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-regressionWrote 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-regression)<a href="https://agentmods.dev/skills/hoangsonww/ai-news-briefing/eval-regression"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-regression/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-regression"><img src="https://agentmods.dev/badge/skills/hoangsonww/ai-news-briefing/eval-regression.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 | $0.00058 | $0.00403 |
| Opus 5 | $0.00029 | $0.00201 |
| Sonnet 5 | $0.00012 | $0.00081 |
| Haiku 4.5 | $0.00006 | $0.00040 |
Grade A, and why
eval-regression 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 — Regression Gate
Guard against silent quality regressions when the daily prompt, judge model, or judge prompt changes.
How to invoke
make eval-regression # stub judge, fast smoke test
make eval-regression JUDGE=claude # real Claude Haiku judge — production gate
Direct invocation supports a tighter (or looser) drop tolerance:
python3 eval/runner.py regression --judge claude --regression-drop 0.5
Behavior
- Read every
eval/golden/*.jsonbaseline (currently 18 cards, real-judge composites 2.9–4.2). - Re-judge each card against the configured backend.
- Compute
delta = new_composite - baseline_composite. - Fail (exit 2) if any card's delta is below
-regression-drop(default-0.5). - Print per-card OK / REGRESSED with explicit deltas.
When to run
- After bumping
PROMPT_VERSIONineval/judge.pyor editingeval/judge_prompt.md. - After switching judge models (
EVAL_JUDGE_MODEL). - In CI before merging changes to the daily
prompt.md. - After a re-baselining workflow to confirm the new goldens hold.
What to tell the user
If regressions are flagged, list each offending card with its delta and the judge's notes — they explain why the score dropped. If no regressions, report total cards passed and the worst-case delta so the user knows how much headroom remains.
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 · 40 lines · 58 tokens per session scan A c8a629a73eb8
eval-regression is a skill published in the GitHub repository hoangsonww/AI-News-Briefing (42 stars, last pushed 4d ago), licensed MIT. It adds 58 tokens to every session and 403 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.
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.