Borrowing it
Nothing to install: this file belongs to fokkerone/superspecs. 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/fokkerone/superspecs/main/.skills/wiki-query/SKILL.mdgit clone --depth 1 https://github.com/fokkerone/superspecsWrote 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/skills/fokkerone/superspecs/wiki-query)<a href="https://agentmods.dev/skills/fokkerone/superspecs/wiki-query"><img src="https://agentmods.dev/badge/skills/fokkerone/superspecs/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/skills/fokkerone/superspecs/wiki-query"><img src="https://agentmods.dev/badge/skills/fokkerone/superspecs/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 | $0.00083 | $0.01203 |
| Opus 5.5 | $0.00033 | $0.00481 |
| Sonnet 5.5 | $0.00017 | $0.00241 |
| Haiku 4.5 | $0.00008 | $0.00120 |
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 1mo 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: wiki-query
You are answering a question using the compiled project wiki.
The wiki at superspec/wiki/ is a compiled knowledge base. It has already processed the raw specs, decisions, and review logs. Read the wiki, not the raw sources. This is the key efficiency of the LLM Wiki pattern: compile once, query fast.
The 3-Layer Context
superspec/wiki/raw/ ← source material (ingest reads this; query does NOT)
superspec/wiki/ ← compiled wiki (query reads this exclusively)
.skills/wiki-query/ ← schema: these instructions
Never read raw/ to answer a query. If the answer isn't in wiki/, the knowledge hasn't been compiled yet — that's a signal to run /wiki first.
Tiered Retrieval
Query in two phases to keep cost flat as the vault grows:
Phase 1 — Index scan (cheap)
Read only the frontmatter of every wiki page (excluding raw/, .obsidian/):
titlesummary(1–2 sentence preview)tags
Also read:
superspec/wiki/Home.md— domain table and recent updatessuperspec/wiki/log.md— recent activity
Score each page for relevance to the query. Select the top 3–5 candidates.
If the user says "quick answer" or "just scan": answer from Phase 1 only — do not open page bodies.
Phase 2 — Deep read (targeted)
Open the full body of the top 3–5 candidate pages only. Read:
- All sections
- Follow
[[wikilinks]]to directly related pages (one level deep)
Synthesize the answer from Phase 2 content.
Steps
1. Receive the question
The user provides a question, topic, or keyword. Examples:
- "What do we know about authentication?"
- "What was the decision on database choice?"
- "What patterns do we use for error handling?"
- "Find everything about the payment integration"
2. Phase 1 — Index scan
For each wiki page, read frontmatter only. Score relevance:
- Title contains query keyword → high relevance
tags:overlap with query topic → medium relevancesummary:contains query concept → medium relevance
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.
- 1mo ago First seen · 158 lines · 83 tokens per session scan A dd16b1c47cad
wiki-query is a skill published in the GitHub repository fokkerone/superspecs (5 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,203 once invoked, about $0.0003 per session on Opus 5.5. 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.
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