Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plus/opportunity-sizer)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plus/opportunity-sizer"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plus/opportunity-sizer.svg" alt="Measured on agentmods" 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 | $0.03121 |
| Opus 5 | $0.00000 | $0.01561 |
| Sonnet 5 | $0.00000 | $0.00624 |
| Haiku 4.5 | $0.00000 | $0.00312 |
Grade C, and why
opportunity-sizer scanned grade C with 1 finding 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 6d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- CONTRACT_START name: opportunity-sizer description: Quantify the business value of an opportunity or fix with sensitivity analysis that identifies which assumptions matter most. inputs: - name: OPPORTUNITY type: str This is a copy
95% identical to opportunity-sizer — 17 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Opportunity Sizer
Purpose
Quantify the business value of an opportunity or a fix, with sensitivity analysis that identifies which assumptions matter most and where the conclusion might break. Turns analytical findings into dollar-denominated business cases that stakeholders can act on.
Inputs
- {{OPPORTUNITY}}: Description of the opportunity (e.g., "Fix iOS payment bug", "Improve mobile checkout conversion", "Reduce support ticket volume"). Should include what would change and for whom.
- {{ANALYSIS_RESULTS}}: (optional) Path to a report from Root Cause Investigator, Descriptive Analytics, or another analysis agent. If provided, the agent extracts baseline metrics and affected populations from it.
- {{DATASET}}: Data source for computing baselines and population sizes.
- {{ASSUMPTIONS}}: (optional) User-provided assumptions for the sizing model (e.g., "assume 30% of affected users would convert", "assume $15 per support ticket"). If not provided, the agent estimates from data and flags the estimates as assumptions.
- {{VALUE_METRICS}}: (optional) How to express value — "revenue", "cost_savings", "time_saved", "users_impacted", or "all" (default: "all").
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.
- 6d ago First seen · 288 lines · 0 tokens per session scan C 044ad2778afe
opportunity-sizer is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plus (19 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,121 tokens. A static security scan graded it C with 1 finding (hidden instructions). It is 95% identical to opportunity-sizer, differing in 17 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.