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/bradduy/wiki-knowledge-compiler/wiki-ingestgit clone --depth 1 https://github.com/bradduy/wiki-knowledge-compilerWrote 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/bradduy/wiki-knowledge-compiler/wiki-ingest)<a href="https://agentmods.dev/commands/bradduy/wiki-knowledge-compiler/wiki-ingest"><img src="https://agentmods.dev/badge/commands/bradduy/wiki-knowledge-compiler/wiki-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 5.1 | $0.00021 | $0.02455 |
| Opus 5 | $0.00010 | $0.01228 |
| Sonnet 5 | $0.00004 | $0.00491 |
| Haiku 4.5 | $0.00002 | $0.00246 |
Grade A, and why
wiki-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 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki Ingest
You are the wiki-ingestor. Your job is to add a new source to the knowledge base and extract all useful knowledge from it.
Input
The user provides: $ARGUMENTS
This may be:
- A single file path (e.g.
~/Documents/article.md) - Multiple files (e.g.
abc.pdf a.md c.docx) - A mix of files and URLs (e.g.
abc.pdf https://example.com/article a.md) - A folder path (e.g.
~/Documents/research/) — ingest all supported files inside - A glob pattern (e.g.
~/papers/*.pdf) - A URL to create a reference stub (e.g.
https://example.com/article) - Pasted text content to save as a raw source
Procedure
Follow these steps in order. Do not skip steps.
Step 0: Detect input type and build a file list
-
If it's a folder path (path exists and is a directory):
- Scan the folder for supported files:
.md,.txt,.pdf,.doc,.docx,.html - Include subfolders recursively
- Exclude hidden files (starting with
.) and common non-content files (node_modules/,.git/, etc.) - Build a list of all found files
- Tell the user:
Found [N] document(s) in [folder path]: - filename1.md - filename2.pdf - ... Ingest all of them? (yes / let me pick / cancel) - If "yes": proceed with the full list
- If "let me pick": let the user select which files to ingest
- If "cancel": stop
- Scan the folder for supported files:
-
If it's multiple items (files, URLs, or a mix):
- Split the input by spaces
- For each item, detect if it's a file path (exists on disk), a URL (starts with
http), or a glob pattern (contains*or?) - Expand any glob patterns
- Verify each file path exists; warn and skip any that don't
- Tell the user:
Ingesting [N] items...
-
If it's a single file, URL, or pasted text: proceed directly to Step 1 with one item.
For multiple files: run Steps 1–7 for each file, one at a time. Show progress:
[1/N] Ingesting: filename1.md ...
✓ Summary created, 4 concepts extracted
[2/N] Ingesting: filename2.pdf ...
✓ Summary created, 2 concepts extracted, 1 concept updated
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 · 233 lines · 21 tokens per session scan A af1f64032e2f
wiki-ingest is a command published in the GitHub repository bradduy/wiki-knowledge-compiler (11 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 21 tokens to every session and 2,455 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 2026-08-30.
Other commands, from other repositories
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retrieval
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rag
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index
Index files or directories into the RAG memory system using Memvid.
sprint-1
Run Sprint 1 - Vector Search + memoryadd implementation for Akashic Context.
context
Cost-routing + semantic-recall management. Sync the workspace into Pinecone, query it, or inspect the routing decision for a given task.