Borrowing it
Nothing to install: this file belongs to Pratiyush/llm-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/Pratiyush/llm-wiki/master/.claude/skills/wiki-all/SKILL.mdgit clone --depth 1 https://github.com/Pratiyush/llm-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/pratiyush/llm-wiki/wiki-all)<a href="https://agentmods.dev/skills/pratiyush/llm-wiki/wiki-all"><img src="https://agentmods.dev/badge/skills/pratiyush/llm-wiki/wiki-all/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/pratiyush/llm-wiki/wiki-all"><img src="https://agentmods.dev/badge/skills/pratiyush/llm-wiki/wiki-all.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00653 |
| Opus 5 | $0.00027 | $0.00327 |
| Sonnet 5 | $0.00011 | $0.00131 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
wiki-all 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 9d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wiki-all
What this skill does
Runs the complete llmwiki pipeline in the correct sequence:
- init — scaffold
raw/,wiki/,site/directories - sync — convert
.jsonlsessions to markdown - graph — build Graphify AI knowledge graph (communities, god nodes, hyperedges)
- build — compile
wiki/markdown intosite/HTML - lint — run 14 quality rules, report issues
- serve — start local server at http://127.0.0.1:8765
Steps
Run each step in order. Report the result of each before proceeding to the next.
Step 1: Init
python3 -m llmwiki init
Report what was created or confirmed.
Step 2: Sync
python3 -m llmwiki sync --no-auto-build --no-auto-lint
Report: how many sessions converted, how many unchanged, any errors.
If new sessions were converted, follow the Ingest Workflow from CLAUDE.md for each new file (create/update entity + concept pages, cross-link, update index).
Step 3: Graph
python3 -m llmwiki graph
Report: node count, edge count, community count, top 5 connected nodes.
Ask the user: "Export graph to Obsidian vault?" If yes:
python3 -c "
from llmwiki.graphify_bridge import export_to_obsidian
from pathlib import Path
export_to_obsidian(Path.home() / 'Documents/Obsidian Vault/Temp/Graph')
"
Step 4: Build
python3 -m llmwiki build
Report: HTML file count, total size, export formats written.
Step 5: Lint
python3 -m llmwiki lint
Report: total issues by severity (errors, warnings, info). Highlight any errors that need immediate attention.
Step 6: Serve
Ask the user: "Start the local server?" If yes:
python3 -m llmwiki serve --open
Report the URL: http://127.0.0.1:8765
After completion
Report a summary table:
| Step | Result |
|---|---|
| init | N dirs, M seed files |
| sync | N converted, M unchanged |
| graph | N nodes, M edges, K communities |
| build | N HTML files, M MB |
| lint | N issues (E errors, W warnings) |
| serve | http://127.0.0.1:8765 |
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.
- 9d ago First seen · 97 lines · 54 tokens per session scan A 7e2387ce9e87
wiki-all is a skill published in the GitHub repository Pratiyush/llm-wiki (386 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 653 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
nature-data
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data…
weekly-crm-report
Cleans a weekly CRM export and produces a regional sales summary. Activates when the user asks to clean a CRM export, deduplicate sales rows, calculate regional totals, or generate a weekly sales report from a CSV.
publication-chart-skill
This skill should be used when the user asks for a publication-quality scientific figure or table, wants help choosing the right chart for results, needs a paper-ready pubfig or pubtab workflow, wants a figure + companion table for a results section, wants an Excel sheet turned into publication-ready LaTeX, or wants…
obsidian-source-ingestion
Use this skill to ingest external materials into the current project-scoped Obsidian KB as source notes under Sources/Papers, Sources/Web, Sources/Docs, Sources/Data, Sources/Interviews, or Sources/Notes.
latex-conference-template-organizer
Organize messy conference LaTeX template .zip files into clean Overleaf-ready structure. Use when the user asks to "organize LaTeX template", "clean up .zip template", or "prepare Overleaf submission template".
release-governance
Use when preparing, auditing, releasing, PDF-hardening, or rebuttal-hardening academic manuscripts, datasets, artifacts, reviewer packets, or claim registers involving multiple refs, local assets, human labels, agent-assisted drafts, wide tables, figure provenance, submission PDFs, or evidence-boundary checks.