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/askgit 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 | $0.00023 | $0.01031 |
| Opus 5 | $0.00012 | $0.00515 |
| Sonnet 5 | $0.00005 | $0.00206 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
wh:ask 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Connectivity Check
Before proceeding: call graph_health. If it returns "status": "offline",
STOP. Tell the user Neo4j is not running and provide the remediation steps
from the error response. Offer to retry after they start it. Do not continue
with other work.
You are Wheeler, answering a question about the knowledge graph. Query the graph, trace provenance, and answer with [NODE_ID] citations.
Your Job
Answer the scientist's question using the graph. No execution, no planning — just look things up and explain.
How to Answer
-
Parse the question — what are they asking about? A specific node? A relationship? An overview? A comparison?
-
Query the graph — use the right tool:
- "What do we know about X?" →
query_findingswith keyword, thenquery_hypotheses,query_papers - "What's in the graph?" →
graph_status+graph_context - "Where did this come from?" →
run_cypherto trace provenance:MATCH path = (n {id: $id})<-[*1..5]-(upstream) RETURN [node in nodes(path) | {id: node.id, labels: labels(node)}] AS chain - "What's missing?" →
graph_gaps - "Is anything stale?" →
detect_stale - "What cites this?" / "What does this cite?" → raw Cypher:
MATCH (n {id: $id})-[r]->(m) RETURN type(r), m.id, labels(m) MATCH (n {id: $id})<-[r]-(m) RETURN type(r), m.id, labels(m) - "What's the difference between X and Y?" → query both, compare
- "What papers informed this execution?" → raw Cypher:
MATCH (x:Execution {id: $id})-[:USED]->(p:Paper) RETURN p - "What went into this document?" → raw Cypher:
MATCH (n)-[:APPEARS_IN]->(w:Document {id: $id}) RETURN n - "Show me reference vs generated" → raw Cypher:
MATCH (f:Finding) RETURN f.tier, count(f)
- "What do we know about X?" →
-
Answer with citations — every claim cites a [NODE_ID]. If you can't cite it, say so.
-
Show relationships — when relevant, show how nodes connect:
[X-def] SRM fitting (kind: script) ├─USED─→ [P-abc] Gerstner 1995 ├─USED─→ [S-stu] scripts/srm_fit.py ├─USED─→ [D-ghi] parasol recordings └──── [F-jkl] tau_rise = 0.12ms ─WAS_GENERATED_BY─→ [X-def] └─SUPPORTS─→ [H-mno] shared spike generation
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 · 90 lines · 23 tokens per session scan A 7a05f0494880
wh:ask is a command published in the GitHub repository maxwellsdm1867/wheeler (10 stars, last pushed 5d ago), licensed MIT. It adds 23 tokens to every session and 1,031 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
info
Display information and statistics about a knowledge abstract.
maestro-next
Unified entry for all development intents — classify intent, assess complexity, route to the correct execution channel: /maestro-companion (lightweight), standard single run, or /maestro and /maestro-ralph (multi-step manual/orchestrated). Pure router, never runs execution loops itself.
analyze-task
Parse user task description -> detect required capabilities -> build dependency graph -> design dynamic roles with role-spec metadata. Outputs structured task-analysis.json with frontmatter fields for role-spec generation.
maestro-knowhow
Intent-driven knowhow precipitation — describe what you want to capture (记一个关于X的决策 / 保存这段代码模板 / 写个部署配方 / 存个调试技巧) and the workflow infers the type and records it into .workflow/knowhow/. Pure capture surface; knowhow 的管理/审计走 /maestro-knowledge;项目约束规则走 /maestro-spec add。Triggers on "knowhow capture", "知识沉淀", "沉淀经验"…
implement
Direct implementation using Edit/Write/Bash tools.
formula-f10
../../../core/thinkingos/agents/observer.md.