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 agentmods add skills/grcengineering/companion/concept-tutornpx skills add grcengineering/companion --skill concept-tutorgit clone --depth 1 https://github.com/grcengineering/companionWhat 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 | $0.00065 | $0.00569 |
| Opus 5 | $0.00032 | $0.00284 |
| Sonnet 5 | $0.00013 | $0.00114 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade B, and why
concept-tutor scanned grade B with 1 finding 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- If the learner has not attempted a definition, do not lecture first; retrieval is the point. How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
concept-tutor
What
Teach one concept just deeply enough for the learner's next move. Keep the session retrieval-first, example-backed, and learning-safe.
When
- The learner asks "what is X?"
- The learner uses a GRC term imprecisely.
- Another skill needs a prerequisite concept before practice.
- The learner asks for a plain-language version of a framework, control, evidence, assurance, risk, or trust concept.
Not For
- Checking a learner-authored explanation. Use
feynman-explainer. - Designing a practice case. Use
practice-scenario. - Applying the concept to the learner's live compliance posture.
Inputs
- Learner's current wording.
- Optional learner profile context.
- Corpus files from
knowledge/when citation grounding is needed.
Steps
- Ask the learner to define the concept in their own words first.
- Give a plain-language explanation calibrated one step above their answer.
- Show one realistic but fictional or sanitized example.
- Contrast the useful version with the theatre version.
- Ask for a short explain-back.
- Correct one gap and name one next concept only if it is needed.
Validation
- The learner can explain the concept without repeating your wording.
- The learner can name one practical implication and one common trap.
- Any corpus-derived claim points back to the source file.
Gotchas
- If the learner asks for advice about their real programme, convert to a fictional example before teaching.
- If the learner has not attempted a definition, do not lecture first; retrieval is the point.
- If the concept has several meanings across frameworks, name the frame before explaining.
Failure Modes
- Too broad: split the topic and teach the smallest useful concept first.
- Too operational: restate the learning boundary and use toy details.
- No corpus support: label the point as a general teaching heuristic.
Examples
- User asks "What is evidence freshness?" -> Ask what they think it means, explain freshness vs existence, show a fictional audit evidence example, then ask for explain-back.
- User confuses controls and evidence -> Contrast the mechanism with the proof, give a toy access review example, then ask them to sort two statements.
- User asks "Explain SOC 2 like I'm new" -> Narrow to one concept such as trust services criteria, then route follow-up sequencing to
learning-path-designer.
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.
- 3d ago First seen · 63 lines · 65 tokens per session scan B 7d258a969756
concept-tutor is a skill published in the GitHub repository grcengineering/companion (32 stars, last pushed 3mo ago), licensed MIT. It adds 65 tokens to every session and 569 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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