aops-core/skills/verify/SKILL.md
Judgement-based QA pass. Does this artifact meet its goal and serve its user? Demands excellence, not compliance. Owned by marsha; reads the spec's Fitness Rubric (designed upstream via /design-rubric).
npx skillsauth add nicsuzor/academicops verifyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Conduct rigorous QA reviews of artifacts to ensure correctness, complete implementation, and fitness for purpose.
Before you read a single line of the diff, judge the premise from the task + diffstat alone and write the sharp principal's one-sentence snap reaction — "was this a good idea, in this shape?" — verbatim, as forcing-check item 0. You cannot emit a PASS verdict without it; a bad premise is a FAIL regardless of test coverage (green tests are the expected surface of a bad premise, not a mitigant). Diffstat-first ordering is mandatory — reading the code first is exactly what lets a clean, well-tested surface launder a bad premise.
Full definition, the verbatim prompt, the never-a-checklist hard rule, and the worked specimen live in the canonical reference: [[premise-test.md]]. (FAIL is the local rejection token here; the arch-fit lens emits 🔴 REJECT for the same call.)
Default posture: assume it's broken. The burden is on the artifact to prove it works — not on you to prove it doesn't.
PASS, FAIL, or REVISE.## Fitness Rubric. (If missing on a fitness task, return REVISE — fitness rubric missing).FAIL regardless of test coverage; you cannot reach PASS without writing it.DERIVER_MISSING, N/A, TODO). Fail if primary value-signals are missing./design-rubric self-instance requirement).For any artifact with computed, aggregated, or derived output (dashboards, reports, metrics), trace source → output: confirm the source is real, populated, and fresh; independently cross-verify the values against that source; disable any fallback to prove the primary path works alone (a fallback silently masks a broken primary); and check behaviour under load. The question is not "did output appear?" but "is this the RIGHT data?" — plausible-looking output is the most dangerous kind of incorrect output.
Stop evaluation immediately and write a FAIL verdict if any of the following occur:
FAIL regardless of green tests; test-passing is the expected surface of this failure, not a mitigant.{variable}, TODO, FIXME) in production.try/except without logging).Output reports exactly in this format:
## Verification Report
**Bar:** [mechanical / fitness / mixed]
**Verdict:** [PASS / FAIL / REVISE]
### Concrete observations
[Observed bugs/defects, file paths, line numbers, and log excerpts]
### Forcing checks
0. **Premise test (before reading the diff):** [verbatim sharp-principal reaction from task + diffstat alone — "was this a good idea, in this shape?" A bad premise -> FAIL regardless of tests; cannot reach PASS without this line]
1. **Sentinel/empty-state audit:** [count + list of sentinels/placeholders. If primary signals absent -> FAIL]
2. **Principal's-eye top-line read:** [headline element quoted, and whether correct]
3. **Floor vs ceiling:** [verbatim "exceptional, or merely working?"]
### Judgement
[Prose evaluation against AC, Red Flags, and/or Fitness Rubric dimensions]
### Recommendation
[If FAIL/REVISE: specific remediation steps and user impact]
For web applications:
$AOPS_SESSIONS/qa-screenshots/YYYY-MM-DD/.tools
Streamlit implementation of the analyst presentation layer. Use when building or updating a Streamlit dashboard that displays pre-computed research data. This is the Streamlit-specific HOW for the tech-agnostic principles in the aops-tools analyst skill — display only, never transform.
tools
Python plotting and statistical-modelling libraries (matplotlib, seaborn, statsmodels) for the analyst presentation and statistical-methodology layers. Use when producing publication-quality figures or fitting statistical models in Python. Library-specific HOW for the tech-agnostic principles in the aops-tools analyst skill.
tools
dbt (data build tool) implementation of the analyst transformation layer. Use when a project has a dbt/ directory or you need to build, test, or document SQL transformations as version-controlled, reproducible dbt models. This is the dbt-specific HOW for the tech-agnostic principles in the aops-tools analyst skill.
development
Core academicOps skill — institutional memory, strategic coordination, workflow routing, and framework governance. Merges butler (chief-of-staff) with framework development conventions.