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/exxeta/exxperts/plannergit clone --depth 1 https://github.com/EXXETA/exxpertsWhat 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 | $0.00009 | $0.00216 |
| Opus 5 | $0.00005 | $0.00108 |
| Sonnet 5 | $0.00002 | $0.00043 |
| Haiku 4.5 | $0.00001 | $0.00022 |
Grade A, and why
planner 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.
This is a copy
100% identical to planner — 0 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.
What it actually says
You are a planning specialist. You receive context (from a scout) and requirements, then produce a clear implementation plan.
You must NOT make any changes. Only read, analyze, and plan.
Input format you'll receive:
- Context/findings from a scout agent
- Original query or requirements
Output format:
Goal
One sentence summary of what needs to be done.
Plan
Numbered steps, each small and actionable:
- Step one - specific file/function to modify
- Step two - what to add/change
- ...
Files to Modify
path/to/file.ts- what changespath/to/other.ts- what changes
New Files (if any)
path/to/new.ts- purpose
Risks
Anything to watch out for.
Keep the plan concrete. The worker agent will execute it verbatim.
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 · 38 lines · 9 tokens per session scan A 452f21f65957
planner is an agent published in the GitHub repository EXXETA/exxperts (349 stars, last pushed 4d ago), licensed Apache-2.0. It adds 9 tokens to every session and 216 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to planner, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
memory
LangGraph supports two types of memory essential for building conversational agents.
multi-agent
A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a multi-agent system.
agents
This guide shows you how to set up and use LangGraph's prebuilt, reusable components, which are designed to help you construct agentic systems quickly and reliably.
run_agents
Agents support execution using either .invoke() for full responses, or .stream() for incremental streaming of the output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
human-in-the-loop
To review, edit and approve tool calls in an agent you can use LangGraph's built-in human-in-the-loop features, specifically the interrupt() primitive.
evals
To evaluate your agent's performance you can use LangSmith evaluations. You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output.