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/asknpx skills add agenticnotetaking/arscontexta --skill askgit 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 | $0.00087 | $0.04933 |
| Opus 5 | $0.00044 | $0.02466 |
| Sonnet 5 | $0.00017 | $0.00987 |
| Haiku 4.5 | $0.00009 | $0.00493 |
Grade A, and why
ask 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 — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE NOW
Question: $ARGUMENTS
If no question provided, ask the user what they want to know.
Execute these steps:
- Classify the question — determine which knowledge base tier(s) to consult (see Query Classification below)
- Search the knowledge base — route to appropriate tiers based on classification
- Read relevant claims and docs — load 3-7 most relevant sources fully (use
mcp__qmd__multi_getwhen reading multiple IDs) - Check user context — read
ops/derivation.mdif the question involves their specific system - Synthesize an answer — weave claims into a coherent, opinionated argument
- Cite sources — reference specific claims and documents so the user can explore further
START NOW. Reference below explains routing and synthesis methodology.
The Three-Tier Knowledge Base
The plugin's knowledge base has three distinct parts, each serving a different function. Effective answers often draw from multiple tiers.
Tier 1: Research Graph (WHY)
Location: ${CLAUDE_PLUGIN_ROOT}/methodology/ — filter by kind: research
Content: 213 interconnected research claims grounded in cognitive science, knowledge system theory, and agent cognition research.
Use for: Questions about principles, trade-offs, why things work, theoretical foundations.
What it contains:
- Claims about how knowledge systems work (human and agent)
- Cognitive science foundations (working memory, attention, retrieval)
- Methodology comparisons (Zettelkasten vs PARA, atomic vs compound)
- Design dimensions (trade-off spectrums with poles and decision factors)
- Failure modes and anti-patterns
- Agent-specific constraints (context windows, session boundaries)
Search strategy: Use mcp__qmd__deep_search (highest quality, LLM-reranked) for conceptual questions. Use mcp__qmd__vector_search for semantic exploration. Use mcp__qmd__search for known terminology. All searches use the methodology collection.
Tier 2: Guidance Docs (HOW)
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 · 394 lines · 87 tokens per session scan A c8dcf2750dfe
ask is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 87 tokens to every session and 4,933 once invoked, about $0.0004 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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