Borrowing it
Nothing to install: this file belongs to me2resh/apexyard. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/me2resh/apexyard/main/.claude/skills/tech-vision/SKILL.mdgit clone --depth 1 https://github.com/me2resh/apexyardWrote 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/me2resh/apexyard/tech-vision)<a href="https://agentmods.dev/skills/me2resh/apexyard/tech-vision"><img src="https://agentmods.dev/badge/skills/me2resh/apexyard/tech-vision/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/me2resh/apexyard/tech-vision"><img src="https://agentmods.dev/badge/skills/me2resh/apexyard/tech-vision.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 157 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- medium MCP Rug Pull · line 181 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00024 | $0.03904 |
| Opus 5 | $0.00012 | $0.01952 |
| Sonnet 5 | $0.00005 | $0.00781 |
| Haiku 4.5 | $0.00002 | $0.00390 |
Grade A, and why
tech-vision 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 today.
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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing rule
When this skill writes a durable artifact, read .claude/rules/writing-standard.md. Use the controlled technical writing profile.
/tech-vision — Interactive Architecture Vision Author
Walks the operator through the existing templates/architecture/vision.md (shipped in #224) section by section — instead of leaving the operator staring at an empty template — so the load-bearing sections (Anti-scope, Current-vs-Target, Migration path) actually get filled in honestly rather than left as aspirational stubs.
The output is a fully populated vision.md ready to commit. Markdown-only — the single graphical element (target-state C4 L1) renders inline via Mermaid on GitHub. No HTML, no SVG, no build step.
Why
/tech-visionand not/vision? In a multi-stakeholder portfolio the word "vision" collides with product / company vision documents that Heads of Product and CEOs author in a different shape. Thetech-prefix disambiguates this skill as the technical / architecture vision author. The output filename (vision.md) and template path (templates/architecture/vision.md) stay unchanged — only the slash-command carries the prefix.
Design rationale + sub-decisions: AgDR-0028.
| Skill | Role |
|---|---|
/tech-vision (this skill) |
Authors the target-state + migration path — north-star architecture, prose + bulleted horizons |
/c4 |
Static topology — current-state system + container diagrams |
/dfd |
Data flow — trust boundaries + data classifications (input to /threat-model) |
/sequence (template) |
Request-flow walkthroughs (auth handshake, payment flow) |
Path resolution
Read the registry path via portfolio_registry, the per-project docs dir via portfolio_projects_dir, and the template path via portfolio_resolve_template — all from .claude/hooks/_lib-portfolio-paths.sh. Source the helper at the top of any bash block that touches those paths:
What ships with it
3 files 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.
- today Changed · +4 lines c695f227faf2
- 7d ago First seen · 251 lines · 24 tokens per session scan A 0a03c16a9330
tech-vision is a skill published in the GitHub repository me2resh/apexyard (498 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 3,904 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 2026-09-03.
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…