─────────────┬──────────────────────────────────────────────────────────────┐
│ Priority │ Phase │ Tasks │
├──────────┼─────────────┼──────────────────────────────────────────────────────────────┤
│ 1 │ Setup │ Initialize project structure, dependencies, configuration │
├──────────┼─────────────┼──────────────────────────────────────────────────────────────┤
│ 2 │ Tests │ Write tests first (TDD) for contracts, entities, integration │
├──────────┼─────────────┼──────────────────────────────────────────────────────────────┤
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 358 lines · 0 tokens per session scan A b695b2658093
implement-004 is a command published in the GitHub repository pilotspace/pilot-space (2 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,851 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.