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-clarifygit 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-clarify)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/evals-clarify"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/evals-clarify/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-clarify"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/evals-clarify.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 | $0.00038 | $0.01123 |
| Opus 5.5 | $0.00015 | $0.00449 |
| Sonnet 5.5 | $0.00008 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00112 |
Grade A, and why
evals-clarify 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 5d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evals-clarify
What this skill does
Conducts axial coding following EDD Principles III & IX to cluster related failure patterns, refine evaluation criteria, generate adversarial examples, and accept validated drafts into the published goldset.
Output:
- Clustered Criteria - Related patterns grouped into coherent evaluation themes
- Adversarial Examples - Generated attack scenarios and edge cases for robustness
- Published Goldset - Accepted criteria in
evals/{system}/goldset.mdwith full documentation - Holdout Dataset - Reserved test set (20%) for unbiased evaluation validation
- JSON Configuration - Auto-generated
goldset.jsonfor system consumption - Auto-handoff to
/evals-implementfor grader generation
Key EDD Principles Applied:
- Principle III: Error Analysis & Pattern Discovery - Axial coding → theoretical relationships
- Principle IX: Test Data as Code - Adversarial generation, holdout splits, version control
- Principle II: Binary Pass/Fail - Maintain strict binary evaluation throughout
- Principle I: Spec-Driven Contracts - Criteria validate spec compliance
When to use
- After
/evals-specify: Refine and accept draft criteria into goldset - Dataset maintenance: Balance pass/fail examples or add adversarial cases
- Adding holdout split: Isolate validation data from training data
When NOT to use
- No draft criteria exist: Run
/evals-specifyto discover patterns first - Grader generation: Use
/evals-implementto convert accepted goldset into code
Process
User Input
$ARGUMENTS
--accept IDS— Accept specific draft IDs (e.g., "EVAL-001,EVAL-003")--merge IDS— Merge related criteria (e.g., "EVAL-001+EVAL-002")--split ID— Split complex criterion into multiple focused criteria--holdout-ratio RATIO— Holdout percentage (default: 0.2, range: 0.1-0.3)
Execution Steps
Step 0: Detect Lightweight Draft Format
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.
- 5d ago Changed ca3c9a5f50f0
- 25d ago Changed 261f6f79aa11
- 1mo ago Changed · +20 lines ec11df6bdda7
- 1mo ago Changed · -2 tokens per session 8b93c8ba20e1
- 1mo ago First seen · 75 lines · 40 tokens per session scan A ffa97b1701ca
evals-clarify 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 1,123 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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