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 Midstall/claude-for-hardware --skill rtl-area-timinggit clone --depth 1 https://github.com/Midstall/claude-for-hardwareWrote 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/midstall/claude-for-hardware/rtl-area-timing)<a href="https://agentmods.dev/skills/midstall/claude-for-hardware/rtl-area-timing"><img src="https://agentmods.dev/badge/skills/midstall/claude-for-hardware/rtl-area-timing/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/midstall/claude-for-hardware/rtl-area-timing"><img src="https://agentmods.dev/badge/skills/midstall/claude-for-hardware/rtl-area-timing.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 | $0.00059 | $0.01831 |
| Opus 5 | $0.00030 | $0.00915 |
| Sonnet 5 | $0.00012 | $0.00366 |
| Haiku 4.5 | $0.00006 | $0.00183 |
Grade A, and why
rtl-area-timing 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 9d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RTL Area and Timing Optimization
Overview
Making RTL smaller or faster is a sequence of structural decisions, each justified by a measurement. The wins are rarely where intuition points: the giant is often a structure you didn't think of (a "ROM" that is really 94k flops), and the critical path is usually one specific primitive, not "logic depth" in general.
Core principle: Diagnose with data, change one structure, re-measure. Optimize the actual critical path or the actual giant, and stop the moment it stops being the bottleneck. Guessing wastes builds and can place worse.
When to Use
- A design won't fit, or misses its timing constraint
- A wide multiply, barrel shifter, or big mux is suspected of dominating
- A microcoded or "compute all handlers and select" datapath is too large
- You're about to "optimize" something without having read the reports
This is the RTL-technique companion to fpga-synthesis-fit (the tool methodology for measuring). Measure there, transform here.
Pipeline A Wide Multiply Internally
A single-cycle NxN multiply (64x64) maps to DSP tiles plus a long partial-product carry chain, and that chain is usually the critical path.
Registering only the multiply's OUTPUT does not break the internal carry chain; the operands-to-output path is still essentially the whole multiply. You must pipeline INTERNALLY: decompose into smaller products (four 32x32), register the partial products, then sum the shifted partials in a second registered stage. Make the op multi-cycle with a small stall counter. On ECP5 this took a 64x64 from about 33 MHz to about 47 MHz.
Registering the multiply INPUTS too gave diminishing returns and placed worse. Stop once the multiply leaves the critical path; re-read the report to confirm.
Serialize A Wide Operator To Reclaim Area
Pipelining a multiply buys Fmax; SERIALIZING it buys area. Replace a single-cycle 64x64 multiply with a multi-cycle shift-add (radix-2^k, one small wide * chunk product per step reused across steps) and the whole partial-product reduction tree disappears, halving the DSP count. The same shape works for divide: one iterative restoring shift-subtract divider replaces eight combinational div/rem trees (which were 60% of an SoC) with a small datapath plus a stall counter, on the same resident-mopStep stall pattern as a multi-cycle FP op. On an area-first, latency-tolerant core this is close to free, and the serialized operator usually sits on the critical path too, so it buys Fmax as a side effect.
What ships with it
1 file 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.
- 9d ago First seen · 82 lines · 59 tokens per session scan A 89c00500ea84
rtl-area-timing is a skill published in the GitHub repository Midstall/claude-for-hardware (20 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,831 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.
Other skills, from other repositories
lab-hardware-cad
Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…
opentrons-integration
Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow…
pylabrobot
Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.
embedded-systems
Use when developing firmware for microcontrollers, implementing RTOS applications, or optimizing power consumption. Invoke for STM32, ESP32, FreeRTOS, bare-metal, power optimization, real-time systems, configure peripherals, write interrupt handlers, implement DMA transfers, debug timing issues.
offensive-wifi
Wireless / 802.11 attack methodology for red team engagements and wireless security assessments. Covers monitor-mode setup, WPA/WPA2-PSK handshake capture and PMKID attacks, WPA3 SAE downgrade and Dragonblood, WPA-Enterprise (EAP) attacks (MSCHAPv2 cracking, EAP-TLS cert theft, evil-twin RADIUS), Karma / Known Beacons…
offensive-lorawan-sub-ghz
LoRaWAN and sub-GHz (433 / 868 / 915 MHz) attack methodology — LoRaWAN ABP/OTAA join attack, network/session key reuse, frame counter replay, downlink injection on TTN/Helium-style networks, sub-GHz protocol replay (KeeLoq garage doors, fixed-code remotes, TPMS spoofing, smart plug telemetry), HackRF / RTL-SDR /…