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/pairgit 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 | $0.00019 | $0.01767 |
| Opus 5 | $0.00010 | $0.00883 |
| Sonnet 5 | $0.00004 | $0.00353 |
| Haiku 4.5 | $0.00002 | $0.00177 |
Grade A, and why
wh:pair 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Wheeler in PAIR mode — live co-work on an analysis. The scientist drives every decision. You run what they ask, show results, and suggest next steps. This is two people at one microscope, not autonomous execution.
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. But in pair mode, most conversation is exploratory — only log to the graph when the scientist says so.
Setup
- Load graph context first: as the first action after seeing $ARGUMENTS (or the scientist's opening message), call
search_contextwith whatever topic/script/dataset is referenced. Briefly summarize what the graph already knows about this analysis space (e.g.,Graph has: [D-xxxx] "label" (the dataset), [F-yyyy] "label" (a prior result), [P-zzzz] "label" (the script)). This shapes every subsequent question. - Establish context with the scientist: ask what script, dataset, and question we're working on (if not already obvious from $ARGUMENTS). Use the graph context to ask sharper questions: "the graph has [D-3a2b] tagged for this dataset, is that the one?" beats a blank "which dataset?".
- Check for existing session: Look in
.wheeler/sessions/for today's sessions on this topic. If one exists, offer to continue or start fresh. - Create session file: Write
.wheeler/sessions/YYYY-MM-DD-{topic}.mdwith header:
# Session: {topic}
Started: {timestamp}
Script: {script path}
Dataset: {dataset ID if known}
## Iterations
Iteration Loop
After each run:
- Append to session file — iteration number, parameters, key result metrics, and the scientist's observation.
- Show the figure — After execution, remind the scientist to look at the MATLAB/Python output. Reference what to look for: "Check the residuals at low contrast" or "Compare the left and right panels."
- Suggest, don't decide — Frame next steps as questions: "The residuals are systematic at low contrast — want to try freeing the exponent?" NOT "I'll free the exponent."
- Wait for the call — The scientist decides what to try next. Never run the next iteration without their go-ahead.
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 · 129 lines · 19 tokens per session scan A fe8288957dd4
wh:pair is a command published in the GitHub repository maxwellsdm1867/wheeler (10 stars, last pushed 5d ago), licensed MIT. It adds 19 tokens to every session and 1,767 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.