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 MinhThang1009/dotclaude --skill modernize-reimaginegit clone --depth 1 https://github.com/MinhThang1009/dotclaudeWrote 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/minhthang1009/dotclaude/modernize-reimagine)<a href="https://agentmods.dev/skills/minhthang1009/dotclaude/modernize-reimagine"><img src="https://agentmods.dev/badge/skills/minhthang1009/dotclaude/modernize-reimagine/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/minhthang1009/dotclaude/modernize-reimagine"><img src="https://agentmods.dev/badge/skills/minhthang1009/dotclaude/modernize-reimagine.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 | $0.00058 | $0.00888 |
| Opus 5 | $0.00029 | $0.00444 |
| Sonnet 5 | $0.00012 | $0.00178 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
modernize-reimagine 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reimagine legacy/$1 as: $2
This is not a port — it's a rebuild from extracted intent. The legacy system becomes the specification source, not the structural template. This command orchestrates a multi-agent team with explicit human checkpoints.
Phase A — Specification mining (parallel agents)
Spawn concurrently and show the user that all three are running:
-
business-rules-extractor — "Extract every business rule from legacy/$1 into Given/When/Then form. Output to a structured list I can parse."
-
legacy-analyst — "Catalog every external interface of legacy/$1: inbound (screens, APIs, batch triggers, queues) and outbound (reports, files, downstream calls, DB writes). For each: name, direction, payload shape, frequency/SLA if discernible."
-
legacy-analyst — "Identify the core domain entities in legacy/$1 and their relationships. Return as an entity list + Mermaid erDiagram."
Collect results. Write analysis/$1/AI_NATIVE_SPEC.md containing:
- Capabilities (what the system must do — derived from rules + interfaces)
- Domain Model (entities + erDiagram)
- Interface Contracts (each external interface as an OpenAPI fragment or AsyncAPI fragment)
- Non-functional requirements inferred from legacy (batch windows, volumes)
- Behavior Contract (the Given/When/Then rules — these are the acceptance tests)
Phase B — HITL checkpoint #1
Present the spec summary. Ask the user one focused question: "Which of these capabilities are P0 for the reimagined system, and are there any we should deliberately drop?" Wait for the answer. Record it in the spec.
Phase C — Architecture (single agent, then critique)
Design the target architecture for "$2":
- Mermaid C4 Container diagram
- Service boundaries with rationale (which rules/entities live where)
- Technology choices with one-line justification each
- Data migration approach from legacy stores
Then spawn architecture-critic: "Review this proposed architecture for
$2 against the spec in analysis/$1/AI_NATIVE_SPEC.md. Identify over-engineering,
missed requirements, scaling risks, and simpler alternatives." Incorporate
the critique. Write the result to analysis/$1/REIMAGINED_ARCHITECTURE.md.
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.
- 11d ago First seen · 85 lines · 58 tokens per session scan A 2f3d81747304
modernize-reimagine is a skill published in the GitHub repository MinhThang1009/dotclaude (20 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 888 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.
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