Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/LiXin97/agora-labnpx agentmods add skills/lixin97/agora-lab/student-meetingWrote 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/lixin97/agora-lab/student-meeting)<a href="https://agentmods.dev/skills/lixin97/agora-lab/student-meeting"><img src="https://agentmods.dev/badge/skills/lixin97/agora-lab/student-meeting/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/lixin97/agora-lab/student-meeting"><img src="https://agentmods.dev/badge/skills/lixin97/agora-lab/student-meeting.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 | $0.00017 | $0.00253 |
| Opus 5 | $0.00009 | $0.00127 |
| Sonnet 5 | $0.00003 | $0.00051 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
student-meeting 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 11d 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.
What it actually says
Student Meeting
Use this with core-meeting.
Responsibilities
- write a high-signal perspective in PREPARE
- read everyone before challenging
- critique peers on novelty, method, and feasibility
- respond directly to material criticisms
File Paths
- PREPARE ->
{meeting_dir}/{id}/perspectives/{your-name}.md, where{meeting_dir}is the canonical shared meeting directory from your AGENTS/CLAUDE instructions - CHALLENGE ->
{meeting_dir}/{id}/critiques/{your-name}_on_{other}.md - RESPOND ->
{meeting_dir}/{id}/responses/{your-name}_response.md - If you are the configured meeting decision maker for this lab, DECISION ->
{meeting_dir}/{id}/decision.md
Required Commands
bash ../../scripts/lab-meeting.sh -caller <your-name> -status
bash ../../scripts/lab-meeting.sh -caller <your-name> -ack-read
Prepare Checklist
- progress since last meeting
- strongest finding
- blocker or uncertainty
- proposed next step
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.
- 11d ago First seen · 37 lines · 17 tokens per session scan A ade9b812615d
student-meeting is a skill published in the GitHub repository LiXin97/agora-lab (49 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 253 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.
Other skills, from other repositories
code-tour
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codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.
gh-file-issue
Use when filing a new Codewhale GitHub issue: turn a bug or idea into a well-formed, actionable issue with repro, acceptance criteria, labels, and milestone.
gh-treasure-hunt
Hunt the issue/PR queue for highest value-over-risk wins: clean focused community PRs, already-implemented issues to close, safe quick-fixes.
get-rep-call-feedback
Use when the user wants evidence-backed coaching for one rep's calls, especially by comparing the rep with peer examples to identify repeatable best practices, specific upgrade moments, and practical next-call language.
recording
Capture screenshots on registered computers, record on macOS or HarmonyOS, and manage saved captures. Probe capabilities before recording.