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 AgiFlow/ai-plugin --skill review-workgit clone --depth 1 https://github.com/AgiFlow/ai-pluginWrote 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/agiflow/ai-plugin/review-work)<a href="https://agentmods.dev/skills/agiflow/ai-plugin/review-work"><img src="https://agentmods.dev/badge/skills/agiflow/ai-plugin/review-work/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/agiflow/ai-plugin/review-work"><img src="https://agentmods.dev/badge/skills/agiflow/ai-plugin/review-work.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 | $0.00072 | $0.01541 |
| Opus 5.5 | $0.00029 | $0.00616 |
| Sonnet 5.5 | $0.00014 | $0.00308 |
| Haiku 4.5 | $0.00007 | $0.00154 |
Grade A, and why
review-work 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.
This is a copy
100% identical to review-work — 0 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.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoked as
/agiflow:review-work. In hosts without slash-prompts, this skill is triggered by matching intent and drives AgiFlow via its MCP tools.
Usage:
/agiflow:review-work <work-unit-slug-or-id>- Review a specific work unit/agiflow:review-work <task-slug-or-id>- Review a specific task/agiflow:review-work- List completed items for review
Examples:
/agiflow:review-work DXX-WU-1(review work unit by slug)/agiflow:review-work DXX-3(review task by slug)/agiflow:review-work(interactive selection)
Purpose Verify that completed work actually meets its acceptance criteria, catches quality issues, and is ready to ship. AI code has 1.7x more defects than human-written code — review is the last line of defense before shipping.
Guardrails
- Review what exists — do not implement fixes during review (create follow-up tasks instead).
- Every acceptance criterion gets a pass/fail verdict with evidence.
- Flag issues by severity: blocker (must fix), warning (should fix), note (nice to fix).
- Be honest — a "pass" with gaps is worse than a clear "needs rework".
If a work unit or task slug/id is provided, load it with get_work_unit / get_task; otherwise list candidates with list_work_units / list_tasks for selection.
AgiFlow Project Management Guidelines
Follow the shared AgiFlow project-management guidelines in references/agiflow-agents.md — agent assignment, the task status workflow and transitions, work-unit best practices, and the tags strategy apply to this workflow.
Steps
1. Load the Work for Review
If work unit slug/id provided:
- Use
get_work_unitMCP tool to load the work unit and its tasks. - For each task in the work unit, use
get_taskto load full details (acceptance criteria, devInfo, comments).
If task slug/id provided: 3. Use get_task MCP tool to load the task with full details.
If nothing provided: 4. Use list_work_units with status: "completed" to show completed work units. 5. Use list_tasks with status: "Review" or status: "Done" to show completed tasks. 6. Ask user to select what to review.
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 · 184 lines · 72 tokens per session scan A 88f3a4a71bd6
review-work is a skill published in the GitHub repository AgiFlow/ai-plugin (4 stars, last pushed 10d ago), licensed MIT. It adds 72 tokens to every session and 1,541 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to review-work, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
review-work
Quality gate: verify each acceptance criterion of a completed task/work unit, run quality checks, and create follow-up tasks for gaps. Use before merging or to audit delivered work. Invoked as /agiflow:review-work . Uses getworkunit, gettask, updatetask, createtask, createtaskcomment.
code-review
Review code for quality, maintainability, and correctness.
claude_code_delegation
Delegate coding, review, and refactor tasks to Claude Code CLI through Row-Bot's approval-gated shell workflow.
agent-review
Critically review a Stencila agent and suggest improvements. Use when asked to review, audit, critique, evaluate, or improve an agent directory or AGENT.md file. Covers frontmatter validation, system instruction quality, configuration correctness, and adherence to the Agent schema.
developer_review
Code-review workflow for repositories in a conversation's code folder.
sentinel
CocoSentinel — eight-dimension artifact quality gate. Dimension G evidence pre-gate runs first (deterministic, .