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 commands/slowww-ai/second-brain/ingestgit clone --depth 1 https://github.com/slowww-ai/second-brainWrote 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/commands/slowww-ai/second-brain/ingest)<a href="https://agentmods.dev/commands/slowww-ai/second-brain/ingest"><img src="https://agentmods.dev/badge/commands/slowww-ai/second-brain/ingest.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 | $0.00015 | $0.00809 |
| Opus 5 | $0.00008 | $0.00404 |
| Sonnet 5 | $0.00003 | $0.00162 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
ingest 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 4d 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.
What it actually says
Your job is to turn new raw captures into clean, linked wiki notes.
Steps:
- Run
python scripts/ingest.py list-newto find raw files not yet referenced by any note. Also list any images or PDFs inwiki/raw/that don't yet have a companion.mdfile — those need to be processed too. - Non-text captures first. For every image or PDF in
raw/without a companion.md:- Read the file directly (Claude can see images and PDFs natively).
- Create
raw/<same-name>.mdwith frontmatter, a description, a full transcription of any visible text/tables, and animage:orpdf:reference line. - Do NOT modify or delete the original binary file.
- Treat the new companion
.mdas a raw capture and continue with the normal flow below.
- For each new raw markdown file (including companions just created):
a. Read it according to its length. First check line count (
wc -l <path>or the file header from Read). Then:- Under 500 lines — read the entire file in one pass. No shortcuts.
- 500–2000 lines — read the entire file, in sequential chunks if needed. Distill aggressively into short notes, but do not skip sections.
- Over 2000 lines — read the first ~200 lines to get structure and thesis, scan the table of contents / headers, then read in full the sections most relevant for distillation and linking. It's fine to skip appendices, repetitive examples, or sections clearly tangential to the core concepts.
- Always report read-coverage in your final summary as
read X/Y lines of <file>so shallow reads are never silent. If coverage is under 100%, note which sections you skipped and why, so the user can request a follow-up deep pass. b. Runpython scripts/ingest.py stamp <path>if it has no frontmatter yet. c. Identify the 1–3 core concepts in the file. d. For each concept, decide: does a matching note already exist inwiki/notes/? Usepython scripts/search.py "<concept>"to check. e. If yes → append a distilled paragraph to that note and cite the raw file (see raw/<filename>). f. If no → create a new note inwiki/notes/<slug>.mdwith proper frontmatter, a short summary, and[[wikilinks]]to any related existing notes. If the source was an image or PDF, embed it inline with.
- Add backlinks: if note A now references note B, make sure B has an entry pointing back to A when it's meaningful.
- Never modify or delete original files in
wiki/raw/(companion.mdfiles you just created are fine to edit). - Record each processed raw file in the ledger. After you finish distilling a raw file, run:
where the second argument is a comma-separated list of note IDs (filenames withoutpython scripts/ingest.py record <raw-filename> <note1,note2,...>.md) produced or updated from that raw file. This is how the ledger tracks what's been ingested — without it, the file will show up as "new" on every run. - At the end, print a short summary: how many raw files processed, how many notes created, how many notes updated, and which decisions you're least sure about so the user can correct you.
Remember: small, focused notes are better than big ones. Split when in doubt.
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.
- 4d ago First seen · 36 lines · 15 tokens per session scan A f1373ae27032
ingest is a command published in the GitHub repository slowww-ai/second-brain (10 stars, last pushed 20d ago), licensed MIT. It adds 15 tokens to every session and 809 once invoked, about $0.0001 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.
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