linux-riscv-contribute

Orchestrate an OpenClaw multi-agent pipeline to close Linux RISC-V gaps versus ARM/x86 (Linux tree + KVM lore), create and manage GitHub issues, generate design plans with Claude Code, implement/verify with Codex, and prepare upstream patch emails. Use when users ask to automate or run RISC-V kernel contribution workflows, gap analysis, issue-driven execution, or patch submission preparation.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "linux-riscv-contribute" with this command: npx skills add zcxggmu/linux-riscv-contribute

Linux RISC-V Contribute

Overview

Use this skill to run a repeatable discover -> issue -> plan -> implement -> patch pipeline with OpenClaw as orchestrator and ACP agents (claude-code, codex) as workers.

Keep humans at exactly three gates:

  1. Confirm gap triage and priorities.
  2. Approve implementation plan.
  3. Approve final patch email before sending.

Workflow

Step 0: Bootstrap workspace

Run scripts/bootstrap_openclaw_workflow.sh <docs_repo_root> <linux_repo_path> to create/update:

  • kernel/openclaw/config/workflow.yaml
  • kernel/openclaw/state/{gap_registry.yaml,issue_map.yaml,run_history/}
  • kernel/openclaw/{plans,patches,logs}

If files already exist, do not overwrite without explicit user approval.

Step 1: Discover RISC-V gaps

Collect evidence from:

  • Linux source tree (arch/riscv, arch/arm64, arch/x86, virt/kvm)
  • KVM lore (https://yhbt.net/lore/kvm/)

Write structured entries to state/gap_registry.yaml with:

  • gap_id, type (feature|performance|maintainability), summary
  • evidence (paths, commits, lore URLs)
  • severity (P0|P1|P2), confidence (high|medium|low)
  • acceptance_hint

Pause for Gate-1 human triage before creating issues.

Step 2: Sync GitHub issues

For each approved gap:

  • Create/update issue in configured repo.
  • Add labels from severity/type.
  • Save gap_id -> issue_number mapping to state/issue_map.yaml.

Use one issue per gap; avoid duplicate issues by matching gap_id.

Step 3: Plan with Claude Code (ACP)

Spawn ACP session explicitly:

  • runtime: "acp"
  • agentId: "claude-code"

Ask for:

  • file-level design
  • test matrix (kselftest, kvm-unit-tests, perf)
  • rollback/risk notes
  • upstreaming strategy

Save outputs under kernel/openclaw/plans/issue-<id>-plan.md. Pause for Gate-2 human plan approval.

Step 4: Implement and verify with Codex (ACP)

Spawn ACP session explicitly:

  • runtime: "acp"
  • agentId: "codex"

Run iterative loop until pass or policy limit:

  1. Implement approved plan.
  2. Build and run configured tests.
  3. Parse failures and patch.

Record each iteration in state/run_history/*.json. If max iterations reached, return to Step 3 with failure summary.

Step 5: Generate patch and email package

Produce:

  • git format-patch series
  • checkpatch result
  • suggested To/Cc (get_maintainer.pl, lore context)
  • cover letter draft

Save artifacts in kernel/openclaw/patches/. Pause for Gate-3 human send approval.

Only send to mailing lists after explicit approval.

OpenClaw execution rules

  • Prefer ACP sessions_spawn for agent work; set agentId explicitly.
  • Limit parallel issues to 2-3 unless user changes policy.
  • Never auto-send external email without user confirmation.
  • Preserve auditability: every stage must have file artifacts.

Quick command prompts for operator

Use these ready prompts in OpenClaw chat:

  1. 按 workflow.yaml 执行 Step-1,更新 gap_registry.yaml,并生成 Gate-1 审核表。
  2. 基于已批准 gap 执行 Step-2,同步 issue 并输出映射表。
  3. 对 issue #<n> 用 claude-code 执行 Step-3,生成详细方案和测试矩阵。
  4. 对 issue #<n> 用 codex 执行 Step-4,直到验证通过或达到迭代上限。
  5. 对 issue #<n> 执行 Step-5,先 dry-run 生成 patch 和发信草案,等待我确认。

References

  • Workflow template: references/workflow-template.yaml
  • Issue template: references/issue-template.md
  • Human gate checklist: references/gate-checklist.md

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Coding

Research Review Skill Factory

Build custom peer-review skills for specific research areas, problem families, and method combinations using OpenReview evidence. Use when Codex needs a comp...

Registry SourceRecently Updated
Coding

本地图片语义搜索

本地图片语义搜索工具,基于 CLIP 模型实现中英文图片内容的语义理解检索,类似小米相册 AI 搜索功能。使用场景:(1) 用户想用自然语言搜索本地图片 (2) 用户需要搜索中文关键词相关的图片 (3) 用户提到"搜图片"、"找图片"、"图片搜索"、"AI相册"等关键词

Registry SourceRecently Updated
Coding

Mini Coder Max

Autonomous coding agent that systematically plans, implements, reviews, and delivers high-quality code. Handles tasks of any complexity by following a struct...

Registry SourceRecently Updated
Coding

Mt4 Trader

MT4 Trader Bridge enables Python to MT4 EA communication via files for trading, order management, risk control, and grid strategies without extra dependencies.

Registry SourceRecently Updated