Ops Research Notebook

E-commerce Automation · Media QA · Static Lab Page
LAB STATUS · active · offline-ready · no tracking

Applied Ops Research: Browser Automation + Media Quality Engineering

This static page documents a practical research track: transforming repetitive listing operations (editing, image normalization, copy validation, CSV hygiene) into a measurable, reproducible, and cost-aware pipeline. The structure mirrors an internal research memo: goals, methods, metrics, milestones, and operational risks.

Open Workstreams Read Methods View Metrics
scope: browser ops / batch media focus: speed + consistency risk: policy / quality drift privacy: no analytics

Workstreams

Three tracks to keep the work “research-like”: reproducible experiments, engineering delivery, and controlled validation.

WS-01 · Browser Automation

Read product codes from Excel/CSV → search in admin → open edit view → patch HTML blocks → select local images by deterministic rules → upload → save → write structured logs + retry strategy.

WS-02 · Media QA Pipeline

Batch image processing: normalize size (1200×1200), safe margins, sharpening and text legibility, compression control; export diffs and traceable parameters for audits.

WS-03 · Content & Data Ops

Title/copy/meta generation and validation; SKU attribute extraction; CSV encoding & schema hygiene; failure capture and human-in-the-loop review gates.

Methods

Write this section like an internal experiment memo: design, constraints, variants, and reproducibility hooks.

01Objective: reduce manual ops time, stabilize listing quality, minimize rework.
02Input: (a) product codes (CSV) (b) local image folders (c) HTML templates.
03Pipeline A (manual): open admin → search → edit → upload → save.
04Pipeline B (automated): script-driven UI steps + deterministic rules + logs.
05Variants:
06 B1) Playwright (headless=false), strict selectors + retry/backoff.
07 B2) Selenium (legacy), fallback selectors + manual checkpoint.
08Media QA:
09 resize → safe-area crop → sharpen → text-enhance → compress → diff.
10Metrics: time/item, fail rate, human review rate, image readability proxy.
11Threats: UI changes, rate limits, selector drift, non-deterministic upload order.
12Repro: fixed sample set + pinned versions + raw logs + before/after hashes.
NOTEHow to make this defensible (and actually useful):
•Define scope: is p95 measured for “save-success” or “end-to-end”?
•Define samples: stratify by watermark presence and text density.
•Define acceptance: readable text, no stray glyphs, compliant formatting.
—Optional: add a Results Table (A/B/C) + static failure gallery placeholders.
—Still static: drop images into the same folder and reference them here.
This structure is intentionally “research-like”: you can hand it to a teammate (or your future self) and reproduce the run.

Metrics

Numbers below are example placeholders. Replace data-target values with your measured aggregates.

time / item
0s
p95 save latency
0ms
fail rate
0‰
human review
0%
ScoreImage Readability (proxy)
—edge-contrast ↑
—text stroke clarity ↑
—jpeg artifacts ↓
—safe margin compliance ↑
NoteCalibrate with periodic human scoring on a fixed sample set.
LogsRun artifacts
—run_id, timestamp, code, step, status
—screenshots for failures
—before/after image hash
—retry_count, final_error
NoteThese fields make audits and debugging dramatically faster.

Timeline

Milestones as an engineering research narrative: tooling, validation, deployment, and regression testing.

Baseline sampling + metric definitions

Sample 100–300 listings; record manual end-to-end time, failure modes, rework rate, and image issue taxonomy.

Closed-loop automation (MVP)

Implement minimal automation: code→admin→edit→HTML patch→image upload→save→structured logging.

Reproducible Media QA module

Parameter traceability for resize/sharpen/legibility/compression; export before/after diffs and run configs.

Robustness + regression

Handle UI drift, upload flakiness, selector fallbacks; add human takeover checkpoints and a failure recovery queue.

FAQ

Common questions for an internal-facing research page—especially reproducibility and compliance.

Q: Why “local-first”?

A: It reduces external dependency, keeps sensitive artifacts on-device, and improves reproducibility in weak/offline environments.

Q: How do you prevent UI updates from breaking automation?

A: Layer selectors (primary + fallback), capture screenshots at critical steps, provide manual takeover points, and run periodic regressions on a fixed sample set.

Q: Will image enhancement over-sharpen or damage readability?

A: Define acceptance criteria and bound processing strength; keep originals for rollback; validate with side-by-side crops on text-dense samples.

Q: What does “compliance” mean here?

A: No tracking, no deceptive flows, no auto-downloads, and clear rights/permissions for any processed assets; the tool is for optimizing your own operations.

Contact

Use this as a collaboration / access request section. Replace with your domain email and documentation links.

email[email protected]
docshttps://example.com/lab-notes
repohttps://example.com/private-repo
policycompliance + reproducibility statement
DISCLAIMERThis page is for technical research / engineering notes.
—No tracking scripts, no auto-downloads, no sensitive input collection.
—Ensure rights/permissions and platform compliance for processed assets.
—All results should be presented with explicit metric definitions for auditability.