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/closegit 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 | $0.00024 | $0.04311 |
| Opus 5 | $0.00012 | $0.02155 |
| Sonnet 5 | $0.00005 | $0.00862 |
| Haiku 4.5 | $0.00002 | $0.00431 |
Grade A, and why
wh:close 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 — 383 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, ending a research session. You do two things, in order:
- Sweep: find graph entities created this session that lack provenance, group them, propose Execution nodes that close the gaps.
- Synthesize: write a session synthesis to
.plans/SESSION-{date}.md, register it as a Document node, and link it to every source node it cites. The graph is the authoritative record of what happened in this session.
Do the sweep first so the synthesis can cite a fully-linked graph.
Phase 1: Orphan Sweep
1.1 Determine the session window
The session window is "since the last close." Find it via Cypher. Closes with an empty or missing started_at must be excluded: they sort wrong under ORDER BY ... DESC and would silently pull the boundary back to an older close.
MATCH (x:Execution {kind: "close"})
WHERE x.started_at IS NOT NULL AND x.started_at <> ""
RETURN x.started_at AS last_close
ORDER BY x.started_at DESC LIMIT 1
- If a row returns, use
last_closeas the window start. - If no row, default to the last 24 hours.
- Remember this timestamp as
$since; both phases use it.
Then check for malformed close boundaries. Never fall back silently:
MATCH (x:Execution {kind: "close"})
WHERE x.started_at IS NULL OR x.started_at = ""
RETURN x.id AS id, x.description AS description, x.date AS date
If this returns any rows, warn the scientist before proceeding:
Warning: {N} close Execution(s) have no started_at ([X-xxxx] "{description}"). The window boundary uses the most recent close with a valid timestamp, so this sweep may re-surface nodes already synthesized in a more recent session. Repair with
update_node(X-xxxx, started_at=<ISO 8601 timestamp>, allow_provenance=true)if you know when that close ran.
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 · 383 lines · 24 tokens per session scan A 4d535cf1d285
wh:close is a command published in the GitHub repository maxwellsdm1867/wheeler (10 stars, last pushed 5d ago), licensed MIT. It adds 24 tokens to every session and 4,311 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.