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 nweii/agent-stuff --skill archive-to-braingit clone --depth 1 https://github.com/nweii/agent-stuffWrote 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/nweii/agent-stuff/archive-to-brain)<a href="https://agentmods.dev/skills/nweii/agent-stuff/archive-to-brain"><img src="https://agentmods.dev/badge/skills/nweii/agent-stuff/archive-to-brain.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 | $0.00053 | $0.03304 |
| Opus 5 | $0.00026 | $0.01652 |
| Sonnet 5 | $0.00011 | $0.00661 |
| Haiku 4.5 | $0.00005 | $0.00330 |
Grade A, and why
archive-to-brain 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 7d 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Archive conversation to Brain vault
Create an archival summary of an AI conversation and save it to Nathan's Obsidian vault (Brain).
Deep analysis requirements
Conduct a thorough analysis of the entire conversation:
- Read through completely first, identifying all conceptual threads, task sequences, and transitions
- Note patterns in questioning, resistance points, breakthrough moments, or technical hurdles
- Identify the conversation's nature (technical work session, creative exploration, strategic planning, philosophical inquiry, etc.)
- Understand what made this particular exchange worth preserving (insight-driven vs. action-documentation)
- Determine what structure would best capture its unique value (narrative vs. log-formatted)
Look deeply for:
- The real question beneath the initial question
- How the problem space was redefined or the technical path was forged
- Moments where assumptions were challenged or implementation details were decided
- Conceptual frameworks or technical patterns that emerged organically
- The emotional/intellectual journey or the step-by-step progress of a work session
- Valuable tangents or "failed" approaches that taught something or informed the final code
- Connections made between seemingly unrelated ideas or system components
- What remained intentionally unresolved or deferred to later tasks
Creating descriptive structure
Instead of using generic headings like "Initial Question" or "Key Findings," create headings that describe the actual content of each section. The heading should give readers immediate context about what happened in that part of the conversation.
Examples of descriptive headings:
- "Starting from hourly vs. project pricing questions"
- "Why the recursive function kept hitting memory limits"
- "Exploring whether this needs to be real-time"
- "The confusion about state management"
- "Deciding between complexity and maintainability"
Use sentence-case for headings, not title case. Avoid marketing-speak, dramatic phrasing, or trying to be clever.
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.
- 7d ago First seen · 221 lines · 53 tokens per session scan A 4c4b23e758c6
archive-to-brain is a skill published in the GitHub repository nweii/agent-stuff (9 stars, last pushed 19d ago), licensed MIT. It adds 53 tokens to every session and 3,304 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 2026-08-31.
Other skills, from other repositories
obsidian
Obsidian vault: search/read/write notes, backlinks, Bases, Canvas.
loopx-doc-registry
Use when a connected LoopX project is asked to read, remember, record, index, register, or use a durable project material such as a Lark/wiki/design doc, research note, SOP, owner packet, migration report, benchmark paper, or external material source. Use even when the user does not mention LoopX or doc registry.
mindos-zh
A local knowledge-base assistant for storing and finding decisions, meeting notes, procedures, troubleshooting lessons, and preferences across conversations and coding agents. It works only with the MindOS knowledge base.
excel-author
Create and edit Excel (.xlsx) workbooks with openpyxl. Supports formulas, charts, formatting, and data analysis.
ppt-author
Create and edit PowerPoint (.pptx) presentations programmatically. Requires python-pptx.
ocr-document
Extract text from PDFs, images, and scanned documents. Uses pymupdf (local) or optional cloud OCR APIs.