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 richardcb/oh-my-gemini --skill technical-planninggit clone --depth 1 https://github.com/richardcb/oh-my-geminiWrote 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/richardcb/oh-my-gemini/technical-planning)<a href="https://agentmods.dev/skills/richardcb/oh-my-gemini/technical-planning"><img src="https://agentmods.dev/badge/skills/richardcb/oh-my-gemini/technical-planning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/richardcb/oh-my-gemini/technical-planning"><img src="https://agentmods.dev/badge/skills/richardcb/oh-my-gemini/technical-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00050 | $0.02093 |
| Opus 5.5 | $0.00020 | $0.00837 |
| Sonnet 5.5 | $0.00010 | $0.00419 |
| Haiku 4.5 | $0.00005 | $0.00209 |
Grade A, and why
technical-planning 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 1mo 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Planning Skill
Goal
Create a detailed, step-by-step technical plan in Markdown format, based on an existing Product Requirements Document (PRD). The plan must be a clear, actionable guide for implementation.
Activation Triggers
- User has a PRD and wants to create a technical plan
- User asks to "plan", "break down", or "create tasks" for a feature
- After PRD creation, before implementation begins
Process
1. Receive PRD Reference
The user provides a specific PRD file or feature name.
2. Load Context
# Find the PRD
find . -name "*prd*.md" | head -10
# Read project tech stack
cat conductor/tech-stack.md 2>/dev/null || cat GEMINI.md 2>/dev/null | head -50
# Check existing patterns
find src -name "*.ts" -o -name "*.js" 2>/dev/null | head -20
3. Analyze PRD & Codebase
Read the PRD's:
- Functional requirements
- User stories
- Business invariants
- Failure states
Review the codebase for:
- Architectural patterns
- Existing similar features
- Shared components to reuse
- Files that need modification
4. Structure the Plan
Break down into logical phases following this order:
Phase 1: Data Layer (Foundation)
- Database schema changes
- Migrations
- Shared TypeScript types
- Why first: Data structures must exist before code references them
Phase 2: Backend API
- API endpoints
- Validation (Zod schemas)
- Error handling
- Why second: Backend must work before frontend can consume it
Phase 3: Frontend Implementation
- React components
- State management
- UI integration
- Why third: Connect UI to working APIs
Phase 4: Review & Finalize
- Verification against PRD
- Testing (unit, integration, E2E)
- Documentation updates
5. Apply AI Guardrails
Every plan must include mitigations for common AI-generated issues:
Resource Efficiency
For any data-fetching task, include sub-tasks for:
- Batching multiple requests
- Pagination for large datasets
- Caching where appropriate
- Avoiding N+1 query patterns
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.
- 1mo ago First seen · 325 lines · 50 tokens per session scan A 56afb40ffa15
technical-planning is a skill published in the GitHub repository richardcb/oh-my-gemini (18 stars, last pushed 7mo ago), licensed MIT. It adds 50 tokens to every session and 2,093 once invoked, about $0.0002 per session on Opus 5.5. 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.
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