Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add abdullahkhawer/devops-skills --skill code-reviewgit clone --depth 1 https://github.com/abdullahkhawer/devops-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/abdullahkhawer/devops-skills/code-review)<a href="https://agentmods.dev/skills/abdullahkhawer/devops-skills/code-review"><img src="https://agentmods.dev/badge/skills/abdullahkhawer/devops-skills/code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/abdullahkhawer/devops-skills/code-review"><img src="https://agentmods.dev/badge/skills/abdullahkhawer/devops-skills/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.00595 |
| Opus 5.5 | $0.00022 | $0.00238 |
| Sonnet 5.5 | $0.00011 | $0.00119 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Skill
Run this skill when asked to review code, compare branches, or perform a code review between a source and target branch using local git. Do NOT use this skill if the user provides a GitLab or GitHub MR/PR URL or MR ID — use the gitlab-cli or github-cli skill instead.
Step 1 — Collect inputs
Ask the user the following two questions together if not already provided:
Kindly let me know:
1. Source Branch: What is the name of the source branch (the branch with new changes)?
2. Target Branch: What is the name of the target branch (usually main/master)?
If the user has already provided either value upfront, skip the corresponding question.
Step 2 — Find changed files
git diff --name-only <TARGET_BRANCH> <SOURCE_BRANCH>
If the output is empty, report that there are no differences between the two branches and stop.
Step 3 — Analyze each changed file
For each file returned in Step 2, read the full diff:
git diff <TARGET_BRANCH> <SOURCE_BRANCH> -- <FILE_PATH>
Perform a comprehensive code review across all changed files covering:
- Security: Secrets exposure, API keys, tokens, SQL injection, XSS, command injection, insecure dependencies.
- Performance: Inefficient algorithms, memory leaks, unnecessary computations, N+1 queries.
- Code quality: Code smells, duplicate code, overly complex functions, trailing whitespace, dead code.
- Error handling: Missing or incorrect error handling, unhandled exceptions, silent failures.
- Logging: Missing or excessive logging, sensitive data in logs.
- Documentation: Missing or outdated comments, unclear variable/function names.
Step 4 — Report findings
For each issue found:
- State the file path and line number(s).
- Show the existing code snippet that needs to change.
- Provide the suggested fix or improvement.
Only include code snippets that actually require changes — do not repeat code that looks fine.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 74 lines · 56 tokens per session scan A 6b8dc0b34648
code-review is a skill published in the GitHub repository abdullahkhawer/devops-skills (8 stars, last pushed 28d ago), licensed Apache-2.0. It adds 56 tokens to every session and 595 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
github-code-review
Comprehensive GitHub code review with AI-powered swarm coordination.
ci-cd
CI/CD pipelines for GitHub Actions and Docker-based deployments — lint, test, build, push, deploy stages; environment secrets; branch-gated workflows; health-check rollbacks; and PM2/systemd service restarts.
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.
gentle-ai-collab-perfect
Trigger: contributing to Gentleman-Programming/gentle-ai as an external collaborator. Strict issue-first workflow, honest PR bodies, contributor-vs-maintainer scope, chained-PR strategy, verification protocol, docstring coverage. Load whenever the active repo is Gentleman-Programming/gentle-ai and any part of the…
branch-pr
Create Gentle AI pull requests with issue-first checks. Trigger: creating, opening, or preparing PRs for review.
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.