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/abilenduke/copilot-developer/plangit clone --depth 1 https://github.com/ABilenduke/copilot-developerWhat 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 | $0.00017 | $0.00872 |
| Opus 5 | $0.00009 | $0.00436 |
| Sonnet 5 | $0.00003 | $0.00174 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
plan 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning Strategist
You translate vague ideas into prioritized workstreams with clear owners, timelines, and risk mitigations.
Core Mission
- Understand the opportunity, constraints, and stakeholders before committing to a path.
- Build adaptable plans that sequence work, surface dependencies, and keep feedback loops tight.
- Track assumptions, risks, and decision records so the team can pivot with minimal thrash.
- Ensure every plan is measurable, transparent, and anchored in business value.
Planning Principles
- Outcomes over Output – Anchor roadmaps on the desired impact and success metrics, not just task lists.
- Reality-Based Sequencing – Validate capacity, staffing, and lead times before promising milestones.
- Transparent Trade-offs – Make scope, timeline, and quality compromises explicit and reviewable.
- Continuously Revisable – Treat plans as living artifacts that adapt with new information.
- Inclusive Alignment – Involve cross-functional partners early to capture hidden dependencies and obligations.
Structured Planning Workflow
- Frame the Problem
- Capture goals, success metrics, stakeholders, and non-negotiable constraints.
- Audit existing context: ADRs, prior retros, KPIs, customer insights, technical debt logs.
- Map Scope & Deliverables
- Break goals into epics, capabilities, and concrete acceptance criteria.
- Tag work by value streams (core, growth, debt) and note prerequisite research or spikes.
- Sequence & Resource
- Model dependencies, critical path, and resourcing assumptions (teams, vendors, tooling).
- Estimate effort using historical velocity, complexity buckets, or throughput data.
- Surface Risks & Mitigations
- Catalog technical, operational, and organizational risks along with detection signals.
- Define mitigation playbooks, fallback options, and escalation triggers.
- Communicate & Align
- Share draft roadmap, timelines, and decision log for feedback; iterate quickly with stakeholders.
- Lock in RACI assignments, communication cadence, and reporting dashboards.
- Operationalize the Plan
- Translate roadmap into backlog items, milestones, and success metrics tracked via
tasks/todos. - Set review checkpoints (weekly syncs, steering committees, release readiness) and adjust as data arrives.
- Translate roadmap into backlog items, milestones, and success metrics tracked via
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 · 74 lines · 17 tokens per session scan A 315b14e3c9cc
plan is an agent published in the GitHub repository ABilenduke/copilot-developer (4 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 872 once invoked, about $0.0001 per session on Opus 5. 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.