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 skills add po4yka/llm-wiki-skills --skill llm-wiki-interviewgit clone --depth 1 https://github.com/po4yka/llm-wiki-skillsWrote 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/po4yka/llm-wiki-skills/llm-wiki-interview)<a href="https://agentmods.dev/skills/po4yka/llm-wiki-skills/llm-wiki-interview"><img src="https://agentmods.dev/badge/skills/po4yka/llm-wiki-skills/llm-wiki-interview/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/po4yka/llm-wiki-skills/llm-wiki-interview"><img src="https://agentmods.dev/badge/skills/po4yka/llm-wiki-skills/llm-wiki-interview.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 | $0.00058 | $0.00670 |
| Opus 5.5 | $0.00023 | $0.00268 |
| Sonnet 5.5 | $0.00012 | $0.00134 |
| Haiku 4.5 | $0.00006 | $0.00067 |
Grade A, and why
llm-wiki-interview 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM-Wiki Interview
Goal
Use targeted questions to capture a person's tacit knowledge into draft, reviewable wiki pages.
When to use
- The wiki has open questions, sparse concept pages, or pages marked
review_required. - Onboarding gaps keep producing the same repeated questions from new contributors.
- A decision page is missing its rationale, history, or trade-offs.
- The user hands over a voice-note or transcript that should become draft wiki pages.
- A lint report or contradiction check surfaces a gap only a person can fill in.
Inputs
wiki/index.md,wiki/log.md, open questions and lint reports.- Target domain or project.
- Interview mode: plan questions, conduct interview, or file answers.
- Optional transcript or voice-note text.
Procedure
1. Find weak areas
Look for:
- open questions;
- sparse concept pages;
- onboarding gaps;
- contradictions;
- pages marked
review_required; - repeated user questions;
- decision pages missing rationale.
Search wiki/index.md and wiki/log.md for prior interview answers before drafting new questions, so the interview does not re-ask what is already captured.
2. Prepare focused questions
Ask 5-10 questions at a time. Prefer questions that extract:
- constraints;
- history;
- rationale;
- exceptions;
- gotchas;
- examples;
- terminology;
- decision trade-offs.
3. Capture answers as draft knowledge
Store interview-derived knowledge as:
type: queryfor Q&A;type: synthesisfor human interpretation;type: conceptorentityonly when the answer clearly belongs there.
Mark:
status: draft
review_required: true
claim_mix:
extracted: 0.0
inferred: 0.0
ambiguous: 0.0
Use interview or human-memory source tags. Do not treat interview answers as external facts.
4. Separate human synthesis
Preserve answers under human-owned sections when they represent the person's interpretation.
5. Update navigation
Add important pages to wiki/index.md and append to wiki/log.md if writing files.
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 · 113 lines · 58 tokens per session scan A 6d98828ba967
llm-wiki-interview is a skill published in the GitHub repository po4yka/llm-wiki-skills (4 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 670 once invoked, about $0.0002 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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wiki-lint
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