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/arozumenko/sdlc-skillsnpx agentmods add skills/arozumenko/sdlc-skills/test-case-analysisWrote 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/arozumenko/sdlc-skills/test-case-analysis)<a href="https://agentmods.dev/skills/arozumenko/sdlc-skills/test-case-analysis"><img src="https://agentmods.dev/badge/skills/arozumenko/sdlc-skills/test-case-analysis/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/arozumenko/sdlc-skills/test-case-analysis"><img src="https://agentmods.dev/badge/skills/arozumenko/sdlc-skills/test-case-analysis.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 | $0.00064 | $0.04986 |
| Opus 5.5 | $0.00026 | $0.01994 |
| Sonnet 5.5 | $0.00013 | $0.00997 |
| Haiku 4.5 | $0.00006 | $0.00499 |
Grade A, and why
test-case-analysis 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 — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Case Analysis
Execute a TMS test case against the live app, observe what actually happens, and emit an Automation-Friendly Spec (AFS) a downstream engineer can implement without re-exploring.
Core philosophy: a written test case is a hypothesis. The app is the only source of truth. This skill never trusts the case as authored — it runs it step by step, captures stable selectors, flags defects, and only then produces a spec.
Absolute boundaries
- No automation code. No
.spec.ts, notest_*.py, no step definitions. The output is a markdown AFS file. Automation is implemented downstream — your agent knows which role / workflow picks the AFS up. - No automating un-automatable cases. Physical device, visual
judgment that can't be asserted, flows that genuinely can't be
scripted — mark the AFS
un-automatableand stop. - No skipping exploration. Even if the TMS case looks complete, execute it. The case describes intent; only execution reveals truth.
Analyst slot contract
This skill IS the analyst slot in the test-automation pipeline. When
dispatched — by an orchestrator like test-automation-lead, or
standalone for "analyse SCRUM-T101" — role, context, parameters, and
return shape are fixed here so dispatch prompts don't have to inline
them.
Role. Execute one TMS test case end-to-end against the live app, capture stable selectors, classify the finding, emit an AFS. No automation code (see § Absolute boundaries).
Session context — read once at session start. Typically
auto-imported via @-blocks in your agent's AGENT.md; if your
agent doesn't auto-import, read them now:
.agents/profile.md— project systems, base URL, credentials matrix, sample users, bug filing target.agents/workflow.md— branch/PR rules, EPIC pattern.agents/testing.md— framework, locator strategy, TMS case-gate exclusion list.agents/memory/<your-agent>/project_briefing.md— accumulated project gotchas from prior sessions.agents/architecture.md— the surfaces you'll touch (also referenced in Phase 2)
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 First seen · 405 lines · 64 tokens per session scan A 6fcc4bd8c094
test-case-analysis is a skill published in the GitHub repository arozumenko/sdlc-skills (25 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 4,986 once invoked, about $0.0003 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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