Cdk Contribution Skill
cdklabs/cdk-contribution-skill
Guide AWS CDK contributions from GitHub issue analysis to PR-ready code.
Implement a GitHub issue end-to-end — branch, code, test, docs, and commit.
OfficialApache-2.0Auto-check passed
Install Issue
$ npx skills add awslabs/loom --skill issue -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install awslabs/loom issue --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/awslabs/loom.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/issue .claude/skills/issue && rm -rf skills-srcUse ~/.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/
Install the "issue" agent skill from https://github.com/awslabs/loom/tree/main/.claude/skills/issue into .claude/skills/issue/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "issue", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/awslabs/loom/tree/main/.claude/skills/issueType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add awslabs/loom --skill issue -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install awslabs/loom issue --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/loom.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/issue .agents/skills/issue && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "issue" agent skill from https://github.com/awslabs/loom/tree/main/.claude/skills/issue into .agents/skills/issue/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "issue", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add awslabs/loom --skill issue -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install awslabs/loom issue --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/loom.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/issue .cursor/skills/issue && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "issue" agent skill from https://github.com/awslabs/loom/tree/main/.claude/skills/issue into .cursor/skills/issue/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "issue", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/awslabs/loom.git --path .claude/skills/issue--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add awslabs/loom --skill issue -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install awslabs/loom issue --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/loom.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/issue .gemini/skills/issue && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "issue" agent skill from https://github.com/awslabs/loom/tree/main/.claude/skills/issue into .gemini/skills/issue/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "issue", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install awslabs/loom issueInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add awslabs/loom --skill issue -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/awslabs/loom.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/issue .github/skills/issue && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "issue" agent skill from https://github.com/awslabs/loom/tree/main/.claude/skills/issue into .github/skills/issue/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "issue", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add awslabs/loom --skill issue -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install awslabs/loom issue --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/awslabs/loom.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/issue .opencode/skills/issue && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "issue" agent skill from https://github.com/awslabs/loom/tree/main/.claude/skills/issue into .opencode/skills/issue/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "issue", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
issue- GitHub stars
- 191
- Token cost
- ~1k tokens
- SKILL.md length
- 521 words
- Files
- 1
- Skills in repo
- 3
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Implement a GitHub issue end-to-end — branch, code, test, docs, and commit.
- Works in 8 steps: Fetch the Issue → Create Feature Branch → Plan the Implementation → …
- SKILL.md covers Input, Step 1: Fetch the Issue, Step 2: Create Feature Branch and Step 3: Plan the Implementation, plus 5 more sections
- Calls git, gh and make
What it does
Issue is an agent skill from awslabs/loom, published by the product's own GitHub organization. Implement a GitHub issue end-to-end — branch, code, test, docs, and commit.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with GitHub and Amazon Web Services. The repository describes itself as: Loom for AWS is an enterprise-grade platform for building, deploying, integrating, and operating AI agents on Amazon Bedrock AgentCore. The licence is Apache-2.0.
Example prompts
- “/issue”
Requirements
- Python 3
- Pre-approved tools (allowed-tools): Agent, Bash(git *), Bash(gh *), Bash(cd *), Bash(make *), Bash(npm *), Bash(uv *), Bash(python *), Bash(cat *), Bash(ls *), Bash(mkdir *), Bash(cp *), Read, Edit, Write, Grep, Glob, TaskCreate, TaskUpdate, TaskGet, TaskList
Workflow steps
8 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4d4dbd6. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
AgentBash(git *)Bash(gh *)Bash(cd *)Bash(make *)Bash(npm *)Bash(uv *)Bash(python *)Bash(cat *)Bash(ls *)
…and 11 more on the same allowed-tools line.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
gitghmakenpm
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, gh and npm, 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
Issue loads about 1k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 521 words of instructions outside code blocks.
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 awslabs/loom at commit 4d4dbd6, republished under its Apache-2.0 licence (© awslabs). 521 words, ~1,006 tokens.
Download SKILL.mdSave it as
.claude/skills/issue/SKILL.md (or your agent's skills folder).- name
- issue
- description
- Implement a GitHub issue end-to-end — branch, code, test, docs, and commit.
- allowed-tools
- Agent, Bash(git *), Bash(gh *), Bash(cd *), Bash(make *), Bash(npm *), Bash(uv *), Bash(python *), Bash(cat *), Bash(ls *), Bash(mkdir *), Bash(cp *), Read, Edit, Write, Grep, Glob, TaskCreate, TaskUpdate, TaskGet, TaskList
- argument-hint
- <issue-number>
Issue Implementation
Implement a GitHub issue end-to-end: fetch the issue, create a feature branch, implement all requirements, test, update docs, and commit.
Input
$ARGUMENTS must be a GitHub issue number (e.g. 78). If missing or non-numeric, ask the user for the issue number.
Step 1: Fetch the Issue
Run gh issue view $ARGUMENTS --json title,body,labels to get the issue details.
Also check if a matching markdown file exists under tmp/issues/ by looking for files that start with a zero-padded version of the issue number (e.g. issue 78 maps to a file starting with 078-). If the file exists, read it for additional context.
Display the issue title and a brief summary to the user.
Step 2: Create Feature Branch
Determine the branch name from the matching issue markdown filename under tmp/issues/, but without the .md extension. For example, if the file is 078-my-feature.md, the branch name is 078-my-feature.
If no matching file exists, derive the branch name from the issue number and title: zero-pad the issue number to 3 digits, kebab-case the title, and join with a hyphen (e.g. 078-my-feature-title).
Create and switch to the feature branch: git checkout -b <branch-name>.
Step 3: Plan the Implementation
Read the full issue body and any linked markdown file. Identify all discrete requirements.
Create a task list using TaskCreate for each requirement so progress can be tracked.
Before writing any code, present the implementation plan to the user and wait for confirmation.
Step 4: Implement
Use an agent team to parallelize independent work where possible:
- Backend changes: Implement in
backend/following project conventions (Python, FastAPI, type hints, SQLAlchemy). - Frontend changes: Implement in
frontend/following project conventions (TypeScript, ESM, shadcn, Tailwind, Vite). - Infrastructure changes: Implement in
iac/following AWS SAM / CloudFormation conventions. - Agent changes: Implement in
agents/with isolated virtual environments.
Update TaskUpdate as each requirement is completed.
Show full SKILL.md (217 more words)Show less
Step 5: Test
Run existing tests to check for regressions:
- Backend:
cd backend && make test(or the appropriate makefile target) - Frontend:
cd frontend && npm test(if test scripts exist) - Agent tests: Run tests within each modified agent directory.
If the issue specifies new test cases, implement and run them.
All tests must pass before proceeding.
Step 6: Update Documentation
Update the following documentation files to reflect the new changes:
- SPECIFICATIONS.md: Update any relevant specification files with the latest design decisions. Search for SPECIFICATIONS.md files in all subdirectories.
- README.md: Update any relevant README files with the latest implementation details. Ensure no sensitive or account-specific information is included (no ARNs, account IDs, tokens, or endpoint URLs).
Step 7: Final Validation
Run the full test suite one more time to confirm nothing was broken during documentation updates.
Review all changes with git diff to verify correctness and ensure no secrets or credentials are included.
Step 8: Commit
Stage and commit all changes locally using semantic commit messages. Group related changes into logical commits:
feat:for new featuresfix:for bug fixesdocs:for documentation updatestest:for test additionsrefactor:for code restructuringchore:for build/config changes
Do NOT push to remote. Only commit locally.
Append Co-Authored-By: Claude <noreply@anthropic.com> to each commit message.
Report a summary of all commits created.
© awslabs, 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
Just SKILL.md in .claude/skills/issue of awslabs/loom.
Open the folder on GitHubat commit 4d4dbd6
Compare with similar skills
Issue 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Issue this skillawslabs/loom | 191 | — | ~1k | Automated safety check: Pass | Apache-2.0 | |
| Cdk Contribution Skillcdklabs/cdk-contribution-skill | 110 | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Readme Skillstudy8677/Readme.skill | 172 | — | ~18k | Automated safety check: Pass | MIT | |
| Update Provider Depsmondoohq/mql | 411 | — | ~4.5k | Automated safety check: Pass | Custom licence | |
| Onboard Repoaws-samples/sample-autonomous-cloud-coding-agents | 157 | — | ~2.8k | Automated safety check: Pass | MIT-0 | |
| Distilled SDKalchemy-run/distilled | 431 | — | ~6k | Automated safety check: Pass | Apache-2.0 |
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More from awslabs/loom
Related
Works with
Questions about Issue
What does Issue do?
Implement a GitHub issue end-to-end — branch, code, test, docs, and commit. Issue is an agent skill from awslabs/loom, published by the product's own GitHub organization. Implement a GitHub issue end-to-end — branch, code, test, docs, and commit.
How do I install Issue in Claude Code?
Run `npx skills add awslabs/loom --skill issue -a claude-code`. Or copy the skill folder (.claude/skills/issue in awslabs/loom) into .claude/skills/issue in your project. Claude Code loads it when a task matches its description.
How do I install Issue in Codex?
Run `npx skills add awslabs/loom --skill issue -a codex`. Or copy the skill folder (.claude/skills/issue in awslabs/loom) into .agents/skills/issue in your project. Codex loads it when a task matches its description.
Can I use Issue 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 awslabs/loom --skill issue -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/issue, .gemini/skills/issue, .github/skills/issue and .opencode/skills/issue in your project.
What does Issue need to run?
Going by SKILL.md and its folder, Issue needs the command-line tools its instructions call (git, gh, make and npm). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Agent, Bash(git *), Bash(gh *), Bash(cd *), Bash(make *), Bash(npm *), Bash(uv *), Bash(python *), Bash(cat *), Bash(ls *), Bash(mkdir *), Bash(cp *), Read, Edit, Write, Grep, Glob, TaskCreate, TaskUpdate, TaskGet, TaskList.
Does Issue access the network?
SKILL.md contains no URLs. Its commands use git, gh and npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Issue 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 Issue use?
Issue 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 Issue use?
About 1k tokens (SKILL.md is roughly 4k 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 Issue?
Skills that share tags, products or a category with Issue: Cdk Contribution Skill (cdklabs/cdk-contribution-skill, 110 stars), Readme Skill (study8677/Readme.skill, 172 stars), Update Provider Deps (mondoohq/mql, 411 stars) and Onboard Repo (aws-samples/sample-autonomous-cloud-coding-agents, 157 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Issue?
awslabs (a GitHub organization, an official publisher) maintains it in awslabs/loom, which has 191 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 7, 2026.
Source: awslabs/loom on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.