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-reportWrote 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-report)<a href="https://agentmods.dev/skills/hoangsonww/ai-news-briefing/eval-report"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-report/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-report"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-report.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 | $0.00049 | $0.00444 |
| Opus 5 | $0.00024 | $0.00222 |
| Sonnet 5 | $0.00010 | $0.00089 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
eval-report 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
Eval — Weekly Markdown Report
Pull the trailing N-day window from eval/store.sqlite and emit a Markdown digest suitable for publishing to Notion / Teams / Slack, or for committing under logs/eval-reports/.
How to invoke
make eval-report D=YYYY-MM-DD W=7 # 7-day window to stdout
make eval-report D=YYYY-MM-DD W=14 OUT=logs/eval-week.md # write to file
Direct invocation:
python3 eval/report.py --as-of YYYY-MM-DD --window 7 --out logs/eval-week.md
Behavior
- Fetch all
eval_runsrows in[as_of - window + 1, as_of]. - Pick the latest run per
card_date(so re-runs don't double-count). - Compute coverage (
N / windowdays judged), composite min/max/median, and per-axis medians. - Emit:
- H1 header with the date range
- Coverage + composite stats
- Axis medians table
- Per-day detail table (date, composite, F/N/D/S/C, judge model, truncated notes)
When to use
- Monday morning summary across the previous 7 days.
- Investigating a drift alert (
make eval-drift) — the per-day table shows which axes are dragging. - After a re-baselining workflow, to confirm the new judge/prompt produces consistent quality.
What to tell the user
If OUT is set, mention the file path and a quick cat or preview command. If piping to stdout, show the digest verbatim (the user can scroll the terminal). For Notion/Teams publishing, point at scripts/notify-teams.sh or the Notion MCP — the harness intentionally does not publish on its own.
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 · 42 lines · 49 tokens per session scan A 535cfc9aef6a
eval-report is a skill published in the GitHub repository hoangsonww/AI-News-Briefing (41 stars, last pushed 3d ago), licensed MIT. It adds 49 tokens to every session and 444 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 2026-08-30.
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reviewing
Claude-on-Claude code review protocol — reviews implementation against spec requirements and code quality standards.