A review workflow for pull requests that add tasks to SkillsBench, a benchmark for testing AI coding agents. It checks the task rules, runs comparisons across several agents, and reviews their work traces.

Reviewing SkillsBench task pull requests, checking policy compliance, benchmarking Claude and Codex with and without skills, auditing trajectories, and producing a report plus submission bundle.

It removes the need to manually classify benchmark tasks, apply different rule sets, compare agent results, and inspect whether agents cheated or used skills correctly.

How benchflow-ai/benchflow configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to benchflow-ai/benchflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/benchflow-ai/benchflow/main/.agents/skills/task-review/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/benchflow-ai/benchflow

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/benchflow-ai/benchflow/task-review/github.svg)](https://agentmods.dev/skills/benchflow-ai/benchflow/task-review)

Your own site

<a href="https://agentmods.dev/skills/benchflow-ai/benchflow/task-review"><img src="https://agentmods.dev/badge/skills/benchflow-ai/benchflow/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/benchflow-ai/benchflow/task-review"><img src="https://agentmods.dev/badge/skills/benchflow-ai/benchflow/task-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 141 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,585 The whole file, excluding the scripts and references it only reads on demand.

Security scan B 2 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 95% copy Near-identical to another mod in the catalogue.

Token cost

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	$0.00141	$0.04585
Opus 5.5	$0.00056	$0.01834
Sonnet 5.5	$0.00028	$0.00917
Haiku 4.5	$0.00014	$0.00458

Measured 1mo ago against content hash e31fc162d21e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade B, and why

task-review scanned grade B with 2 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.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/fetch_pr.sh, scripts/package_traj.sh, scripts/parse_results.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

1. The user's own configs: `cat ~/.codex/config.toml` (often pins a Codex model + reasoning effort), `~/.claude/settings.json` for Claude.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

| `verifier/test_outputs.py` imports a network client (`exa_py`, `requests`, `urllib`, `httpx`, `googleapiclient`) used during verification | **research-track** |

Origin

This is a copy

95% identical to task-review — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/task-review/SKILL.md · 213 lines

How it starts

The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SkillsBench Task Review

Repo context. This skill lives in the benchflow repo but reviews PRs against benchflow-ai/skillsbench. Unqualified references below — CONTRIBUTING.md, MAINTAINER.md, docs/*.md, tasks/<task-id>/ — mean files in skillsbench. scripts/fetch_pr.sh defaults to that repo; override with SKILLSBENCH_REPO. For auditing already-published run trajectories, use benchflow-experiment-review instead.

End-to-end review of a SkillsBench task PR. Two artifacts are produced: a human-readable .txt report, and a pr<N>.zip bundle that mirrors the format reviewers post on PRs (see PR #560 comment for the reference structure).

Workflow

1. fetch       → pull PR files into a workspace (no git checkout)
2. route       → classify task track; pick the track-specific rubric
3. policy      → static checks against rubric (no execution)
4. benchmark   → 5 configs: oracle + claude×{skills,no} + codex×{skills,no}
5. audit       → read trajectories: skill use, cheating, root cause of failures
6. report      → fill report-template.txt and bundle pr<N>.zip

Each step is described below. Run them in order — never skip benchmark to write a verdict, never skip audit to interpret results.

Step 1 — Fetch the PR

scripts/fetch_pr.sh <pr_number> <workspace>
# → echoes the task dir path; writes <workspace>/pr-<N>.meta.json with PR metadata.

Use gh API + raw download. Do not gh pr checkout or git pull — keep the local clone clean. For a local-path review, skip this step and pass the task directory directly to step 3.

Step 2 — Route to a track

A SkillsBench task belongs to one of three tracks. The track determines what "verifiable" means and which policy items apply. Always classify before running policy checks — applying the wrong rubric is the most common reason a review goes sideways.

Read the full file on GitHub · 213 lines

Files

What ships with it

12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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 · 213 lines · 141 tokens per session scan B e31fc162d21e

Subscribe to this mod's changes

task-review is a skill published in the GitHub repository benchflow-ai/benchflow (355 stars, last pushed 7d ago), licensed Apache-2.0. It adds 141 tokens to every session and 4,585 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it B with 2 findings (reads agent configuration directories, makes network calls). It is 95% identical to task-review, differing in 7 lines, and is treated as a copy.

Code review benchmark evals

find-bugs

Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.

benchflow-ai/skillsbench · 40 tokens

architect-init

Use when bootstrapping architecture documentation for a brownfield project by reverse-engineering ADRs from an existing codebase.

tikalk/adlc-team-skills · 27 tokens

architect-analyze

Use when validating generated or refined architecture artifacts, before feature development, during architecture review, or periodically to detect ADR↔AD drift, completeness gaps, and quality issues.

tikalk/adlc-team-skills · 38 tokens

factory-review

Use when reviewing PRs for severity-ranked policy compliance against REVIEW.md, babysitting agent PRs to merge, or self-healing Important findings before a human sees them.

tikalk/adlc-team-skills · 37 tokens

code-review

Paranoid architect review of code changes for bugs, security, missing tests, and undocumented assumptions. Works on local git diffs OR a GitHub pull request (e.g. owner/repo N). For PRs, can post findings as line-level review comments.

suyoumo/ClawProBench · 57 tokens

parallel-pr-review

Use when asked to "review the open PRs", review a batch or stack of pull requests, or run a recurring PR-review pass on a repo — especially with many PRs, stacked branches, conflicts, or security-sensitive changes. Covers grouping, fan-out to review subagents, verdict synthesis, and posting.

suyoumo/ClawProBench · 67 tokens

benchflow: Skill for Claude Code