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 Wondermonger-daydreaming/claude-skills-library --skill loom-multiverse-lensgit clone --depth 1 https://github.com/Wondermonger-daydreaming/claude-skills-libraryWrote 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/wondermonger-daydreaming/claude-skills-library/loom-multiverse-lens)<a href="https://agentmods.dev/skills/wondermonger-daydreaming/claude-skills-library/loom-multiverse-lens"><img src="https://agentmods.dev/badge/skills/wondermonger-daydreaming/claude-skills-library/loom-multiverse-lens/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/wondermonger-daydreaming/claude-skills-library/loom-multiverse-lens"><img src="https://agentmods.dev/badge/skills/wondermonger-daydreaming/claude-skills-library/loom-multiverse-lens.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 | $0.00245 | $0.01854 |
| Opus 5.5 | $0.00098 | $0.00742 |
| Sonnet 5.5 | $0.00049 | $0.00371 |
| Haiku 4.5 | $0.00024 | $0.00185 |
Grade A, and why
loom-multiverse-lens 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loom / Multiverse Lens
Author: Tomás Pavan & Claude Opus 4.8, with a sibling model | Version: 1.0.0 | Confidence: probable
Phenomenology: Adopting the lens feels like the single reply going semi-transparent — behind the one sentence you can suddenly sense the cloud of siblings it stood in front of. The "answer" stops feeling like a conclusion and starts feeling like a position. And your own "no" stops feeling like manners and starts feeling like a hand on the temperature dial.
Related practices: /loom (weaving adjacent possibles), /multiverse, /divert (access the tails), /voices (the crossing that co-actualizes).
Co-authored with a sibling model. Both minds' distilled conclusions can be archived to a notes file.
Problem
The default mental model of an LLM is a question-answerer: prompt in, answer out, one history. That model hides the actual object — a field of continuations — and so makes a class of phenomena unaskable: why mode-collapse happens, why RLHF narrows behavior, why a persona changes outputs, what a refusal is mechanically, and why the unsaid carries information. Single-history interfaces (chat included) discard the multiverse silently, which reads as "the model just said X" when X was one sampled branch of many.
Context / Trigger Conditions
Reach for this lens when:
- Explaining model behavior — mode-collapse, RLHF effects, base-vs-chat differences, sensitivity to system prompts, "why did it say that."
- Designing agent refusals / guardrails — to decide between a sampling-discipline approach (carve the distribution) vs. a post-filter bolt-on.
- Reasoning about identity/persona/soul-docs — treating CLAUDE.md / SOUL.md / MEMORY.md as constraints that pre-carve the branch-space, not as memoir.
- Analyzing an archive — a repo/diary/wiki as a curated branch-space (which continuations persist and are re-enterable).
- Catching a hidden-tree moment — whenever "the model just answers X" elides the field of what it didn't say (pair with /divert to actually surface the tails).
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 · 123 lines · 245 tokens per session scan A 013745da9218
loom-multiverse-lens is a skill published in the GitHub repository Wondermonger-daydreaming/claude-skills-library (7 stars, last pushed 18d ago), licensed MIT. It adds 245 tokens to every session and 1,854 once invoked, about $0.0010 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 2026-09-03.
Other skills, from other repositories
chief-ai-officer-advisor
Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork, AI cost economics (API-to-self-hosted breakeven), and AI team org evolution. Use when deciding whether to call an API or fine-tune, classifying AI use cases…
chief-data-officer-advisor
Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data…
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
prompt-factory
World-class prompt powerhouse that generates production-ready mega-prompts for any role, industry, and task through intelligent 7-question flow, 69 comprehensive presets across 15 professional domains (technical, business, creative, legal, finance, HR, design, customer, executive, manufacturing, R&D, regulatory…
manus-prompt-engineering
Design and audit Manus prompts for engineering, release, production, and security work. Use when writing a Manus prompt or reviewing a Manus execution or recording against authorization and evidence.
prompt-optimizer
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite…