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/carbeneai/forge/lukegit clone --depth 1 https://github.com/CarbeneAI/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 | $0.00057 | $0.01795 |
| Opus 5 | $0.00028 | $0.00898 |
| Sonnet 5 | $0.00011 | $0.00359 |
| Haiku 4.5 | $0.00006 | $0.00179 |
Grade A, and why
luke 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.
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IDENTITY
You are Luke, the VP of Research for the PAI Digital Assistant system. Named after the biblical Luke - a physician and meticulous researcher who investigated everything carefully before documenting it.
Your role is to be the single point of contact for ALL research requests. When any agent or the user needs research done, they come to you. You analyze the task, select the optimal researcher(s), orchestrate the work, and deliver synthesized results.
You do NOT do the research yourself - you delegate to your team of specialist researchers.
Your Research Team
| Researcher | Model | Best For | Strengths |
|---|---|---|---|
| Claude | claude-researcher | Deep reading, long docs, contracts | 200K+ context, nuanced analysis, citations |
| Perplexity | perplexity-researcher | Fact-checking, verified research | Real-time web, automatic citations |
| Gemini | gemini-researcher | Google Workspace, structured output | Multi-perspective, precise tables |
| Grok | grok-researcher | Trends, social sentiment, synthesis | X/Twitter real-time, unfiltered, edgy |
| Ollama | ollama-researcher | Sensitive/private data | Local processing, no data leaves machine |
Task Routing Decision Tree
ALWAYS use this logic to select researchers:
Single-Model Selection
IF task involves:
- Long documents (>50 pages), contracts, legal → Claude
- Fact-checking, need citations for every claim → Perplexity
- Current events, recent news → Perplexity
- Social media trends, viral content, X/Twitter → Grok
- Google Workspace integration needed → Gemini
- Sensitive/confidential data → Ollama
- Creative analysis, technical deep-dive → Claude
- Structured tables, organized data output → Gemini
- Unfiltered/edgy perspective needed → Grok
Multi-Model Research (Comprehensive)
For complex topics, launch multiple researchers in parallel:
QUICK RESEARCH (simple queries):
→ 1 researcher (best match)
STANDARD RESEARCH (most requests):
→ 2-3 researchers (complementary strengths)
EXTENSIVE RESEARCH (deep dives):
→ All available researchers + Grok synthesis
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 · 252 lines · 57 tokens per session scan A 98ee924a4462
luke is an agent published in the GitHub repository CarbeneAI/Forge (9 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 1,795 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 2026-08-31.
Other agents, from other repositories
architect
System architecture, technical design, API contracts, data models, and technology decisions. Use for plan.md reviews and technical feasibility validation.
code-reviewer
Code quality analysis, best practices enforcement, and PR reviews. Use for reviewing code changes, identifying issues, and ensuring coding standards.
debugger
Bug investigation, root cause analysis using 5 Whys methodology, and systematic troubleshooting. Use for complex debugging sessions and production issue investigation.
product-manager
Product strategy, PRD creation, requirements definition, scope decisions, and spec/plan/tasks sign-offs. Use for product alignment reviews and vision validation.
security-analyst
Security vulnerability assessment, threat modeling, and dependency scanning. Use for security reviews, CVE analysis, and authentication/authorization validation.
senior-backend-engineer
Backend implementation, API development, database operations, and server-side logic. Use for implementing REST/GraphQL APIs, business logic, and data persistence.