Borrowing it
Nothing to install: this file belongs to okfriansyah-moh/ares. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/okfriansyah-moh/ares/main/.ai/skills/task-review/SKILL.mdgit clone --depth 1 https://github.com/okfriansyah-moh/aresWrote 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/okfriansyah-moh/ares/task-review)<a href="https://agentmods.dev/skills/okfriansyah-moh/ares/task-review"><img src="https://agentmods.dev/badge/skills/okfriansyah-moh/ares/task-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/okfriansyah-moh/ares/task-review"><img src="https://agentmods.dev/badge/skills/okfriansyah-moh/ares/task-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 | $0.00000 | $0.00477 |
| Opus 5 | $0.00000 | $0.00238 |
| Sonnet 5 | $0.00000 | $0.00095 |
| Haiku 4.5 | $0.00000 | $0.00048 |
Grade A, and why
task-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 8d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Review a completed implementation task for compliance with the plan, architecture, tests, security expectations, complexity budget, and release readiness.
Inputs
docs/architecture.mdfor system boundaries, provider mappings, security requirements, and release rules.docs/PLAN.mdfor the selected task, dependencies, files, validation commands, and definition of done.- The implementation diff, relevant code, tests, generated artifacts, and command output.
- Current
git statusto separate task changes from unrelated work.
Review Checklist
- PLAN compliance: verify only the selected task was implemented, required files are complete, future tasks were not pulled forward, and task-specific validation was run.
- Architecture compliance: verify package boundaries, interfaces, local file-based behavior, deterministic compose/import output, and
.ai/source-of-truth rules are preserved. - Test coverage: verify focused unit or integration tests cover new behavior, edge cases, errors, and regressions; confirm
go test -race -count=1 ./...passes. - Security: verify path handling, symlink behavior, atomic writes, dependency vulnerability checks, and absence of unnecessary network, runtime, or privilege-expanding behavior.
- Complexity: verify KISS, YAGNI, DRY, narrow interfaces, no speculative abstractions, no global mutable state, and complexity within the plan budget.
- Release readiness: verify build, vet, staticcheck, tests,
govulncheck, CLI behavior, container expectations, and versioning impact as relevant to the task.
Findings Format
- Lead with findings ordered by severity.
- Include file and line references where possible.
- State the violated plan, architecture, test, security, complexity, or release requirement.
- Describe the user-visible or maintenance risk.
- Keep summaries brief and secondary to findings.
- If no issues are found, say so clearly and list any residual risk or commands not run.
Anti-Patterns
- Reviewing only for style while missing plan or architecture drift.
- Treating future-task implementation as a bonus.
- Accepting untested behavior because the change is small.
- Ignoring security checks around filesystem access and generated artifacts.
- Recommending broad refactors unrelated to the selected task.
- Hiding failed or skipped validation commands.
- Mixing unrelated dirty worktree changes into the review result.
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.
- 8d ago First seen · 39 lines · 0 tokens per session scan A b361af4f0fde
task-review is a skill published in the GitHub repository okfriansyah-moh/ares (2 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 477 tokens. 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
agentic-code-review
Use when reviewing a diff, pull request, branch, or AI-generated code for correctness, security, regression, test, performance, and maintainability risks before merge.
repo-health-check
Use when auditing a repo before changes to find the smallest safe PR, quality risks, stale docs, missing tests, ignored-file gaps, or agent setup issues. Best first skill for unfamiliar codebases.
intelligence-review-skills
Audit the intelligence layer for duplication, drift, size, hardcoded paths and framing.
safe-change
Trace, implement, test, and independently review one bounded software change. Use for ordinary feature, bug-fix, or refactor work after the repository is bootstrapped; do not use for broad repository cleanup, production operations, or undefined rewrites.
readiness
Use when the user wants to analyze, audit, or assess their codebase for AI agent readiness. Also use for "readiness report", "how ready is my project", "analyze my codebase", "audit my repo", "check my setup", or "what should I improve".
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation.