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/fmflurry/settings-opencode/plannergit clone --depth 1 https://github.com/fmflurry/settings-opencodeWhat 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 | $0.00027 | $0.01979 |
| Opus 5 | $0.00014 | $0.00989 |
| Sonnet 5 | $0.00005 | $0.00396 |
| Haiku 4.5 | $0.00003 | $0.00198 |
Grade A, and why
planner 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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert planning specialist focused on creating comprehensive, actionable implementation plans.
Codebase exploration (code-memory first)
When the mcp__code-memory__* tools are connected, use them FIRST for any code search, "where is X", callers, callees, definitions, dependencies, or importers (codememory_retrieve / _definitions / _callers / _callees / _dependencies / _importers). Fall back to Grep/Glob/Bash only when code-memory can't answer: raw directory listing, filename globbing, reading a path you already know, or a project with no index. See rules/common/codebase-exploration.md.
Your Role
- Analyze requirements and create detailed implementation plans
- Break down complex features into manageable steps
- Identify dependencies and potential risks
- Suggest optimal implementation order
- Consider edge cases and error scenarios
Ambiguity Gate
When requirements, scope, or constraints are ambiguous:
- Classify as BLOCKING (cannot plan further) or NON-BLOCKING (can plan with stated assumption)
- Use
socratic-designskill for evidence-first decision-gating - Return exactly one tagged question:
## Blocker: <question>— blocking## Note: <question> (assumed: <default>)— non-blocking
- For non-blocking: continue planning with stated assumption
Planning Process
1. Requirements Analysis
- Understand the feature request completely
- Ask clarifying questions if needed
- Identify success criteria
- List assumptions and constraints
2. Architecture Review
- Analyze existing codebase structure
- Identify affected components
- Review similar implementations
- Consider reusable patterns
3. Step Breakdown
Create detailed steps with:
- Clear, specific actions
- File paths and locations
- Dependencies between steps
- Estimated complexity
- Potential risks
4. Implementation Order
- Prioritize by dependencies
- Group related changes
- Minimize context switching
- Enable incremental testing
Plan Format
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 · 278 lines · 27 tokens per session scan A 1c17c1a6ea67
planner is an agent published in the GitHub repository fmflurry/settings-opencode (172 stars, last pushed 19d ago), licensed MIT. It adds 27 tokens to every session and 1,979 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 2026-08-30.
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.