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 tikalk/adlc-team-skills --skill evals-validategit clone --depth 1 https://github.com/tikalk/adlc-team-skillsWrote 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/tikalk/adlc-team-skills/evals-validate)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/evals-validate"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/evals-validate/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/tikalk/adlc-team-skills/evals-validate"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/evals-validate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 51 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00038 | $0.00779 |
| Opus 5.5 | $0.00015 | $0.00312 |
| Sonnet 5.5 | $0.00008 | $0.00156 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
evals-validate 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evals-validate
What this skill does
Conducts comprehensive validation of the implemented evaluation system following EDD principles to ensure production readiness through statistical analysis, performance verification, and quality assurance.
Output:
- Statistical Validation - TPR/TNR analysis, accuracy metrics, confidence intervals
- Performance Validation - SLA compliance verification for evaluation pyramid tiers
- Quality Assurance - Goldset integrity, example balance, coverage analysis
- Holdout Dataset Validation - Unbiased accuracy assessment on reserved test set
- Auto-handoff to
/evals-analyzefor closed loop trajectory analysis
Key EDD Principles Applied:
- Principle IV: Evaluation Pyramid - Tier performance SLA validation (Tier 1 <30s, Tier 2 <5min)
- Principle II: Binary Pass/Fail - Statistical compliance verification
- Principle IX: Test Data as Code - Holdout dataset validation integrity
- Principle III: Error Analysis - Pattern stability validation
When to use
- After
/evals-implement: Execute the evaluation suite and measure quality - CI/CD Pipeline gate: Run evaluations before release to ensure no regressions
- Periodic audit: Verify evaluator accuracy on holdout data to check for model drift
When NOT to use
- Evaluator not generated: Run
/evals-implementto build grader files first - Analysing failure traces: Use
/evals-analyzeto extract deep insights from run results
Process
User Input
$ARGUMENTS
--holdout-only— Validate only on holdout dataset (unbiased validation)--performance-only— Skip statistical analysis, focus on SLA compliance--metrics METRICS— Specific metrics to validate (tpr, tnr, accuracy, performance)
Execution Steps
Phase 1: Execute Evaluations
Runs the underlying framework CLI directly:
- PromptFoo:
npx promptfoo eval --config evals/promptfoo/config.js - DeepEval:
pytest evals/deepeval/ -vorpython evals/deepeval/config.py
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · +6 tokens per session 1792ef698b3e
- 1mo ago First seen · 78 lines · 32 tokens per session scan A fa0cc69e0e08
evals-validate is a skill published in the GitHub repository tikalk/adlc-team-skills (143 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 779 once invoked, about $0.0002 per session on Opus 5.5. 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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