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 tmchow/tmc-marketplace --skill tech-planninggit clone --depth 1 https://github.com/tmchow/tmc-marketplaceWrote 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/tmchow/tmc-marketplace/tech-planning)<a href="https://agentmods.dev/skills/tmchow/tmc-marketplace/tech-planning"><img src="https://agentmods.dev/badge/skills/tmchow/tmc-marketplace/tech-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/tmchow/tmc-marketplace/tech-planning"><img src="https://agentmods.dev/badge/skills/tmchow/tmc-marketplace/tech-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 | $0.00053 | $0.02545 |
| Opus 5 | $0.00026 | $0.01273 |
| Sonnet 5 | $0.00011 | $0.00509 |
| Haiku 4.5 | $0.00005 | $0.00254 |
Grade A, and why
iterative:tech-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 9d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Technical Plan
Turn a PRD or set of requirements into a structured, executable implementation plan. Write as if the implementer has zero context for the codebase — document the decisions, the reasoning, and enough detail that they can start working without asking clarifying questions.
The plan captures WHAT to build and WHERE. The implementer writes the actual code.
When to Use
- After
iterative:brainstormingskill is complete - When clear requirements exist and need an implementation plan
- Can be invoked standalone with existing requirements
If requirements are vague and no PRD exists, offer to start with iterative:brainstorming skill first.
Note on scope: Quick scope skips tech-planning entirely — the user implements directly from the brainstorming conversation. Standard scope may also skip tech-planning if the user chooses to implement directly from brainstorming's summary; tech-planning is invoked only when the user explicitly opts in. Full scope always uses tech-planning. Adapt plan depth to scope: a Standard-scope task doesn't need 5 parent tasks with 3 subtasks each — a flat checklist of 3-5 steps is sufficient. Full scope uses the complete structured plan format. Tech-planning is where the HOW lives — file paths, architecture decisions, implementation steps, test scenarios. This complements brainstorming's WHAT (requirements, scope, decisions).
Key Principles
- Understand before structuring — Explore the codebase and ask questions before writing the plan
- Decisions, not code — Capture architecture choices, query strategies, component boundaries, trade-offs. Leave method names, signatures, and implementation code to the implementer
- Concrete test scenarios — Specific inputs, expected outputs, edge cases to cover. Not full test code, not "test that it works"
- Test files are explicit — Every feature subtask must include the test file path in its
**Files:**field. Test scenarios without a target test file get skipped during implementation - Right-sized subtasks — Scoped to a single atomic commit, typically touching 2-3 files. Not too big (5+ files, multiple unrelated changes), not too small (single line, no meaningful test)
- Dependencies clear — Explicit ordering of what depends on what
- Verification built-in — Each subtask has a way to confirm it works
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 136 lines · 53 tokens per session scan A 5ea233c679d8
iterative:tech-planning is a skill published in the GitHub repository tmchow/tmc-marketplace (22 stars, last pushed 6mo ago), licensed MIT. It adds 53 tokens to every session and 2,545 once invoked, about $0.0003 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…