SkillHub

linux-riscv-contribute

v1.0.0

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...

Sourced from ClawHub, Authored by zcxGGmu

Installation

Please help me install the skill `linux-riscv-contribute` from SkillHub official store. 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