Borrowing it
Nothing to install: this file belongs to alfadur7/llm-wiki-newsroom. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alfadur7/llm-wiki-newsroom/main/.claude/commands/wiki-query.mdgit clone --depth 1 https://github.com/alfadur7/llm-wiki-newsroomWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/alfadur7/llm-wiki-newsroom/wiki-query)<a href="https://agentmods.dev/commands/alfadur7/llm-wiki-newsroom/wiki-query"><img src="https://agentmods.dev/badge/commands/alfadur7/llm-wiki-newsroom/wiki-query/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/alfadur7/llm-wiki-newsroom/wiki-query"><img src="https://agentmods.dev/badge/commands/alfadur7/llm-wiki-newsroom/wiki-query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01670 |
| Opus 5 | $0.00000 | $0.00835 |
| Sonnet 5 | $0.00000 | $0.00334 |
| Haiku 4.5 | $0.00000 | $0.00167 |
Grade A, and why
wiki-query 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 12d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Query the LLM Wiki and synthesize an answer.
Usage: /wiki-query <question> — required argument
If $ARGUMENTS is empty: print the usage below and stop.
Usage: /wiki-query <question>
Examples:
/wiki-query OpenSourceInitiative open source AI definition
/wiki-query What are the main themes across all sources?
/wiki-query summarize the open weights vs open source debate
Traversal Pattern
Classify the question as one of 6 types (Entity-centric / Relation / Topic / Fact / Meta / Contradiction), then branch.
| Mode | Cycle | Owner |
|---|---|---|
| Simple answer (no save) | Reading | Reporter (mode=ground) — broad wiki read + tools/query.py graph + answer synthesis |
| Synthesis save | L2-3 synthesis cycle | Columnist GROUND·APPLY → Copy Editor → Desk → Editor-in-Chief gate |
The rule for question-type classification → mode branch is the sole responsibility of the ## Mode Routing section below.
Question Type Classification (6 Types)
Read the question and classify it as 1 of 6 types, then enter the relevant procedure. When ambiguous, default to Topic.
| Type | Trigger | Recommended --edge-type |
Entry tool |
|---|---|---|---|
| Contradiction | "Contradiction between A and B?", "What's at issue?" | contradicts |
tools/query.py graph neighbors X --edge-type contradicts |
| Entity-centric | "What does X cite·oppose?" | cites,contradicts |
tools/query.py graph neighbors X |
| Topic | "Overview of field X", "Definition of X" | defines,cites |
mcp__qmd__query (structured: intent+vec/lex) |
| Fact | "Did X announce Y?" | cites |
mcp__qmd__query (lex-centered) |
| Relation | "Relationship between A and B?" | (full default) | tools/query.py graph path A B |
| Meta | "Wiki structure", "Hub distribution" | (full default) | read root meta pages |
Additional natural-language intent mapping (when the user does not state one of the 6 types):
- "where opinions diverge" · "controversy" →
--edge-type contradicts - "primary source" · "announcement·citation" →
--edge-type cites - "defined concept" →
--edge-type defines - "simple mention" →
--edge-type references - "appearing together" · "co-occurring" →
--edge-type inferred
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.
- 12d ago First seen · 103 lines · 0 tokens per session scan A 4d34c66eb109
wiki-query is a command published in the GitHub repository alfadur7/llm-wiki-newsroom (85 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,670 tokens. 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 2026-08-30.
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