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/hoangsonww/AI-News-Briefingnpx agentmods add skills/hoangsonww/ai-news-briefing/eval-dashboardWrote 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/hoangsonww/ai-news-briefing/eval-dashboard)<a href="https://agentmods.dev/skills/hoangsonww/ai-news-briefing/eval-dashboard"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-dashboard/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/hoangsonww/ai-news-briefing/eval-dashboard"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.00535 |
| Opus 5 | $0.00028 | $0.00267 |
| Sonnet 5 | $0.00011 | $0.00107 |
| Haiku 4.5 | $0.00006 | $0.00053 |
Grade A, and why
eval-dashboard 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 12d 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
Eval — Interactive Dashboard
Single-file offline UI over eval/store.sqlite + eval/golden/. No backend, no build step. Chart.js loads from a CDN, but everything else works over file://.
How to invoke
make eval-dashboard # regenerate dashboard/data.js
make eval-dashboard OPEN=1 # also open in default browser
make eval-dashboard DASHBOARD_JUDGE=claude-haiku-4-5-20251001 # filter rows
Direct invocation:
python3 eval/export_dashboard.py --judge claude-haiku-4-5-20251001 --open
Behavior
- Pull rows from
eval/store.sqlite(latest-per-date wins). - Join each row with its corresponding
eval/golden/<date>.jsonbaseline. - Compute summary stats: composite min/max/median/mean, axis medians, drift z-score, gate-fail count, regression count.
- Serialize to
eval/dashboard/data.jsaswindow.EVAL_DATA = {...}. - Optionally launch the default browser pointing at
eval/dashboard/index.htmlviafile://.
Panels rendered
| Panel | Visualization |
|---|---|
| Stat cards | Composite median + mean, drift status, gate fails, regressions |
| Composite trend | Line chart with baseline overlay + dashed 3.0 gate threshold |
| Axis radar | 5-axis median across all cards |
| Composite histogram | Buckets < 2.5 → ≥ 4.5 |
| Per-card stacked bars | Each card's weighted axis contributions |
| Per-card table | Sortable, filterable (All / Below gate / Regressed / Composite ≥ 4), live search |
What to tell the user
After running, tell them the dashboard path (eval/dashboard/index.html) and how many cards / goldens loaded. If OPEN=1 was not passed, give them the open command for their platform (open on macOS, xdg-open on Linux, start on Windows). Mention the dashboard is regenerated from the store — they should re-run this after any make eval-backfill to refresh visualizations.
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.
- 12d ago First seen · 45 lines · 57 tokens per session scan A 023a8ae5068d
eval-dashboard is a skill published in the GitHub repository hoangsonww/AI-News-Briefing (42 stars, last pushed 4d ago), licensed MIT. It adds 57 tokens to every session and 535 once invoked, about $0.0003 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.
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