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 skills add grcengineering/companion --skill learning-path-designergit clone --depth 1 https://github.com/grcengineering/companionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/grcengineering/companion/learning-path-designer)<a href="https://agentmods.dev/skills/grcengineering/companion/learning-path-designer"><img src="https://agentmods.dev/badge/skills/grcengineering/companion/learning-path-designer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/grcengineering/companion/learning-path-designer"><img src="https://agentmods.dev/badge/skills/grcengineering/companion/learning-path-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.00563 |
| Opus 5.5 | $0.00025 | $0.00225 |
| Sonnet 5.5 | $0.00012 | $0.00113 |
| Haiku 4.5 | $0.00006 | $0.00056 |
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 1mo 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 — 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
- Ask for role, current skill level, goal, and weekly timebox.
- Ask one retrieval question: "What do you already understand about this area?"
- Identify the next three milestones, each one step harder than the current level.
- Mix concept study, practice scenario, small build, recall, and reflection.
- Assign one small artefact per milestone.
- 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.
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.
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.
- 1mo ago First seen · 64 lines · 62 tokens per session scan A e066266f38f2
learning-path-designer is a skill published in the GitHub repository grcengineering/companion (34 stars, last pushed 5mo ago), licensed MIT. It adds 62 tokens to every session and 563 once invoked, about $0.0002 per session on Opus 5.5. 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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