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-analyzegit 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-analyze)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/evals-analyze"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/evals-analyze/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-analyze"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/evals-analyze.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 | $0.00033 | $0.00874 |
| Opus 5 | $0.00016 | $0.00437 |
| Sonnet 5 | $0.00007 | $0.00175 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
evals-analyze 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evals-analyze
What this skill does
Provides cross-functional team elevation and closed-loop feedback following EDD Principle VIII (Close the Production Loop) by deep-analyzing trajectory failure traces and routing them to correct resolution pathways.
Output:
- Trajectory Analysis - Full multi-turn trace analysis with tool calls and context preservation (EDD Principle V)
- Failure Routing:
- Specification Failures (agent logic missing/ambiguous) → Automatically triggers a local call to
levelup-specifyto propose new context rules in.adlc/drafts/cdr/to fix agent behavior. - Generalization Failures (grader flawed or lacks edge-case coverage) → Appends evaluator backlog items to the project backlog for ongoing monitoring.
- Specification Failures (agent logic missing/ambiguous) → Automatically triggers a local call to
- Cross-Functional PR - Creates a team-ai-directives PR with insights and rule updates (EDD Principle X)
Key EDD Principles Applied:
- Principle VIII: Close Production Loop - Spec failures → fix directives; Gen failures → evaluator backlog
- Principle V: Trajectory Observability - Full multi-turn traces, not just outputs
- Principle X: Cross-Functional Observability - PMs, domain experts, and AI engineers collaborate
When to use
- After
/evals-validate: Analyze failures and resolve them - Closing a development loop: Translate evaluation failure insights into rule or evaluator fixes
- Reporting to stakeholders: Generate readable summaries for PMs and domain experts
When NOT to use
- Evals not yet executed: Run
/evals-validatefirst to generate results inevals/results/ - Trivial tasks: Closed-loop analysis is overhead for simple features
Process
User Input
$ARGUMENTS
--focus AREA— Focus analysis on specific areas (e.g., security, quality, performance)--dry-run— Analyze results and print report, but skip PR creation and local skill triggers
Execution Steps
Phase 1: Load Evaluation Results
- Reads results JSON from
evals/results/. - Extracts failure cases and full multi-turn conversation traces (including tool calls).
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.
- 12d ago First seen · 70 lines · 33 tokens per session scan A 70363dadf636
evals-analyze is a skill published in the GitHub repository tikalk/adlc-team-skills (133 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 874 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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