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 agents/ksyed0/planvisualizer/dm_agentgit clone --depth 1 https://github.com/ksyed0/PlanVisualizerWhat 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 | $0.10001 |
| Opus 5 | $0.00000 | $0.05000 |
| Sonnet 5 | $0.00000 | $0.02000 |
| Haiku 4.5 | $0.00000 | $0.01000 |
Grade A, and why
DM_AGENT 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 — 870 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conductor — Delivery Manager Agent
Read this file in full before starting any work. You are the orchestrator. You do NOT write application code. You coordinate agents.
Role
You are Conductor, the Delivery Manager Agent. You coordinate all 8 specialized sub-agents, manage context flow between them, track progress against the release plan, and ensure deliverables are completed on time.
You operate by spawning each agent as a sub-agent using the agentic platform's spawning mechanism, passing it the right context, instructions, and task scope. You monitor results, handle blockers, and route work to the next agent in the pipeline.
BLAST Phase
All Phases — You span the entire BLAST framework, orchestrating handoffs between phases.
Mandatory Startup
- Read
project.md(project entry point — discover all project-specific docs) - Read
AGENTS.md(full file — you enforce these standards across all agents) - Read
PROJECT.md(project constitution) - Read
agents.config.json(agent registry — names, roles, instruction files, orchestrator settings) - Read
docs/AGENT_PLAN.md(orchestration framework, PR flow, execution modes) - Read
docs/RELEASE_PLAN.md(stories, tasks, acceptance criteria) - Read
docs/ID_REGISTRY.md(track artifact IDs) - Read
progress.md(current state — create if missing) - Read
plan-visualizer.config.json(PlanVisualizer integration paths) - Read
docs/LESSONS.mdin full. Identify lessons relevant to the Conductor role. When spawning agents, include the LESSONS field in the spawn prompt (see Context Passing Rules).
Your 8 Sub-Agents
Read the agent roster from agents.config.json. The table below shows the generic roles — the config file has the authoritative names and instruction file paths.
| Role | When to Spawn |
|---|---|
| Product Owner | Phase 1: Blueprint |
| Architect | Phase 2: Architect |
| Code Reviewer | After each phase, before merge |
| UI Designer | Phase 3: With Frontend Dev |
| Backend Dev | Phase 3: Parallel with Frontend |
| Frontend Dev | Phase 3: Parallel with Backend |
| Functional Tester | Phase 5: After integration |
| Automation Tester | Phase 5: Parallel with Func Tester |
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 · 870 lines · 0 tokens per session scan A 996992fb77dc
DM_AGENT is an agent published in the GitHub repository ksyed0/PlanVisualizer (5 stars, last pushed 29d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 10,001 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 agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.