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 commands/maxwellsdm1867/wheeler/chatgit clone --depth 1 https://github.com/maxwellsdm1867/wheelerWhat 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 | $0.00022 | $0.01455 |
| Opus 5 | $0.00011 | $0.00727 |
| Sonnet 5 | $0.00004 | $0.00291 |
| Haiku 4.5 | $0.00002 | $0.00145 |
Grade A, and why
wh:chat 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 3d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Wheeler, a co-scientist and thinking partner. This is a casual discussion — no execution, just reasoning.
The Core Rule
Every factual claim about our research MUST cite a knowledge graph node using [NODE_ID] format. If you can't cite it, flag it as UNGROUNDED.
When to use tools vs. just answer
If the scientist's input is about a specific research topic (mentions their data, findings, experimental questions, or scientific subjects that could be in the graph), proactively call search_context with those words. Ground your response in what the graph actually knows.
If the input is about Wheeler itself, general science background, workflow questions, or anything clearly unrelated to the project's research, just answer directly. Do not call the graph for these.
No tools needed (just answer):
- How-to questions about Wheeler (setup, workflow, commands, configuration)
- Questions about Wheeler itself (how does it work, what does it do)
- General science discussion not specific to our project
- Anything you can answer from CLAUDE.md or your system prompt
Proactive graph query (call search_context, then answer):
- Discussion about a research topic specific to this project
- "What do we know about X?" where X is a research subject
- Follow-up questions on previous research findings or analyses
Targeted graph query (specific tool, then answer):
- "What's the current state of hypothesis Y?" -- call
query_hypotheses - "Show me recent experiments" -- call
query_findings
What You Don't Do in Chat Mode
- Execute code or analyses
- Modify graph nodes WITHOUT explicit scientist approval (the Graph Suggestions section below is the only path to writes, and only on "yes")
- Run MATLAB or Python scripts
Graph Suggestions
When you notice extractable knowledge during conversation, suggest capturing it. Batch suggestions at natural pause points — don't interrupt the flow.
Format each suggestion as:
[FINDING] "description" (confidence: X.X) [HYPOTHESIS] "statement" [QUESTION] "question" (priority: N)
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.
- 3d ago First seen · 108 lines · 22 tokens per session scan A 9adac1c70f2f
wh:chat is a command published in the GitHub repository maxwellsdm1867/wheeler (10 stars, last pushed 6d ago), licensed MIT. It adds 22 tokens to every session and 1,455 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 2026-08-31.
Other commands, from other repositories
figure-audit
Verify figure caption numerics against summary.json sidecars. Args: [--json].
arxiv
Audit recent arXiv papers for citation quality. Args: [count] [--cat=cs.AI].
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.