Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Flagrare/llm-tutor/plugin install llm-tutorWrote 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/flagrare/llm-tutor/tutor-done)<a href="https://agentmods.dev/skills/flagrare/llm-tutor/tutor-done"><img src="https://agentmods.dev/badge/skills/flagrare/llm-tutor/tutor-done.svg" alt="Measured on agentmods" 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 | $0.00120 | $0.03128 |
| Opus 5 | $0.00060 | $0.01564 |
| Sonnet 5 | $0.00024 | $0.00626 |
| Haiku 4.5 | $0.00012 | $0.00313 |
Grade B, and why
tutor-done 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 7d 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 not substantive (skipped/short): no extra reward. Don't punish, don't lecture. How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tutor Done
This skill closes an active tutoring topic — the pair to /tutor-start. It calculates the XP earned, marks the topic complete in state.json, runs the two-step feedback flow (thumbs + targeted rotating question), and credits the user with cycles rewards for engaging.
The feedback flow is the load-bearing piece. Per the design decisions doc (D4), feedback is rewarded to fix the "users skip it" problem, with anti-gaming guards (symmetric thumbs reward, substantive-answer guard for the targeted question, daily cycles cap).
XP rubric (per-concept)
XP is awarded per concept based on the first_attempt field — the user's first interaction with that concept during the session.
first_attempt |
XP awarded | Reasoning |
|---|---|---|
"success" |
30 | Solved it on the first try. Earned mastery signal. |
"needed_hint" |
15 | Used the hint ladder; learned something but with scaffolding. |
"needed_reveal" |
5 | Needed the canonical answer revealed. Still engaged, still some learning. |
null |
0 | Concept was never attempted (probably skipped during calibration). No XP. |
A typical 6-concept topic where the user gets most concepts first-try and needed one hint earns ~150 XP. The numbers are tunable in this rubric — adjust here if the felt-pacing turns out wrong.
Step 0 — Resolve the target topic
STATE="$CLAUDE_PLUGIN_ROOT/scripts/state.sh"
0a. Subject resolution
If the user provided an argument (e.g., /tutor-done python-decorators), use that as the slug.
If no argument, find the active topic:
ACTIVE=$(bash "$STATE" get '[.topics | to_entries[] | select(.value.status == "in_progress") | .key]')
If the active list is empty:
"No topic in progress. Run
/tutor-statusto see what you've started, or/tutor-start <subject>to begin one."
Exit cleanly.
If exactly one in-progress topic, use that slug.
If multiple in-progress topics, ask:
"Multiple topics in progress: [list]. Which one are you closing? (Type the slug, e.g.
python-decorators.)"
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.
- 7d ago First seen · 316 lines · 120 tokens per session scan B 96f2a12ab856
tutor-done is a skill published in the GitHub repository Flagrare/llm-tutor (5 stars, last pushed 3mo ago), licensed MIT. It adds 120 tokens to every session and 3,128 once invoked, about $0.0006 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 2026-08-31.
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