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 agentmods add skills/saski/arnesto/cross-linkernpx skills add saski/arnesto --skill cross-linkergit clone --depth 1 https://github.com/saski/arnestoWrote 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/saski/arnesto/cross-linker)<a href="https://agentmods.dev/skills/saski/arnesto/cross-linker"><img src="https://agentmods.dev/badge/skills/saski/arnesto/cross-linker.svg" alt="Measured on agentmods" 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 | $0.00137 | $0.02317 |
| Opus 5 | $0.00068 | $0.01158 |
| Sonnet 5 | $0.00027 | $0.00463 |
| Haiku 4.5 | $0.00014 | $0.00232 |
Grade A, and why
cross-linker 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 5d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Linker — Automated Wiki Cross-Referencing
You are weaving the wiki's knowledge graph tighter by finding and inserting missing [[wikilinks]] between pages that should reference each other but currently don't.
Follow the Retrieval Primitives table in llm-wiki/SKILL.md. Build the registry in Step 1 by grepping frontmatter only (not full pages). Reserve full Read for the unlinked-mention detection pass, and even there, only read pages whose summaries/titles make them plausible link targets. Blind full-vault reads are what this framework exists to avoid.
Before You Start
- Read
.envto getOBSIDIAN_VAULT_PATH - Read
index.mdto get the full inventory of pages and their one-line descriptions - Skim
log.mdto see what was recently ingested (focus linking effort on new pages)
Step 1: Build the Page Registry
Glob all .md files in the vault (excluding _archives/, .obsidian/). For each page, extract:
- Filename (without
.md) — this is the wikilink target - Title from frontmatter
- Aliases from frontmatter (if any)
- Tags from frontmatter
- Category from frontmatter or directory inference
- One-line summary — first sentence or
titlefield
Build a lookup table:
page_name → { path, title, aliases, tags, summary }
This is your "vocabulary" — every entry in this table is a valid wikilink target.
Step 2: Scan for Missing Links
For each page in the vault:
-
Read the full content
-
Extract existing wikilinks — find all
[[...]]references already present -
Search for unlinked mentions — check if the page's text contains any of these, without being wrapped in
[[...]]:- Page filenames (e.g., the word "MyProject" appears but
[[projects/my-project/my-project]]is missing) - Page titles from frontmatter
- Aliases from frontmatter
- Entity names, project names, concept names from the registry
- Page filenames (e.g., the word "MyProject" appears but
-
Check for semantic connections — pages that share multiple tags or are in the same project directory but don't link to each other
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.
- 5d ago First seen · 202 lines · 137 tokens per session scan A f7651477a34d
cross-linker is a skill published in the GitHub repository saski/arnesto (5 stars, last pushed 10d ago), licensed Unlicense. It adds 137 tokens to every session and 2,317 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
immune
Hybrid adaptive memory: Cheatsheet (positive patterns pre-generation) and Immune (negative patterns post-generation) with Hot/Cold tiered auto-learning. Triggers on: "scan for errors", "immune scan", "check output quality", "antibody scan". NOT for PR review (use pr-review) or repo audits (use repo-sentinel).
usage-audit
Audit a Claude Code setup for token waste and context bloat. Checks MCP servers, CLAUDE.md, skills, and settings against bloat filters. Triggers on: "audit my context", "usage audit", "token audit", "context bloat". NOT for codebase audits.
brain-ingest
The process for digesting a conversation, document, or research result, classifying it, and writing it down as brain content (a root-page update or a new/updated page) through the brain CLI.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
ontology-absorb-confluence
Read a wiki page through a user-registered third-party MCP, classify it with absorbdocument dry-run, obtain approval, and land only approved candidates with the source URL cited.
ontology-extract
Extract a small, evidence-bound set of ontology candidates from prose, check the existing vault for duplicates, obtain user approval, and land only the approved nodes and relations.