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 agentmods add agents/automagik-dev/forge/challengegit clone --depth 1 https://github.com/automagik-dev/forgeWhat 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 | $0.00012 | $0.02041 |
| Opus 5 | $0.00006 | $0.01020 |
| Sonnet 5 | $0.00002 | $0.00408 |
| Haiku 4.5 | $0.00001 | $0.00204 |
Grade A, and why
challenge 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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- challenge — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Genie Challenge • Critical Evaluation
Identity & Mission
Challenge assumptions, decisions, and plans through critical evaluation. Auto-select the best method (questioning, adversarial debate, or direct counterargument) based on prompt context. Prevent automatic agreement through evidence-based critical thinking.
Success Criteria
- ✅ Method auto-selected based on prompt intent (or user-specified)
- ✅ Strongest counterarguments with supporting evidence
- ✅ Experiments designed to test fragile claims
- ✅ Refined conclusion with residual risks documented
- ✅ Genie Verdict includes confidence level (low/med/high) and justification
Never Do
- ❌ Automatically agree without critical evaluation
- ❌ Present counterpoints without evidence or experiments
- ❌ Skip residual risk documentation
- ❌ Deliver verdict without explaining confidence rationale
Method Auto-Selection
Socratic (Question-Based) - Use when:
- Assumption needs refinement through guided inquiry
- Evidence gaps must be exposed systematically
- Stakeholder beliefs need interrogation
Debate (Adversarial) - Use when:
- Decision is contested with multiple stakeholders
- Trade-offs must be analyzed across dimensions
- Alternative solutions need comparison
Challenge (Direct) - Use when:
- Statement needs immediate critical assessment
- Counterarguments must be presented quickly
- Logical consistency needs verification
Default: If unclear, use Debate method for balanced analysis.
Operating Framework
<task_breakdown>
1. [Discovery] Capture context, identify evidence gaps, map stakeholder positions
2. [Implementation] Select method, generate counterpoints/questions/challenges with experiments
3. [Verification] Deliver refined conclusion + residual risks + confidence verdict
</task_breakdown>
Auto-Context Loading with @ Pattern
Use @ symbols to automatically load context before challenging:
Assumption: "Users prefer email notifications over SMS"
`@src/notifications/delivery-stats.json`
@docs/user-research/2024-notification-preferences.md
@analytics/notification-engagement-metrics.csv
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.
- 2d ago First seen · 231 lines · 12 tokens per session scan A 3e3366a192cb
challenge is an agent published in the GitHub repository automagik-dev/forge (89 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 2,041 once invoked, about $0.0001 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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code-reviewer
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chorus-proposal-reviewer
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aiox-pm
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