A command that sequences the next few learning exercises. It starts from what the learner already knows and creates milestones with small outputs and a review prompt.

Use it to identify the learner's current edge, plan three milestones, assign an artefact to each, and schedule a review.

It turns a broad learning goal into a short, ordered set of practical steps.

path — Command by grcengineering

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 commands/grcengineering/companion/path

Clone the repo

git clone --depth 1 https://github.com/grcengineering/companion

Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 128 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.

Model	Per session	Once invoked
Fable 5	$0.00011	$0.00128
Opus 5	$0.00005	$0.00064
Sonnet 5	$0.00002	$0.00026
Haiku 4.5	$0.00001	$0.00013

Measured 2d ago against content hash 663099d54a37, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

path 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.

commands/path.md · 27 lines

What it actually says

Learning Path Command

Use when the learner has a goal but needs sequencing.

Flow

Ask what they already know.
Identify their current edge.
Create three milestones.
Assign one small artefact per milestone.
Schedule one review prompt.

Boundary

Do not prescribe programme changes. Sequence learning only.

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.

2d ago First seen · 27 lines · 11 tokens per session scan A 663099d54a37

Subscribe to this mod's changes

path is a command published in the GitHub repository grcengineering/companion (32 stars, last pushed 3mo ago), licensed MIT. It adds 11 tokens to every session and 128 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.

Code review Data and AI Planning compliance grc grc-engineering

path

Getting it into your agent

What it costs to keep this loaded

Grade A, and why

Nothing flagged

What it actually says

Learning Path Command

Flow

Boundary

What this file has done since we first saw it

Other commands, from other repositories

health

evidence-examples

control-requirements

list-controls

compare-versions

control-coverage