wh:ask

A command for querying Wheeler, a knowledge graph: a database that stores connected facts and their sources. It looks up nodes, relationships, and where information came from.

In plain words
What is it for?
Use it to find nodes, check graph status, trace provenance, explore connections, identify gaps, detect stale information, and inspect citations.
Why use it?
It helps users inspect research knowledge without manually tracing related records or source history.

Command for Claude Code

Install

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.

agentmods
npx agentmods add commands/maxwellsdm1867/wheeler/ask
Clone the repo
git clone --depth 1 https://github.com/maxwellsdm1867/wheeler

Made for: Claude Code.

Per session 23 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,031 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 7a05f0494880, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

.claude/commands/wh/ask.md · 90 lines

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

  1. Parse the question — what are they asking about? A specific node? A relationship? An overview? A comparison?

  2. Query the graph — use the right tool:

    • "What do we know about X?" → query_findings with keyword, then query_hypotheses, query_papers
    • "What's in the graph?" → graph_status + graph_context
    • "Where did this come from?" → run_cypher to 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)
      
  3. Answer with citations — every claim cites a [NODE_ID]. If you can't cite it, say so.

  4. 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
    

Read the full file on GitHub · 90 lines

Changes

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.

  1. 2d ago First seen · 90 lines · 23 tokens per session scan A 7a05f0494880

Subscribe to this mod's changes

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.