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 elct9620/ai-coding-skills --skill design-forcesgit clone --depth 1 https://github.com/elct9620/ai-coding-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/elct9620/ai-coding-skills/design-forces)<a href="https://agentmods.dev/skills/elct9620/ai-coding-skills/design-forces"><img src="https://agentmods.dev/badge/skills/elct9620/ai-coding-skills/design-forces/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/elct9620/ai-coding-skills/design-forces"><img src="https://agentmods.dev/badge/skills/elct9620/ai-coding-skills/design-forces.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 | $0.00109 | $0.02190 |
| Opus 5 | $0.00055 | $0.01095 |
| Sonnet 5 | $0.00022 | $0.00438 |
| Haiku 4.5 | $0.00011 | $0.00219 |
Grade A, and why
design-forces 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 2d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Related Skills
- Memo recommends modelling rules explicitly? → domain-modeling
- Memo recommends a layered or partitioned structure? → architecture
- Memo recommends introducing a Strategy / Polymorphism for a recurring variation? → design-patterns
- Memo says open a seam in legacy code first? → refactoring
- Memo flags a trust boundary? → security + schema + deeper testing
Principles (KISS, SOLID, DRY, YAGNI, fail-fast) apply underneath every option. The memo operates one layer above them.
Applicability Rubric
| Condition | Pass |
|---|---|
| Architectural commitment on the table | Question of the form "do we use CA / DDD / pattern X / scaffold?" is live |
| Non-trivial scope | Feature crosses files or layers, or replaces existing behaviour |
| Surrounding code lacks a clear convention | Legacy area or framework-inconsistent code |
| Risk-bearing change | Auth, payments, data integrity, external API, migration |
Apply when: Any condition passes.
What the Memo Produces
A short note for the developer: forces pulling on the decision, 4–6 options drawn from this codebase, framework, and wider repertoire (framework defaults, scaffolds, internal libraries, defer, spike, plus structural choices), trade-offs, and a soft recommendation with the conditions under which it would change. The developer reads and decides; other skills activate based on the chosen direction.
Six Forces (lenses)
| Force | The question this lens asks |
|---|---|
| Delivery pressure | Is the cost of deeper investment recoverable in the time we have? |
| Rule complexity | Are the rules thick enough to deserve explicit modelling? |
| Change rate | Is paying upfront for flexibility cheaper than paying later for rework? |
| Team and language fragmentation | Will more than one team or role describe this in their own words? |
| Blast radius | What breaks if this is wrong, and how visibly? |
| Code maturity | Does the surrounding code need preparing before this lands cleanly? |
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.
- 2d ago Changed · +16 tokens per session a9b54c62577e
- 8d ago First seen · 206 lines · 93 tokens per session scan A 4f16e12323ac
design-forces is a skill published in the GitHub repository elct9620/ai-coding-skills (5 stars, last pushed 3d ago), licensed Apache-2.0. It adds 109 tokens to every session and 2,190 once invoked, about $0.0005 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 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…
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…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…