modernize-assess

modernize-assess is a skill for Claude Code from MinhThang1009/dotclaude. It costs 51 tokens per session (1,738 once invoked), scanned A, original, MIT.

An assessment process for measuring a legacy system’s size, complexity, dependency freshness, technical debt, and modernization effort. A legacy system is older software that may be costly or risky to change.

In plain words
What is it for?
Use it to assess one system or several systems, collect code and dependency metrics, estimate effort with COCOMO-II, and create a portfolio heat map.
Why use it?
It turns a broad modernization project into comparable evidence that can support sequencing and planning decisions.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions subagents; positional $N argument.

Part of the code-modernization plugin — 7 skills, 5 agents shipped together

Good fit Use it to assess one system or several systems, collect code and dependency metrics, estimate effort with COCOMO-II, and create a portfolio heat map.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/minhthang1009/dotclaude/modernize-assess
Install

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.

Any agent
npx skills add MinhThang1009/dotclaude --skill modernize-assess
Clone the repo
git clone --depth 1 https://github.com/MinhThang1009/dotclaude

Made for: Claude Code.

Or install code-modernization, the plugin that ships this one along with the rest of its 7 skills, 5 agents.

Wrote 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.

agentmods badge for modernize-assess

README.md
[![agentmods](https://agentmods.dev/badge/skills/minhthang1009/dotclaude/modernize-assess/github.svg)](https://agentmods.dev/skills/minhthang1009/dotclaude/modernize-assess)
Your own site
<a href="https://agentmods.dev/skills/minhthang1009/dotclaude/modernize-assess"><img src="https://agentmods.dev/badge/skills/minhthang1009/dotclaude/modernize-assess/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.

agentmods 80×15 button for modernize-assess

Your own site · 80×15
<a href="https://agentmods.dev/skills/minhthang1009/dotclaude/modernize-assess"><img src="https://agentmods.dev/badge/skills/minhthang1009/dotclaude/modernize-assess.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,738 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.01738
Opus 5 $0.00026 $0.00869
Sonnet 5 $0.00010 $0.00348
Haiku 4.5 $0.00005 $0.00174

Measured 10d ago against content hash 117899f0f7b6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

modernize-assess 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 10d 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.

plugins/code-modernization/skills/modernize-assess/SKILL.md · 163 lines

How it starts

The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Mode select. If $ARGUMENTS starts with --portfolio, run Portfolio mode against the directory that follows. Otherwise run Single-system mode against legacy/$1.


Portfolio mode (--portfolio <parent-dir>)

Sweep every immediate subdirectory of the parent dir and produce a heat-map a steering committee can use to sequence a multi-year program.

Step P1 — Per-system metrics

For each subdirectory <sys>:

cloc --quiet --csv <parent>/<sys>          # LOC by language
lizard -s cyclomatic_complexity <parent>/<sys> 2>/dev/null | tail -1

If cloc/lizard are not installed, fall back to scc <parent>/<sys> (LOC + complexity) or find + wc -l grouped by extension, and estimate complexity by counting decision keywords per file. Note which tool you used.

Capture: total SLOC, dominant language, file count, mean & max cyclomatic complexity (CCN). For dependency freshness, locate the manifest (package.json, pom.xml, *.csproj, requirements*.txt, copybook dir) and note its age / pinned-version count.

Step P2 — COCOMO-II effort

Compute person-months per system using COCOMO-II basic: PM = 2.94 × (KSLOC)^1.10 (nominal scale factors). Show the formula and inputs so the figure is defensible, not a guess.

Step P3 — Documentation coverage

For each system, count source files with vs without a header comment block, and list architecture docs present (README, docs/, ADRs). Report coverage % and the top undocumented subsystems.

Step P4 — Render the heat-map

Write analysis/portfolio.html (dark #1e1e1e bg, #d4d4d4 text, #cc785c accent, system-ui font, all CSS inline). One row per system; columns: System · Lang · KSLOC · Files · Mean CCN · Max CCN · Dep Freshness · Doc Coverage % · COCOMO PM · Risk. Color-grade the PM and Risk cells (green→amber→red). Below the table, a 2-3 sentence sequencing recommendation: which system first and why.

Then stop. Tell the user to open analysis/portfolio.html.


Single-system mode

Read the full file on GitHub · 163 lines

Changes

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.

  1. 10d ago First seen · 163 lines · 51 tokens per session scan A 117899f0f7b6

Subscribe to this mod's changes

modernize-assess is a skill published in the GitHub repository MinhThang1009/dotclaude (20 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 1,738 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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…

microsoft/ai-agents-for-beginners · 200 tokens

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…

vercel/next.js · 95 tokens

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…

vercel/next.js · 83 tokens

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…

vercel/next.js · 170 tokens

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…

vercel/next.js · 103 tokens