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 skills add jdrhyne/agent-skills --skill plannergit clone --depth 1 https://github.com/jdrhyne/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jdrhyne/agent-skills/planner)<a href="https://agentmods.dev/skills/jdrhyne/agent-skills/planner"><img src="https://agentmods.dev/badge/skills/jdrhyne/agent-skills/planner.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00035 | $0.00896 |
| Opus 5 | $0.00017 | $0.00448 |
| Sonnet 5 | $0.00007 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00090 |
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 8d ago.
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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planner
Create structured, orchestrator-ready plans for multi-task projects.
Source: Adapted from am-will's codex-skills workflow patterns Pairs with: task-orchestrator skill for follow-on implementation
Quick Start
Follow this process:
- Phase 0: Clarify requirements (ask up to 5 targeted questions)
- Phase 1: Research & understand the codebase
- Phase 2: Create detailed plan with sprints, tasks, acceptance criteria
- Phase 3: Subagent review of the plan
- Phase 4: Return the plan in markdown and save a file only if the user asked for a persisted artifact
Key Principles
Task Atomicity
Each task must be:
- Atomic and committable — small, independent pieces of work
- Specific and actionable — not vague
- Testable — include tests or validation method
- Located — include file paths and code locations
Bad vs Good Task Breakdown
❌ Bad: "Implement third-party sign-in"
✓ Good:
- "Add sign-in config to environment variables"
- "Install and configure the required authentication package"
- "Create sign-in callback route handler in src/routes/auth.ts"
- "Add the sign-in button to the login UI"
Sprint Structure
Each sprint must:
- Result in a demoable, runnable, testable increment
- Build on prior sprint work
- Include clear demo/verification checklist
Plan Template
# Plan: [Task Name]
**Generated**: [Date]
**Estimated Complexity**: [Low/Medium/High]
## Overview
[Brief summary of what needs to be done and the general approach]
## Prerequisites
- [Dependencies or requirements that must be met first]
- [Tools, libraries, or access needed]
- [Tooling limitations, e.g., browser relay/CDP restrictions]
## Sprint 1: [Sprint Name]
**Goal**: [What this sprint accomplishes]
**Demo/Validation**:
- [How to run/demo this sprint's output]
- [What to verify]
### Task 1.1: [Task Name]
- **Location**: [File paths or components involved]
- **Description**: [What needs to be done]
- **Perceived Complexity**: [1-10]
- **Dependencies**: [Any previous tasks this depends on]
- **Acceptance Criteria**:
- [Specific, testable criteria]
- **Validation**:
- [Test(s) or alternate validation steps]
### Task 1.2: [Task Name]
[...]
## Sprint 2: [Sprint Name]
[...]
## Testing Strategy
- [How to test the implementation]
- [What to verify at each sprint]
## Potential Risks
- [Things that could go wrong]
- [Mitigation strategies]
## Rollback Plan
- [How to undo changes if needed]
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.
- 8d ago First seen · 135 lines · 35 tokens per session scan A 4c893f667e21
planner is a skill published in the GitHub repository jdrhyne/agent-skills (241 stars, last pushed 9d ago), licensed MIT. It adds 35 tokens to every session and 896 once invoked, about $0.0002 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 skills, from other repositories
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.
autonomous-run
Prepare, start, inspect, resume, or stop a finite local overnight coding run after a human has accepted a Wayfinder terminal spec; coordinates a declared Claude/Codex maker and independent checker without pushing, merging, or writing to external systems.
adaptive-quality
Per-task execution profile selection based on complexity in Balanced quality mode.
auto-qa
QAMESH project QA mesh — plan, run, report, and publish deterministic QA evidence.
experiment
Experiment loop for iterative metric-driven code optimization using XLOOP.
agent-presets
Domain-specific agent configurations for different project types.