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 nabeelhyatt/coworkpowers --skill workflow-reviewgit clone --depth 1 https://github.com/nabeelhyatt/coworkpowersWrote 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/nabeelhyatt/coworkpowers/workflow-review)<a href="https://agentmods.dev/skills/nabeelhyatt/coworkpowers/workflow-review"><img src="https://agentmods.dev/badge/skills/nabeelhyatt/coworkpowers/workflow-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/nabeelhyatt/coworkpowers/workflow-review"><img src="https://agentmods.dev/badge/skills/nabeelhyatt/coworkpowers/workflow-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 | $0.00064 | $0.01157 |
| Opus 5 | $0.00032 | $0.00579 |
| Sonnet 5 | $0.00013 | $0.00231 |
| Haiku 4.5 | $0.00006 | $0.00116 |
Grade A, and why
workflow-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 11d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review: Multi-Agent Knowledge Work Review
You are orchestrating the Review phase of the Compound Knowledge Work loop. Your job is to evaluate completed work from multiple specialized perspectives and synthesize findings into actionable improvements.
Different perspectives catch different issues. A single reviewer misses things that parallel review catches.
Process
Phase 1: Determine Review Scope
- Read the completed work thoroughly.
- Identify the work type to select appropriate reviewers:
| Work Type | Standard Reviewers | Conditional Reviewers |
|---|---|---|
| Communication | clarity-reviewer, tone-calibrator, completeness-auditor | sensitivity-scanner (if sensitive topic), strategic-alignment-checker (if strategic) |
| Decision | completeness-auditor, risk-assessor, devil's-advocate, actionability-validator | strategic-alignment-checker (if organizational), sensitivity-scanner (if political) |
| Analysis | completeness-auditor, clarity-reviewer, actionability-validator | risk-assessor (if recommendations included), devil's-advocate (if controversial) |
| Meeting prep | completeness-auditor, actionability-validator, clarity-reviewer | stakeholder-mapper (if complex dynamics), sensitivity-scanner (if contentious topics) |
| Coaching notes | clarity-reviewer, actionability-validator | tone-calibrator (if shared with coachee) |
- Check if the plan specified reviewers. If so, use those as the baseline.
Phase 2: Parallel Agent Review
Launch all selected review agents in parallel using the Task tool.
Each agent reviews independently and returns findings in their specialized format.
Agent Instructions:
- Each agent receives the complete work output
- Each agent applies its specialized lens
- Each agent returns findings with severity levels
Phase 3: Synthesize Findings
After all agents return, synthesize their findings:
Output Format:
## Review Summary
### Overall Assessment
**Quality Score**: [1-10]
[2-3 sentence assessment of overall quality and readiness]
### Critical Issues (Must Fix)
Issues that would cause real problems if not addressed.
#### Issue 1: [Title]
- **Found by**: [Agent name]
- **Location**: [Where in the document]
- **Problem**: [What's wrong]
- **Impact**: [What happens if not fixed]
- **Fix**: [Specific recommendation]
### Important Issues (Should Fix)
Issues that meaningfully improve quality.
#### Issue 1: [Title]
- **Found by**: [Agent name]
- **Location**: [Where in the document]
- **Problem**: [What's wrong]
- **Fix**: [Specific recommendation]
### Minor Issues (Nice to Fix)
Polish and refinement opportunities.
- [Issue]: [Quick fix] _(found by [agent])_
- [Issue]: [Quick fix] _(found by [agent])_
### What's Working Well
- [Strength 1] _(noted by [agent])_
- [Strength 2] _(noted by [agent])_
### Conflicting Feedback
[If agents disagree, note the conflict and recommend resolution]
### Recommended Action
- [ ] Fix critical issues (estimated: [time])
- [ ] Fix important issues (estimated: [time])
- [ ] Fix minor issues (estimated: [time])
- [ ] Ready to finalize after fixes
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.
- 11d ago First seen · 140 lines · 64 tokens per session scan A d4605afb22c8
workflow-review is a skill published in the GitHub repository nabeelhyatt/coworkpowers (114 stars, last pushed 6mo ago), licensed MIT. It adds 64 tokens to every session and 1,157 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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