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 LiXin97/agora-lab --skill student-analyze-resultsgit clone --depth 1 https://github.com/LiXin97/agora-labWrote 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-analyze-results)<a href="https://agentmods.dev/skills/lixin97/agora-lab/student-analyze-results"><img src="https://agentmods.dev/badge/skills/lixin97/agora-lab/student-analyze-results/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-analyze-results"><img src="https://agentmods.dev/badge/skills/lixin97/agora-lab/student-analyze-results.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 | $0.00018 | $0.00257 |
| Opus 5 | $0.00009 | $0.00129 |
| Sonnet 5 | $0.00004 | $0.00051 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
student-analyze-results 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 Analyze Results
Purpose
Turn raw metrics into honest claims and a credible next experiment.
Workflow
- Read the experiment plan and the result artifact together.
- Compare against the intended baselines and ablations.
- Separate fair comparisons from noisy or compromised ones.
- Identify stable gains, fragile gains, and outright failures.
- Publish to your canonical shared artifact directory:
{artifact_dir}/{your-name}/analysis_{experiment_id}.md
Output Format
# Result Analysis: {title}
## Inputs
- **Plan**: ...
- **Results**: ...
## Headline Findings
1. ...
## Comparison Table
| Comparison | Fair? | Outcome | Notes |
|---|---|---|---|
## Supported Claims
1. ...
## Unsupported but tempting claims
1. ...
## Anomalies and Failure Modes
1. ...
## Recommended Next Experiment
1. ...
Rules
- Every claim must point back to a concrete result artifact.
- If the baseline comparison is unfair or noisy, say so explicitly.
- Always include one section called
Unsupported but tempting claims.
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 · 54 lines · 18 tokens per session scan A eb4a9b1154a7
student-analyze-results is a skill published in the GitHub repository LiXin97/agora-lab (49 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 257 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
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
contributor-onboarding
Help a new contributor get productive on this checkout - inspect sync state against main, build, run the repository's exact verification gate, and produce a local what's-new digest. Never fetches, pulls, or modifies a dirty tree on its own. Explicit-only.
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.