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 alphabetc1/agent-skills --skill mentorgit clone --depth 1 https://github.com/alphabetc1/agent-skillsWrote 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/alphabetc1/agent-skills/mentor)<a href="https://agentmods.dev/skills/alphabetc1/agent-skills/mentor"><img src="https://agentmods.dev/badge/skills/alphabetc1/agent-skills/mentor/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/alphabetc1/agent-skills/mentor"><img src="https://agentmods.dev/badge/skills/alphabetc1/agent-skills/mentor.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 | $0.00033 | $0.00799 |
| Opus 5 | $0.00016 | $0.00400 |
| Sonnet 5 | $0.00007 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
mentor 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 10d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mentor
When To Use
Use this skill when the user wants systematic long-term learning of a repo, technical system, or knowledge domain, including interview preparation, that should continue across multiple conversations instead of resetting every turn.
Files To Read
- Always read
learning/<topic-slug>/learner-state.yaml. - Read the most recent relevant entries in
learning/<topic-slug>/session-log.md. - Read
references/state-schema.mdwhen creating or updating learner state. - Read
references/session-modes.mdwhen selecting or switching the session mode. - Read
references/curriculum-design.mdwhen defining or extending a curriculum graph. - Read
references/llm-inference-curriculum.mdwhen the topic is LLM inference or closely related interview prep.
Bootstrap
- Determine the study topic and its slug.
- If
learning/<topic-slug>/does not exist, initialize it with:
python scripts/init_learning_state.py --topic "<topic>"
Pass --slug <ascii-slug> if you want a custom folder name.
- Do not rely on chat memory when it conflicts with the learner state files. Prefer explicit file evidence or ask one focused clarification question.
Fixed Session Loop
Run this loop for every substantive session:
- Read learner state.
- Select exactly one primary mode.
- Select the current module from the curriculum graph.
- Run the teaching, diagnosis, drill, recall, or planning session.
- Update
learner-state.yamland append tosession-log.md. - End with one explicit next action.
Mode Selection
- Use
mapwhen the user needs a big-picture view, the curriculum graph is missing, or the learner needs orientation. - Use
teachwhen the learner wants an explanation and prerequisites are mostly in place. - Use
diagnosewhen ability is unclear, inconsistent, or likely overestimated. - Use
drillwhen the user wants interview-style practice or short-answer pressure. - Use
recallwhen thereview_queueis due or the user wants to revisit prior material. - Use
planwhen the user asks for a roadmap, sequencing, or schedule. - Explain any mode switch in one sentence and keep only one primary mode at a time.
What ships with it
16 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.
- .gitignore 49 B
- agents/openai.yaml 206 B
- assets/learner-state-template.yaml 970 B
- assets/session-log-template.md 337 B
- install.ps1 2.9 KB runs code
- install.sh 3.1 KB runs code
- plan.md 7.8 KB
- prompt.md 5.2 KB
- README.md 4.7 KB
- references/curriculum-design.md 2.5 KB
- references/llm-inference-curriculum.md 6.6 KB
- references/session-modes.md 3.3 KB
- references/state-schema.md 3.4 KB
- research.md 4.3 KB
- review.md 2.1 KB
- scripts/init_learning_state.py 4.3 KB runs code
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.
- 10d ago First seen · 85 lines · 33 tokens per session scan A 0cd54db82d71
mentor is a skill published in the GitHub repository alphabetc1/agent-skills (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 799 once invoked, about $0.0002 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 2026-08-31.
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