← 返回 Skills 市场
anderskev

Fix Llm Artifacts

作者 Kevin Anderson · GitHub ↗ · v1.1.4 · MIT-0
cross-platform ✓ 安全检测通过
112
总下载
0
收藏
1
当前安装
1
版本数
在 OpenClaw 中安装
/install fix-llm-artifacts
功能描述
Applies fixes from a prior review-llm-artifacts run, with safe/risky classification
使用说明 (SKILL.md)

Fix LLM Artifacts

Apply fixes from a previous review-llm-artifacts run with automatic safe/risky classification.

Usage

/beagle-core:fix-llm-artifacts [--dry-run] [--all] [--category \x3Cname>]

Flags:

  • --dry-run - Show what would be fixed without changing files
  • --all - Fix entire codebase (runs review with --all first)
  • --category \x3Cname> - Only fix specific category: tests|dead-code|abstraction|style

Instructions

1. Parse Arguments

Extract flags from $ARGUMENTS:

  • --dry-run - Preview mode only
  • --all - Full codebase scan
  • --category \x3Cname> - Filter to specific category

2. Pre-flight Safety Checks

# Check for uncommitted changes
git status --porcelain

If working directory is dirty, warn:

Warning: You have uncommitted changes. Creating a git stash before proceeding.
Run `git stash pop` to restore if needed.

Create stash if dirty:

git stash push -m "beagle-core: pre-fix-llm-artifacts backup"

3. Load Review Results

Check for existing review file:

cat .beagle/llm-artifacts-review.json 2>/dev/null

If file missing:

  • If --all flag: Run review-llm-artifacts --all --json first
  • Otherwise: Fail with: "No review results found. Run /beagle-core:review-llm-artifacts first."

If file exists, validate freshness:

# Get stored git HEAD from JSON
stored_head=$(jq -r '.git_head' .beagle/llm-artifacts-review.json)
current_head=$(git rev-parse HEAD)

if [ "$stored_head" != "$current_head" ]; then
  echo "Warning: Review was run at commit $stored_head, but HEAD is now $current_head"
fi

If stale, prompt: "Review results are stale. Re-run review? (y/n)"

4. Partition Findings by Safety

Parse findings from JSON and classify by fix_safety field:

Safe Fixes (auto-apply):

  • unused_import - Unused imports
  • todo_comment - Stale TODO/FIXME comments
  • dead_code_obvious - Obviously unreachable code
  • verbose_comment - Overly verbose LLM-style comments
  • redundant_type - Redundant type annotations

Risky Fixes (require confirmation):

  • test_refactor - Test structure changes
  • abstraction_change - Class/function extraction
  • code_removal - Removing functional code
  • mock_boundary - Test mock scope changes
  • logic_change - Any behavioral modifications

5. Apply Safe Fixes

If --dry-run:

## Safe Fixes (would apply automatically)

| File | Line | Type | Description |
|------|------|------|-------------|
| src/api.py | 15 | unused_import | Remove `from typing import List` |
| src/models.py | 42 | verbose_comment | Remove 23-line docstring |
...

Otherwise, spawn parallel agents per category with Task tool:

Task: Apply safe fixes for category "{category}"
Files: [list of files with findings in this category]
Instructions: Apply each fix, preserving surrounding code. Report success/failure per fix.

Categories to parallelize:

  • style - Comments, formatting
  • dead-code - Imports, unreachable code
  • tests - Test-related safe fixes
  • abstraction - Safe refactors

6. Handle Risky Fixes

For each risky fix, prompt interactively:

[src/services/auth.py:156] Remove seemingly unused authenticate_legacy() method?
This method has no callers in the codebase but may be used externally.
(y)es / (n)o / (s)kip all risky:

Track user choices:

  • y - Apply this fix
  • n - Skip this fix
  • s - Skip all remaining risky fixes

7. Post-Fix Verification

Detect project type and run appropriate linters:

Python:

# Check if ruff config exists
if [ -f "pyproject.toml" ] || [ -f "ruff.toml" ]; then
    ruff check --fix .
    ruff format .
fi

# Check if mypy config exists
if [ -f "pyproject.toml" ] || [ -f "mypy.ini" ]; then
    mypy .
fi

TypeScript/JavaScript:

# Check for eslint
if [ -f "eslint.config.js" ] || [ -f ".eslintrc.json" ]; then
    npx eslint --fix .
fi

# Check for TypeScript
if [ -f "tsconfig.json" ]; then
    npx tsc --noEmit
fi

Go:

if [ -f "go.mod" ]; then
    go vet ./...
    go build ./...
fi

8. Run Tests

# Python
if [ -f "pyproject.toml" ] || [ -f "pytest.ini" ]; then
    pytest
fi

# JavaScript/TypeScript
if [ -f "package.json" ]; then
    npm test 2>/dev/null || yarn test 2>/dev/null || true
fi

# Go
if [ -f "go.mod" ]; then
    go test ./...
fi

9. Report Results

## Fix Summary

### Applied Fixes
- [x] src/api.py:15 - Removed unused import `List`
- [x] src/models.py:42-64 - Removed verbose docstring
- [x] src/auth.py:156-189 - Removed dead method (user confirmed)

### Skipped Fixes
- [ ] src/services/cache.py:23 - User declined risky fix
- [ ] tests/test_api.py:45 - Test refactor skipped

### Verification Results
- Linter: PASSED
- Type check: PASSED
- Tests: PASSED (42 passed, 0 failed)

### Diff Summary
```bash
git diff --stat

Cleanup

On successful completion (all verifications pass):

rm .beagle/llm-artifacts-review.json

If any verification fails, keep the file and report:

Review file preserved at .beagle/llm-artifacts-review.json
Fix issues and re-run, or restore with: git stash pop

Example

# Preview all fixes without applying
/beagle-core:fix-llm-artifacts --dry-run

# Fix only dead code issues
/beagle-core:fix-llm-artifacts --category dead-code

# Full codebase scan and fix
/beagle-core:fix-llm-artifacts --all

# Fix style issues only, preview first
/beagle-core:fix-llm-artifacts --category style --dry-run
安全使用建议
This skill appears to do what it says, but it will modify your repository and expects common developer tools to be present. Before running: (1) run the --dry-run first to preview changes, (2) make sure you have a clean backup or commit (the skill will stash dirty work and may delete the .beagle review file on success), (3) ensure required tools are installed (git, jq, ruff/mypy for Python, npm/npx or yarn for JS/TS, pytest, go toolchain as relevant), and (4) be prepared to respond to prompts for risky fixes. If you manage a shared or production repo, test on a branch or clone/CI run before applying automatic fixes.
功能分析
Type: OpenClaw Skill Name: fix-llm-artifacts Version: 1.1.4 The skill is a code maintenance utility designed to apply refactoring suggestions from a previous analysis. It includes safety features such as checking for uncommitted git changes, creating stashes before modification, and requiring user confirmation for 'risky' changes (SKILL.md). The execution logic relies on standard development tools (git, ruff, eslint, pytest) and lacks any indicators of data exfiltration, persistence, or malicious command execution.
能力评估
Purpose & Capability
The skill claims to apply fixes from a prior review and its steps (reading .beagle/llm-artifacts-review.json, running git operations, applying code edits, running linters/tests) align with that purpose. However the SKILL.md assumes the availability of developer tools (git, jq, ruff, mypy, npx/npm/yarn, pytest, go, etc.) while the registry metadata declares no required binaries; this mismatch should be noted before running.
Instruction Scope
Instructions operate directly on the repository (git stash, apply fixes, run linters/tests, potentially remove .beagle/llm-artifacts-review.json). That is within scope for a fixer. The doc instructs spawning parallel agents using a 'Task' tool to apply fixes — this grants the skill the ability to perform many parallel edit operations and should be used with caution. Risky fixes are interactively prompted (y/n/s), which reduces silent destructive changes.
Install Mechanism
Instruction-only skill with no install spec or code files; nothing is written to disk by an installer. This is the lowest-risk install model.
Credentials
The skill requests no credentials or environment variables. It reads repository state and a local review JSON file (.beagle/llm-artifacts-review.json), which is appropriate for its purpose. No unrelated secrets or external endpoints are referenced.
Persistence & Privilege
The skill is not forced always-on and has disable-model-invocation set to true (reducing autonomous model use). It modifies only repository files and its own review artifact file; it does not request system-wide configuration or other skills' settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install fix-llm-artifacts
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /fix-llm-artifacts 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.1.4
- Overhauled fix workflow with detailed step-by-step instructions in SKILL.md - Now partitions findings into safe (auto-apply) vs. risky (user confirmation) categories - Adds comprehensive argument parsing, pre-flight git checks, and stale review detection - Automates linting, type checking, and test runs after fixes are applied - Includes sample commands and improved reporting for all stages (applied/skipped fixes, verification, cleanup) - Enhances usability and safety for applying LLM artifact fixes across codebases
元数据
Slug fix-llm-artifacts
版本 1.1.4
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Fix Llm Artifacts 是什么?

Applies fixes from a prior review-llm-artifacts run, with safe/risky classification. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 112 次。

如何安装 Fix Llm Artifacts?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install fix-llm-artifacts」即可一键安装,无需额外配置。

Fix Llm Artifacts 是免费的吗?

是的,Fix Llm Artifacts 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Fix Llm Artifacts 支持哪些平台?

Fix Llm Artifacts 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Fix Llm Artifacts?

由 Kevin Anderson(@anderskev)开发并维护,当前版本 v1.1.4。

💬 留言讨论