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Agent skill

Code Review Riscv

by FastLED in FastLED/FastLED

Review RISC-V assembly code for correctness, ABI compliance, and best practices.

MITAuto-check passedDevelopment

Install Code Review Riscv

skills CLI
$ npx skills add FastLED/FastLED --skill code-review-riscv -a claude-code

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

GitHub CLI
$ gh skill install FastLED/FastLED code-review-riscv --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/FastLED/FastLED.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/code-review-riscv .claude/skills/code-review-riscv && 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
code-review-riscv
GitHub stars
7.5k
Token cost
~283 tokens
SKILL.md length
110 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Review RISC-V assembly code for correctness, ABI compliance, and best practices.

  • Works in 8 steps: Instruction correctness: Valid syntax,… → Register conventions: ABI compliance,… → Memory access: Alignment requirements,… → …
  • Tasks that involve Code review
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Code Review Riscv is an agent skill from FastLED/FastLED. Review RISC-V assembly code for correctness, ABI compliance, and best practices. Use after modifying RISC-V assembly or inline asm code.

Its SKILL.md is about 280 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Code review. The repository describes itself as: The FastLED library for colored LED animation on Arduino. Please direct questions/requests for help to the FastLED Reddit community: http://fastled.io/r We'd like to use github… The licence is MIT.

When your agent uses it

  • Tasks that involve Code review

Example prompts

  • “/code-review-riscv”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Instruction correctness: Valid syntax, proper operands, immediate ranges
  2. Register conventions: ABI compliance, saved vs temporary registers, stack alignment
  3. Memory access: Alignment requirements, sign extension, offset limits
  4. Control flow: Branches, jumps, function calls, returns
  5. CSR operations: Privilege level access, atomic semantics, field types
  6. Assembler directives: Sections, alignment, symbol visibility, data placement
  7. Common issues: Misalignment, stack corruption, clobbered registers, illegal immediates
  8. Platform-specific: RV32I vs RV64I, extensions (M/A/F/D/C), ABI variants

What it can do on your machine

Read from SKILL.md and the folder at commit ce225ed. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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

Code Review Riscv loads about 283 tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 110 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~283

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 FastLED/FastLED at commit ce225ed, republished under its MIT licence (© FastLED). 110 words, ~283 tokens.

Download SKILL.mdSave it as .claude/skills/code-review-riscv/SKILL.md (or your agent's skills folder).
name
code-review-riscv
description
Review RISC-V assembly code for correctness, ABI compliance, and best practices. Use after modifying RISC-V assembly or inline asm code.
disable-model-invocation
true
context
fork
agent
riscv-review-agent

Review RISC-V assembly code for correctness, adherence to conventions, and best practices.

The review covers 8 analysis categories:

  1. Instruction correctness: Valid syntax, proper operands, immediate ranges
  2. Register conventions: ABI compliance, saved vs temporary registers, stack alignment
  3. Memory access: Alignment requirements, sign extension, offset limits
  4. Control flow: Branches, jumps, function calls, returns
  5. CSR operations: Privilege level access, atomic semantics, field types
  6. Assembler directives: Sections, alignment, symbol visibility, data placement
  7. Common issues: Misalignment, stack corruption, clobbered registers, illegal immediates
  8. Platform-specific: RV32I vs RV64I, extensions (M/A/F/D/C), ABI variants

Search for RISC-V assembly code in git changes (inline asm and .S files) and perform deep analysis.

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

Files

Just SKILL.md in .claude/skills/code-review-riscv of FastLED/FastLED.

Open the folder on GitHubat commit ce225ed

Compare with similar skills

Code Review Riscv 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.

Code Review Riscv compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Code Review Riscv this skillFastLED/FastLED7.5k—~283Automated safety check: PassMIT
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
Backend Code Reviewlangflow-ai/langflow156k—~3.5kAutomated safety check: NotesMIT
Understand Diff AnalysisEgonex-AI/Understand-Anything86k1 repos~1.4kAutomated safety check: PassMIT
Mole Bug Patternstw93/Mole70k—~2kAutomated safety check: PassGPL-3.0

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  • Memory Audit

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  • Platform Port

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Categories

Questions about Code Review Riscv

What does Code Review Riscv do?

Review RISC-V assembly code for correctness, ABI compliance, and best practices. Code Review Riscv is an agent skill from FastLED/FastLED. Review RISC-V assembly code for correctness, ABI compliance, and best practices.

When should I use Code Review Riscv?

Code Review Riscv fits situations like: tasks that involve Code review.

How do I install Code Review Riscv in Claude Code?

Run `npx skills add FastLED/FastLED --skill code-review-riscv -a claude-code`. Or copy the skill folder (.claude/skills/code-review-riscv in FastLED/FastLED) into .claude/skills/code-review-riscv in your project. Claude Code loads it when a task matches its description.

How do I install Code Review Riscv in Codex?

Run `npx skills add FastLED/FastLED --skill code-review-riscv -a codex`. Or copy the skill folder (.claude/skills/code-review-riscv in FastLED/FastLED) into .agents/skills/code-review-riscv in your project. Codex loads it when a task matches its description.

Can I use Code Review Riscv 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 FastLED/FastLED --skill code-review-riscv -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-review-riscv, .gemini/skills/code-review-riscv, .github/skills/code-review-riscv and .opencode/skills/code-review-riscv in your project.

What does Code Review Riscv need to run?

SKILL.md names no scripts, command-line tools or credentials: Code Review Riscv is instructions for the agent only.

Does Code Review Riscv access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Code Review Riscv 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 Code Review Riscv use?

Code Review Riscv is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Code Review Riscv use?

About 283 tokens (SKILL.md is roughly 1.1k 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 Code Review Riscv?

Skills that share tags, products or a category with Code Review Riscv: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Backend Code Review (langflow-ai/langflow, 156k stars) and Understand Diff Analysis (Egonex-AI/Understand-Anything, 86k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Review Riscv?

FastLED (a GitHub organization) maintains it in FastLED/FastLED, which has 7,505 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 8, 2026.

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