New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Rtl Release Packager

by exeex in exeex/edge-cores

Generate, compare, validate, and prepare mixed public/private RTL release packages with symbol-obfuscated proprietary RTL and unchanged public RTL dependencies.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Rtl Release Packager

skills CLI
$ npx skills add exeex/edge-cores --skill rtl-release-packager -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install exeex/edge-cores rtl-release-packager --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/exeex/edge-cores.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/rtl-release-packager .claude/skills/rtl-release-packager && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rtl-release-packager
GitHub stars
110
Token cost
~892 tokens
SKILL.md length
326 words
Files
2
Skills in repo
9
Repo updated
First seen
Licence
Apache-2.0

At a glance

Generate, compare, validate, and prepare mixed public/private RTL release packages with symbol-obfuscated proprietary RTL and unchanged public RTL dependencies.

  • Obfuscated Verilog/SystemVerilog releases
  • SKILL.md covers Protect the boundary, Generate a package, Review deterministic output and Validate the public consumer, plus 1 more section
  • Calls git and python3
  • Source-available ASIC simulation packages

What it does

Rtl Release Packager is an agent skill from exeex/edge-cores. Generate, compare, validate, and prepare mixed public/private RTL release packages with symbol-obfuscated proprietary RTL and unchanged public RTL dependencies. Use for encrypted or obfuscated Verilog/SystemVerilog releases, source-available ASIC simulation packages, private-to-public RTL ABI boundaries, edge-e3enc regeneration, deterministic release diffs, or publishing an RTL release submodule without exposing private source or symbol mappings.

Its SKILL.md is about 890 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in AI & LLM Engineering. The repository describes itself as: The shortest path from PyTorch to custom ASICs. The licence is Apache-2.0.

When your agent uses it

  • Obfuscated Verilog/SystemVerilog releases
  • Source-available ASIC simulation packages
  • Private-to-public RTL ABI boundaries
  • Edge-e3enc regeneration

Example prompts

  • “/rtl-release-packager”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 68dc8aa. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Rtl Release Packager loads about 892 tokens when it runs. Until then it costs about 118 tokens; SKILL.md has 326 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~892

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from exeex/edge-cores at commit 68dc8aa, republished under its Apache-2.0 licence (© exeex). 326 words, ~892 tokens.

Download SKILL.mdSave it as .claude/skills/rtl-release-packager/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
rtl-release-packager
description
Generate, compare, validate, and prepare mixed public/private RTL release packages with symbol-obfuscated proprietary RTL and unchanged public RTL dependencies. Use for encrypted or obfuscated Verilog/SystemVerilog releases, source-available ASIC simulation packages, private-to-public RTL ABI boundaries, edge-e3enc regeneration, deterministic release diffs, or publishing an RTL release submodule without exposing private source or symbol mappings.

RTL release packager

Use tools/package_obfuscated_rtl.py for the generic flow and tools/obfuscate_edge_e3.py for the backward-compatible edge-e3 defaults.

Protect the boundary

  • Confirm the user is authorized to read the private RTL before initializing or accessing a private submodule.
  • Never add a symbol mapping, private source path contents, credentials, or unlicensed RTL to the public repository.
  • Treat obfuscation as reverse-engineering resistance, not cryptographic secrecy.
  • Require a clean output submodule before overwriting it. Stop if it contains unrelated edits.
  • Do not commit or push the output submodule or parent gitlink unless requested.

Generate a package

Resolve every private root, the public root, production filelist, SoC boundary, license, output submodule, top module, and SoC core module. The maintained edge-cores defaults obfuscate both src/edge-e3 and src/edge-asic, leaving only src/edge-32 as unchanged public RTL:

sh
python3 tools/package_obfuscated_rtl.py

For another checkout, pass --private-root once for each private source tree. For another design, set at least --product-name, --artifact-stem, --namespace, --top, --soc-top, --soc-core-module, and --regenerate-command. Add --sram-pattern or --keep only for genuine hard macro boundaries or stable public ABI symbols.

The generator must pass all built-in Verilator stages before replacing the output: source elaboration, mixed public/private elaboration, and SoC boundary elaboration. Preserve the output submodule's .git entry.

Review deterministic output

Inspect the output submodule, not only the parent gitlink:

sh
git -C src/edge-e3enc status --short
git -C src/edge-e3enc diff --stat
git -C src/edge-e3enc diff -- manifest.json '*.fl' README.md LICENSE.md
git -C src/edge-e3enc diff -- edge_e3enc.v edge_e3enc_sram.v

Accept metadata, documentation, or generator-banner changes only when expected. Investigate changes to filelists, module headers, port names, license text, symbol counts, or RTL bodies. For a banner-only RTL change, compare body hashes:

sh
git -C src/edge-e3enc show HEAD:edge_e3enc.v | tail -n +2 | shasum -a 256
tail -n +2 src/edge-e3enc/edge_e3enc.v | shasum -a 256

Use sha256sum instead of shasum -a 256 where appropriate.

Validate the public consumer

Use a checkout with only src/edge-32 and src/edge-e3enc initialized. Keep src/edge-e3, src/edge-asic, and src/test-e3 absent, then run:

sh
./scripts/build-verilator.sh
./example/hello/run.sh
./example/tensor/run.sh

Require TEST PASS in both reports. Never initialize private RTL merely to make the public build pass.

Restore or publish

For a comparison-only run, restore the output submodule exactly:

sh
git -C src/edge-e3enc reset --hard HEAD
git -C src/edge-e3enc clean -fd

For a release, review and commit inside the output submodule first, push it only with explicit authorization, then update and commit the parent gitlink. Report the two commits separately.

© exeex, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .codex/skills/rtl-release-packager of exeex/edge-cores.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 68dc8aa

Compare with similar skills

Rtl Release Packager next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Rtl Release Packager compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Rtl Release Packager this skillexeex/edge-cores110—~892Automated safety check: PassApache-2.0
Agent BuildershareAI-lab/learn-claude-code78k6 repos~1.2kAutomated safety check: PassMIT
Add Uint Supportpytorch/pytorch104k2 repos~2.3kAutomated safety check: PassCustom licence
Peft Fine TuningOrchestra-Research/AI-Research-SKILLs13k9 repos~3.1kAutomated safety check: PassMIT
Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs13k9 repos~3.3kAutomated safety check: PassMIT
1passwordtrpc-group/trpc-agent-go1.8k15 repos~656Automated safety check: PassApache-2.0

Similar skills

  • Agent Builder

    shareAI-lab/learn-claude-code

    Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.

    78k GitHub starsUsed in 6 repos~1.2k tokens
    AI & LLM EngineeringAuto-check passed

  • Add Uint Support

    pytorch/pytorch

    Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.

    104k GitHub starsUsed in 2 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

  • Peft Fine Tuning

    Orchestra-Research/AI-Research-SKILLs

    Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods.

    13k GitHub starsUsed in 9 repos~3.1k tokens
    AI & LLM EngineeringAuto-check passed

  • Segment Anything Model Guide

    Orchestra-Research/AI-Research-SKILLs

    Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.

    13k GitHub starsUsed in 9 repos~3.3k tokens
    AI & LLM EngineeringAuto-check passed

  • 1password

    trpc-group/trpc-agent-go

    Set up and use 1Password CLI (op). An agent skill from trpc-group/trpc-agent-go.

    1.8k GitHub starsUsed in 15 repos~656 tokens
    AI & LLM EngineeringAuto-check passed

  • Chroma Vector Database

    Orchestra-Research/AI-Research-SKILLs

    Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

    13k GitHub starsUsed in 8 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

More from exeex/edge-cores

All 9 skills in this repo

  • Edge Bringup

    exeex/edge-cores

    Prepare a macOS or Ubuntu machine for edge-e3 development, diagnose missing Verilator/LLVM/Python dependencies, initialize the public repository, and answer or act on the example prompts in the root…

    110 GitHub stars~1.7k tokensUpdated 12 days ago
    Auto-check: notes

  • Edge Contribution

    exeex/edge-cores

    Prepare, validate, submit, triage, and review open-source contributions to the shared Edge RV framework in edge-cores.

    110 GitHub stars~1.2k tokensUpdated 12 days ago
    Auto-check passed

  • Integrate Your Design

    exeex/edge-cores

    Integrate and publish your own accelerator or ASIC design, DMA, and DTCM/SRAM with the Edge RV64 scalar core and command path.

    110 GitHub stars~1.4k tokensUpdated 12 days ago
    Auto-check passed

  • Edge Synth Report

    exeex/edge-cores

    Run and diagnose the repository-local Yosys synthesis profiles for edge-e3 encrypted product RTL, edge-rv, and edge-rv-lite, then summarize FPGA resource reports.

    110 GitHub stars~516 tokensUpdated 12 days ago
    Auto-check passed

  • Llama Nnc

    exeex/edge-cores

    Develop and profile the edge-e3 PyTorch-to-NNC flow, including nnc/compiler.py lowering and generated ABI, example/llama smoke models, cpp/libnn runtime operators, BF16 correctness checks, and…

    110 GitHub stars~1.1k tokensUpdated 12 days ago
    Auto-check passed

  • Software Harness

    exeex/edge-cores

    Run, extend, debug, or review the public edge-e3 bare-metal software harness, including encrypted Verilator builds, hello and tensor examples, all example/llama/model smoke cases, PyTorch BF16…

    110 GitHub stars~1k tokensUpdated 12 days ago
    Auto-check passed

Categories

Questions about Rtl Release Packager

What does Rtl Release Packager do?

Generate, compare, validate, and prepare mixed public/private RTL release packages with symbol-obfuscated proprietary RTL and unchanged public RTL dependencies. Rtl Release Packager is an agent skill from exeex/edge-cores. Generate, compare, validate, and prepare mixed public/private RTL release packages with symbol-obfuscated proprietary RTL and unchanged public RTL dependencies.

When should I use Rtl Release Packager?

Rtl Release Packager fits situations like: obfuscated Verilog/SystemVerilog releases; source-available ASIC simulation packages; private-to-public RTL ABI boundaries; edge-e3enc regeneration.

How do I install Rtl Release Packager in Claude Code?

Run `npx skills add exeex/edge-cores --skill rtl-release-packager -a claude-code`. Or copy the skill folder (.codex/skills/rtl-release-packager in exeex/edge-cores) into .claude/skills/rtl-release-packager in your project. Claude Code loads it when a task matches its description.

How do I install Rtl Release Packager in Codex?

Run `npx skills add exeex/edge-cores --skill rtl-release-packager -a codex`. Or copy the skill folder (.codex/skills/rtl-release-packager in exeex/edge-cores) into .agents/skills/rtl-release-packager in your project. Codex loads it when a task matches its description.

Can I use Rtl Release Packager in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add exeex/edge-cores --skill rtl-release-packager -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rtl-release-packager, .gemini/skills/rtl-release-packager, .github/skills/rtl-release-packager and .opencode/skills/rtl-release-packager in your project.

What does Rtl Release Packager need to run?

Going by SKILL.md and its folder, Rtl Release Packager needs the command-line tools its instructions call (git and python3). Our summary lists: Python 3.

Does Rtl Release Packager access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Rtl Release Packager safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Rtl Release Packager use?

Rtl Release Packager is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rtl Release Packager use?

About 892 tokens (SKILL.md is roughly 3.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Rtl Release Packager?

Skills that share tags, products or a category with Rtl Release Packager: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rtl Release Packager?

exeex (a GitHub user) maintains it in exeex/edge-cores, which has 110 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on September 26, 2026.

Source: exeex/edge-cores on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.