path-optimizer

A learning-path planner that sequences concepts, schedules spaced repetition, places challenges that test whether knowledge transfers to new situations, and prioritises risky areas. Spaced repetition means reviewing information at planned intervals over time.

In plain words
What is it for?
Use it to order concepts, schedule reviews, place transfer challenges, and prioritise learning areas in the learner’s danger zone.
Why use it?
It helps avoid presenting topics in an unsuitable order and gives extra attention to concepts likely to cause trouble. It also plans reviews instead of relying on one-time study.

Agent for Claude Code

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 agents/idoforgod/vibe-learning-agenticworkflow/path-optimizer
Clone the repo
git clone --depth 1 https://github.com/idoforgod/Vibe-learning-AgenticWorkflow

Made for: Claude Code.

Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,211 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00029 $0.03211
Opus 5 $0.00015 $0.01605
Sonnet 5 $0.00006 $0.00642
Haiku 4.5 $0.00003 $0.00321

Measured 3d ago against content hash 3f1da217fc86, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

path-optimizer 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 3d 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.

.claude/agents/path-optimizer.md · 262 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

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. 3d ago First seen · 262 lines · 29 tokens per session scan A 3f1da217fc86

Subscribe to this mod's changes

path-optimizer is an agent published in the GitHub repository idoforgod/Vibe-learning-AgenticWorkflow (24 stars, last pushed 6mo ago), with no licence file. It adds 29 tokens to every session and 3,211 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.

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