Borrowing it
Nothing to install: this file belongs to ystreibel/logseq-wiki. 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/ystreibel/logseq-wiki/main/.skills/wiki-narrate/SKILL.mdgit clone --depth 1 https://github.com/ystreibel/logseq-wikiWrote 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/ystreibel/logseq-wiki/wiki-narrate)<a href="https://agentmods.dev/skills/ystreibel/logseq-wiki/wiki-narrate"><img src="https://agentmods.dev/badge/skills/ystreibel/logseq-wiki/wiki-narrate/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/ystreibel/logseq-wiki/wiki-narrate"><img src="https://agentmods.dev/badge/skills/ystreibel/logseq-wiki/wiki-narrate.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 | $0.00109 | $0.01098 |
| Opus 5.5 | $0.00044 | $0.00439 |
| Sonnet 5.5 | $0.00022 | $0.00220 |
| Haiku 4.5 | $0.00011 | $0.00110 |
Grade A, and why
wiki-narrate 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 2mo 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki Narrate — Developed Briefing
REQUIRED: Invoke llm-wiki skill first for Logseq syntax and file format rules.
narrate ≠ query
| wiki-query | wiki-narrate | |
|---|---|---|
| Question | closed, pointed | open, transversal topic |
| Output | minimal answer (a few lines) + 1–2 sources | long structured exposé, narrative arc, abundant citations |
| Method | grep → read targeted pages → answer | aggregate many pages, rebuild a thread, surface tensions and gaps |
A briefing is not a padded query: it makes the pages talk to each other instead of juxtaposing them. Read-only by default.
Before You Start
- Resolve config — read
~/.logseq-wiki/configfirst (cross-project); fall back to.env. GivesLOGSEQ_VAULT_PATH. - If
wiki/_hot.mdexists, read it first for recent-activity context. - Read
wiki/_master-index.mdto understand scope and themes.
Step 1: Gather (multi-angle sweep)
Do not stop at the first batch:
- Broad, multilingual grep on bodies +
tags::: the term plus FR/EN synonyms (e.g. "AI/IA/LLM/agents") tags::overlap to find the core pages- Transitive link-following through
[[wiki/...]](the## Related/## Lienssections) to widen scope - Classify core / context / false positives from the grep — explicitly drop off-topic hits
- Trace back to original sources:
sources::points intopages/(namespace encoded___:[[web/x]]= filepages/web___x.md,[[videos/x]]=pages/videos___x.md). For a briefing, open at least the core pages' sources to verify a number or pull an exact quote before asserting it.
Respect the Retrieval Primitives table in llm-wiki — cheapest primitive first, escalate only when needed.
Step 2: Structure the Exposé
A narrative plan that links the pages, not one-section-per-page:
- Executive summary (2–3 sentences: the transversal message)
- Thesis — the thread that unifies the pages
- Thematic sections (by angle, not by page) with transitions
- Cross-page synthesis: make sources talk — agreements and contrasts. A contradiction between two pages becomes a paragraph of analysis, not an inconvenience.
- Conclusion + gaps: what the wiki does not yet say
- Bibliography at the end
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.
- 2mo ago First seen · 85 lines · 109 tokens per session scan A b5108da4ca9d
wiki-narrate is a skill published in the GitHub repository ystreibel/logseq-wiki (5 stars, last pushed 11d ago), licensed MIT. It adds 109 tokens to every session and 1,098 once invoked, about $0.0004 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.
wiki-narrate compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| wiki-narrateystreibel/logseq-wiki | Skill | 5 | 11d ago | MIT | A | 109 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |