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/robinslange/learning-loop/ingestnpx skills add robinslange/learning-loop --skill ingestgit clone --depth 1 https://github.com/robinslange/learning-loopWhat 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 | $0.00119 | $0.06354 |
| Opus 5 | $0.00060 | $0.03177 |
| Sonnet 5 | $0.00024 | $0.01271 |
| Haiku 4.5 | $0.00012 | $0.00635 |
Grade C, and why
ingest scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$HOME/Library/Application Support/ygrep/indexes/"* 2>/dev/null || true How it starts
The opening of the file, as written. The whole thing — 415 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest: External Context Import
Overview
Pulls data from external sources (Linear, repositories, or any content Claude can read), extracts atomic insights, previews them for confirmation, then routes to auto-memory and/or vault notes. The context mode accepts anything: PDFs, images, code files, conversation dumps, documents, or plain text.
When to Use
/ingest linear: pull my assigned Linear tickets/ingest linear "Project Name": pull tickets from a specific project/ingest linear --state "In Progress": filter by ticket state/ingest repo ~/path/to/repo: scan a repository/ingest repo: prompt for repo path/ingest context: provide any content (paste text, give a file path, drop an image)/ingest bundle <path>: restore aharvest-bundle-<date>/carried from another instance you own (verbatim restore, no insight extraction)/ingest: ask which source type--refine: append to any source mode (e.g.,/ingest context --refine) to enable Step 5.6 upstream refinement after ingest. Off by default; will move to default-on after a few validation runs.
Process
Step 0: Parameter Resolution
Parse the source type from the first argument.
No argument (/ingest):
Use AskUserQuestion:
What would you like to ingest?
- linear: Pull Linear tickets (my assigned, or a specific project)
- repo: Scan a repository for architecture and patterns
- context: Provide any content (text, PDF, image, code, doc) to extract insights from
- bundle: Restore a harvest bundle carried from another instance you own
Source type provided: Parse remaining args as source-specific parameters.
Step 1: Resolve Source Parameters
Linear:
- No additional args → scope = "me" (all assigned tickets)
- Quoted string arg → scope = that project name
--state "X"→ state filter- Announce: "Pulling Linear tickets ({scope})..."
Repo:
- Path arg → use it
- No path →
AskUserQuestion: "Which repository? (full path)" - Verify path exists with
ls - Announce: "Scanning {path}..."
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.
- 2d ago First seen · 415 lines · 119 tokens per session scan C 127dec63e9fd
ingest is a skill published in the GitHub repository robinslange/learning-loop (11 stars, last pushed 11d ago), licensed Apache-2.0. It adds 119 tokens to every session and 6,354 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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