recommend

A research-based adviser for designing a personal knowledge system. It uses a library of research claims, methods, components, and constraints to shape its recommendations.

In plain words
What is it for?
Use it to describe a knowledge-management need and receive architecture recommendations with reasons and research support.
Why use it?
It helps choose a knowledge-system structure that fits a specific use case, limits, and goals instead of relying on generic advice.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/agenticnotetaking/arscontexta/recommend
Any agent
npx skills add agenticnotetaking/arscontexta --skill recommend
Clone the repo
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta

Made for: Claude Code, Codex.

Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,605 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00056 $0.04605
Opus 5 $0.00028 $0.02302
Sonnet 5 $0.00011 $0.00921
Haiku 4.5 $0.00006 $0.00460

Measured 2d ago against content hash 16f298291924, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

recommend 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.

skills/recommend/SKILL.md · 558 lines

How it starts

The opening of the file, as written. The whole thing — 558 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Runtime Configuration (Step 0 — before any processing)

Read these files to configure recommendation behavior:

  1. ${CLAUDE_PLUGIN_ROOT}/reference/tradition-presets.md — tradition and use-case presets

    • Pre-validated coherence points in the 8-dimension space
    • Starting points for customization, not final answers
  2. ${CLAUDE_PLUGIN_ROOT}/reference/methodology.md — universal methodology principles

  3. ${CLAUDE_PLUGIN_ROOT}/reference/components.md — component blueprints (what can be toggled)

  4. ${CLAUDE_PLUGIN_ROOT}/reference/dimension-claim-map.md — maps each dimension position to supporting research claims

  5. ${CLAUDE_PLUGIN_ROOT}/reference/interaction-constraints.md — hard blocks, soft warns, cascade effects between dimensions

  6. ${CLAUDE_PLUGIN_ROOT}/reference/claim-map.md — topic navigation for the research graph

If any reference file is missing, note the gap but continue with available information. The recommendation degrades gracefully — fewer citations, same structure.


EXECUTE NOW

Target: $ARGUMENTS

Parse immediately:

  • If target is empty or a question: enter conversational mode — ask 1-2 clarifying questions, then recommend
  • If target contains a use case description: proceed directly to recommendation mode
  • If target contains --compare [A] [B]: enter comparison mode — compare two presets or configurations

START NOW. Reference below defines the workflow.


Philosophy

Advisory, not generative.

/recommend exists for exploration. The user is considering a knowledge system — maybe they have a use case, maybe they're comparing approaches, maybe they're curious what the research says about a specific pattern. /recommend answers with specific, research-backed reasoning without creating any files.

This is the entry point before commitment. /setup generates a full system. /recommend sketches what that system would look like and WHY, so the user can decide whether to proceed. Every recommendation traces to specific research claims. "I recommend X" is never enough — "I recommend X because [[claim]]" is the minimum.

Read the full file on GitHub · 558 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 558 lines · 56 tokens per session scan A 16f298291924

Subscribe to this mod's changes

recommend is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 56 tokens to every session and 4,605 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.

Related

Other skills, from other repositories

dogfood

Run a dogfooding session for hyalo — build from source, exercise the CLI against real knowledgebases (own KB, MDN, GitHub Docs, VS Code docs), find bugs, verify recent fixes, assess UX, measure performance, and write a structured report. Use this skill whenever the user says /dogfood, "dogfood", "run a dogfood…

ractive/hyalo · 102 tokens

release

Cut a hyalo release end-to-end — verify/bump the workspace version, rotate the changelog with hyalo itself, sync the winget fork, publish the GitHub release with curated + auto-generated notes, and watch the pipeline. Use this skill whenever the user wants to release a new hyalo version, cut/tag/publish vX.Y.Z, says…

ractive/hyalo · 102 tokens

review-rust

Perform critical Rust code reviews covering correctness, edition 2024 compliance, error handling, API design, async pitfalls, and dependency hygiene. ALWAYS use this skill when the user wants to review, audit, or critically evaluate Rust code — whether that's a PR diff, a specific crate or module, a cross-cutting…

ractive/hyalo · 177 tokens

security-audit

REQUIRED skill for any security-related request. Use this skill whenever the user wants to find anything dangerous, sensitive, or risky in their code, files, or repository. This includes but is not limited to: scanning for secrets/keys/tokens/credentials, checking dependencies for vulnerabilities, auditing destructive…

ractive/hyalo · 150 tokens

hyalo-tidy

../../../crates/hyalo-cli/templates/skill-hyalo-tidy.md.

ractive/hyalo · 0 tokens

hyalo

../../../crates/hyalo-cli/templates/skill-hyalo.md.

ractive/hyalo · 0 tokens