learning-path-designer

A tool for arranging learning into a short sequence based on someone's role, skill gaps, goals, and available time.

In plain words
What is it for?
Use it to create a learning roadmap with study, practice, small projects, recall, reflection, and review tasks.
Why use it?
It helps people who know what they want to achieve but do not know what to learn first or how to build confidence step by step.

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/grcengineering/companion/learning-path-designer
Any agent
npx skills add grcengineering/companion --skill learning-path-designer
Clone the repo
git clone --depth 1 https://github.com/grcengineering/companion

Made for: Claude Code, Codex.

Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 563 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 $0.00062 $0.00563
Opus 5 $0.00031 $0.00282
Sonnet 5 $0.00012 $0.00113
Haiku 4.5 $0.00006 $0.00056

Measured 2d ago against content hash e066266f38f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

learning-path-designer 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/learning-path-designer/SKILL.md · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

learning-path-designer

What

Sequence the learner's next few reps. The output is a learning path, not a programme plan.

When

  • The learner is new and does not know where to start.
  • The learner has a target role, task, or credibility goal but needs sequencing.
  • The learner is changing roles or trying to build confidence.
  • The learner asks for a curriculum, roadmap, or next steps.

Not For

  • A corpus reading list without practice. Use reading-guide.
  • A hands-on lab spec. Use lab-builder.
  • Operational maturity planning or control remediation.

Inputs

  • Role or target role.
  • Current level and known gaps.
  • Goal and time available.
  • Optional learner profile from profile/.

Steps

  1. Ask for role, current skill level, goal, and weekly timebox.
  2. Ask one retrieval question: "What do you already understand about this area?"
  3. Identify the next three milestones, each one step harder than the current level.
  4. Mix concept study, practice scenario, small build, recall, and reflection.
  5. Assign one small artefact per milestone.
  6. Add a spaced review prompt.

Validation

  • The path has exactly enough scope for the timebox.
  • Each milestone has a visible learning artefact.
  • The learner knows what to do first without needing a menu of skills.

Gotchas

  • If the learner asks for a 90-day operational programme plan, convert it into a 90-day learning plan.
  • If the goal is vague, propose a short diagnostic question before sequencing.
  • If the learner has too little time, reduce milestone depth instead of creating a heroic schedule.

Failure Modes

  • Path is too generic: anchor each step to the learner's role or target role.
  • Path is too passive: add practice, build, or explain-back reps.
  • Path becomes advice: remove live programme actions and use fictional or sanitized practice.

Examples

  • User asks "I am new to TPRM, where do I start?" -> Ask current understanding, then create three milestones covering concepts, a fictional review scenario, and recall.
  • User wants to move from IT audit to GRC engineering -> Sequence control ownership, evidence pipelines, policy-as-code concepts, and a toy build.
  • User has four hours this week -> Produce one small milestone, one reading, one recall check, and one reflection prompt.

Read the full file on GitHub · 64 lines

Files

What ships with it

3 files 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 · 64 lines · 62 tokens per session scan A e066266f38f2

Subscribe to this mod's changes

learning-path-designer is a skill published in the GitHub repository grcengineering/companion (32 stars, last pushed 3mo ago), licensed MIT. It adds 62 tokens to every session and 563 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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