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-backfillWrote 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-backfill)<a href="https://agentmods.dev/skills/hoangsonww/ai-news-briefing/eval-backfill"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-backfill/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-backfill"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-backfill.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 | $0.00046 | $0.00375 |
| Opus 5 | $0.00023 | $0.00187 |
| Sonnet 5 | $0.00009 | $0.00075 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
eval-backfill 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 — Backfill All Cards
Score every card in example-cards/*-card.json and write rows to eval/store.sqlite.
How to invoke
make eval-backfill # stub judge, offline
make eval-backfill JUDGE=claude # real Claude Haiku judge, ~$0.04 for 18 cards
Direct invocation supports --workers and --max-calls:
python3 eval/runner.py backfill --judge claude --workers 4 --max-calls 50
Behavior
- Stub runs serially (instant).
- Real backends parallelize via a
ThreadPoolExecutor(default 4 workers). 18 cards usually finish in ~3-5 minutes. - A pre-call status line prints per card (
[HH:MM:SS] YYYY-MM-DD: judging...). Full subprocess traces append tologs/eval-judge-YYYY-MM-DD.logso the user cantail -fwhile it runs. - Failures on individual cards are logged but do not abort the whole backfill.
--max-calls 50caps accidental sweeps; bump it deliberately if the cap is hit.
What to tell the user
Show the trailing summary: number of cards judged, total elapsed time, per-card amortized cost. If any card failed, surface the failing date and the error. Mention tail -f logs/eval-judge-$(date +%F).log so the user can monitor progress without re-running.
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 · 33 lines · 46 tokens per session scan A f05faf27c5dd
eval-backfill is a skill published in the GitHub repository hoangsonww/AI-News-Briefing (41 stars, last pushed 3d ago), licensed MIT. It adds 46 tokens to every session and 375 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.
Other skills, from other repositories
brainstorming
Interactive spec generation — turn ideas into concrete specs with R-numbered requirements and testable acceptance criteria.
backpropagation
Trace runtime bugs back to spec gaps — identify missing acceptance criteria, update specs, generate regression tests, and detect patterns.
certify
Full verification loop — tests, types, lint, build, and a Playwright browser check for UI projects; collects evidence before any success claim. Use to verify a change is actually green.
blueprint
Converts an approved spec into a TDD task plan with a parallelism map (PARALLEL vs SEQUENTIAL), saved to .workspace/shared/plans/. Use after a spec is approved, before implementation.
ward
TDD engine and enforcer — red/green/refactor, one behavior at a time. Use while implementing any feature or bugfix, or to execute a specific blueprint task with TDD.
g-review
Run the review gate on the current branch diff. Runs the test suite, captures the diff, and dispatches code-lead, which verifies done conditions and reviews the diff itself. Issues MERGE READY or HOLD.