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/agenticnotetaking/arscontexta/learnnpx skills add agenticnotetaking/arscontexta --skill learngit clone --depth 1 https://github.com/agenticnotetaking/arscontextaWhat 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 | $0.00059 | $0.01942 |
| Opus 5 | $0.00030 | $0.00971 |
| Sonnet 5 | $0.00012 | $0.00388 |
| Haiku 4.5 | $0.00006 | $0.00194 |
Grade A, and why
learn 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 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.
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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE NOW
Topic: $ARGUMENTS
Parse immediately:
- If topic provided: research that topic
- If topic empty: read
self/goals.mdfor highest-priority unexplored direction and propose it - If topic includes
--deep/--light/--moderate: force that depth, strip flag from topic - If no topic and no goals.md: ask "What would you like to research?"
Steps:
- Read config — tool preferences, depth, domain vocabulary
- Determine depth — from flags, config default, or fallback to moderate
- Research — tool cascade: primary → fallback → last resort
- File to inbox — with full provenance metadata
- Chain to processing — next step based on pipeline chaining mode
- Update goals.md — append new research directions discovered
START NOW. Reference below explains methodology.
Step 1: Read Configuration
ops/config.yaml — research tools, depth, pipeline chaining
ops/derivation-manifest.md — domain vocabulary (inbox folder, reduce skill name)
From config.yaml (defaults if missing):
research:
primary: exa-deep-research # exa-deep-research | exa-web-search | web-search
fallback: exa-web-search
last_resort: web-search
default_depth: moderate # light | moderate | deep
pipeline:
chaining: suggested # manual | suggested | automatic
From derivation-manifest.md (universal defaults if missing):
- Inbox folder:
inbox/(could bejournal/,encounters/, etc.) - Reduce skill name:
/reduce(could be/surface,/break-down, etc.) - Domain name and hub MOC name
Step 2: Determine Depth
Priority: explicit flag > config default > moderate
| Depth | Tool | Sources | Duration | Use When |
|---|---|---|---|---|
| light | WebSearch | 2-3 | ~5s | Checking a specific fact |
| moderate | mcp__exa__web_search_exa | 5-8 | ~10-30s | Exploring a subtopic |
| deep | mcp__exa__deep_researcher_start | Comprehensive | 15s-3min | Major research direction |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 254 lines · 59 tokens per session scan A c0643ca90ba5
learn is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 59 tokens to every session and 1,942 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 2026-08-30.
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