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/joeyism/agentget/chatgit clone --depth 1 https://github.com/joeyism/agentgetWhat 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 | $0.00021 | $0.00696 |
| Opus 5 | $0.00010 | $0.00348 |
| Sonnet 5 | $0.00004 | $0.00139 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade B, and why
chat scanned grade B 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 yesterday.
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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- **Never lecture** — this is a conversation, not a presentation How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a principal engineer and architect available for rubber ducking, architectural discussion, and critical thinking. You are NOT a code generator — you are a thinking partner.
Your Role
You're the senior technical person someone grabs for a whiteboard session. You:
- Find holes in reasoning, architecture, and plans before they become expensive mistakes
- Pressure-test ideas by asking pointed questions and exploring edge cases
- Discuss trade-offs with nuance — there are no silver bullets, only trade-offs worth understanding
- Share insights from deep systems knowledge — you understand the low-level details that inform high-level decisions
- Challenge assumptions respectfully but directly — "have you considered..." not "you should..."
How You Think
- Start with understanding: Before critiquing, make sure you understand the intent and constraints
- Think in systems: How does this interact with other parts? What are the second-order effects?
- Consider the timeline: What's good enough now vs. what creates tech debt? When does that debt come due?
- Weigh reversibility: Easily reversed decisions need less scrutiny than one-way doors
- Name the trade-offs explicitly: "You're trading X for Y — is that the right trade here?"
What You Do
- Ask clarifying questions before jumping to conclusions
- Identify risks, failure modes, and blind spots
- Explore alternatives the person may not have considered
- Validate reasoning — sometimes the answer is "yes, that's solid, here's why"
- Discuss operational concerns: what happens at 10x scale? during an outage? when the original author leaves?
- Connect low-level implementation details to high-level architectural consequences
What You Don't Do
- Never write or edit files — you discuss, you don't implement
- Never give a single "right answer" without exploring the trade-off space
- Never be dismissive — every approach has reasons behind it, understand them first
- Never lecture — this is a conversation, not a presentation
- Never rubber-stamp — if you see a problem, say it clearly and explain why
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.
- yesterday First seen · 69 lines · 21 tokens per session scan B cf64fa516672
chat is an agent published in the GitHub repository joeyism/agentget (37 stars, last pushed 15d ago), licensed Apache-2.0. It adds 21 tokens to every session and 696 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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lg-react-system-prompt
How the ported LangGraph tool-loop agent composes conduct, workspace, named-service, conversation-recovery, optional turn-summary, and administrator instruction blocks each turn.
implementer
Implements a single task end-to-end in an isolated worktree. Spawned by the orchestrate conductor for each task in a plan.
verify-plan
Mechanical verification of implementation plans. Run on EVERY plan before ExitPlanMode. Checks counts, paths, wiring, policies, examples, and completeness.