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 zeeshanhanif/agentic-sdlc-kit --skill project-scaffoldinggit clone --depth 1 https://github.com/zeeshanhanif/agentic-sdlc-kitWrote 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/zeeshanhanif/agentic-sdlc-kit/project-scaffolding)<a href="https://agentmods.dev/skills/zeeshanhanif/agentic-sdlc-kit/project-scaffolding"><img src="https://agentmods.dev/badge/skills/zeeshanhanif/agentic-sdlc-kit/project-scaffolding.svg" alt="Measured on agentmods" 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 | $0.00196 | $0.02191 |
| Opus 5 | $0.00098 | $0.01095 |
| Sonnet 5 | $0.00039 | $0.00438 |
| Haiku 4.5 | $0.00020 | $0.00219 |
Grade A, and why
project-scaffolding 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Scaffolding
The first skill in the pipeline whose output is a running system, not a document. It takes the architecture and the plan and produces the walking skeleton as a real repo: generated structure, wired end-to-end, foundations in place, verified by execution. It ends where deployment begins — everything is deploy-ready; the first actual deploy is the user's step.
Three principles govern it:
- The stack is an input, never a decision. The architecture's ADRs already chose the technology. This skill reads the stack and refuses to relitigate it. Gaps the generators need answered (package manager, monorepo tool) are elicited — and flagged as candidate architecture amendments.
- Official generators first, manual structure second. Every serious ecosystem ships an official project initializer that encodes current best-practice structure. Discover it, verify it against live docs (never trust memory for CLI names/flags — live reality always wins), run it for real, then customize. Hand-build only where no generator exists, following that ecosystem's documented conventions. This keeps the skill self-updating: framework conventions change, generators change with them, the skill inherits it.
- What this skill owns is the stack-independent layer: the walking-skeleton wiring between units, module-boundary enforcement, the design system wired into the UI shell, pipeline conventions carried into the repo, and empirical verification. Generators create isolated apps; this skill makes them a system.
Inputs
Defaults below; user-provided paths win. Each degrades independently.
- Architecture —
docs/architecture.md(primary). The stack per container (from ADRs), the containers themselves (each becomes a deployable unit), module boundaries, deployment target, cross-cutting concepts. Without it the skill can still run by eliciting the stack directly, but say plainly that fidelity suffers — this skill is designed to realize an architecture. - Implementation plan —
docs/implementation-plan.md. The walking-skeleton spec (what's real, what's stubbed, done-when) and the engineering-foundations checklist. If missing, derive a minimal skeleton from the architecture and confirm it with the user. - UX foundations —
docs/ux-foundations.md,docs/design.md,docs/tokens.json. The UI-shell contract: tokens wired into each frontend surface's token mechanism, design.md referenced from AGENTS.md file. - SRS —
docs/srs.md, light touch: constraints (§2.5) affecting tooling.
What ships with it
4 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.
- 8d ago First seen · 189 lines · 196 tokens per session scan A bac19f4f8cf6
project-scaffolding is a skill published in the GitHub repository zeeshanhanif/agentic-sdlc-kit (5 stars, last pushed 12d ago), licensed Apache-2.0. It adds 196 tokens to every session and 2,191 once invoked, about $0.0010 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…