{"data":{"post":{"fields":{"slug":"/weekly-ai-bites-last-manual-gap-qa-testing/","date":"2026-08-25"},"html":"

Weekly AI Bites is a series offering a direct window into our day-to-day AI work. Each post shares insights, experiments, and real experiences straight from our team’s meetings and Slack conversations — the models we’re testing, the challenges we’re working through, and what’s actually delivering results in live products. To keep up with what’s happening in AI, check Boldare’s channels every Monday for the newest bite.

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As a QA and test automation engineer, I build a fair number of Claude Code skills for commercial projects, and nearly all of them exist for a single purpose: testing. Here I want to walk through the set I rely on daily, along with a recent shift on one project where a long-standing manual bottleneck was finally automated away.

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Starting point

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I don’t have a long list of flashy, off-the-shelf skills to recommend — aside from Claude’s built-in skill-creator. What I do have are four skills, purpose-built for QA on one specific client project, that I use constantly:

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manual-tester carries the most weight. Before touching anything, it requests acceptance criteria, sharpens them, and comes up with its own edge cases. If a feature needs a local environment, it sets that up first. The real value isn’t the browser automation itself — it’s that the skill front-loads the thinking a human tester would normally have to do manually, before any test is even run.

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Two speeds of context, by design

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One detail I’m particularly happy with: these skills don’t rely solely on pasted-in acceptance criteria or on memory. They pull live ticket context and cross-reference it against a curated quirks store — and the two are deliberately engineered to run at different speeds.

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The live per-ticket fetch covers the current status, acceptance criteria, and comments for a single ticket. It’s pulled fresh every single time and never cached, living in a disposable, gitignored cache. A subagent handles the raw ticket call and returns only a condensed summary, since a full ticket can run 5–20KB of bilingual spec text.

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The known-quirks knowledge base, by contrast, holds cross-ticket QA quirks, won’t-fix decisions, and already-diagnosed root causes. It’s built once and updated only through an explicit verification run, stored in committed, curated files — one per quirk. Entries move through confidence tiers: unverified-seed, then verified-single-source, then confirmed.

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A separate, self-checking log tracks behavioral divergences that are anchored to a test deliberately built to fail: if the underlying bug ever gets fixed, that test flips to red on its own, so the log can never quietly become outdated. Quirks that can’t be anchored this way — tied to a live KYC-verified account, an admin session, or timing dependencies — go through the confidence-tier process instead, only reaching “confirmed” status after a human signs off.

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The bottleneck that finally gave way

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For months, one identity-verification step in the product under test could only be run manually, since the biometric check couldn’t be safely automated. That meant manual-tester could push a feature into a verified state, but never carry it fully through — genuine end-to-end coverage wasn’t possible.

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That changed once a colleague delivered automation for the biometric flow itself: real ID document and facial data fed into the browser’s spoofed camera, with a freshly generated identity stamped onto the document image so the verification service treats it as current. Every run spins up a brand-new disposable account and deletes it automatically after 2 days, so no shared data ever gets touched.

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With that piece solved, manual-tester can now carry a ticket through the entire flow on its own, not just up to the point where a human previously had to step in. That same week, three new automated checks were added on top of it: verification in an additional supported language, a check that denying camera access produces the correct, recoverable error message, and a check that a name pulled from a scanned document renders correctly throughout the interface.

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What’s working

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Every skill is put through evals, and token cost is tracked as a genuine metric rather than an afterthought — a skill that technically works but burns excess context isn’t worth keeping.

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Limitations

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What’s next

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Some of these skills generalize more cleanly than others — the plan is to strip out the client-specific pieces from manual-tester, playwright-test, and the underlying skill-building approach, so they can serve as a reusable starting point on other projects, and to publish anonymized versions to Boldare’s internal Claude Code skill marketplace.

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Technical note: a Claude Code skill is a folder containing a SKILL.md file with instructions, optionally packaged with scripts and reference material. Claude only loads a skill’s full contents once it determines a task actually needs it — until invoked, a skill costs next to nothing in context. That’s what makes it practical to keep dozens of narrow, project-specific skills installed simultaneously without paying a constant token cost for their mere presence.

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Takeaway

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The most useful Claude Code skills grow out of repetitive QA work, not a chase for novelty. A skill earns its keep when it removes a bottleneck a human previously had to sit through by hand — and proves, through evals and token cost, that it’s still worth keeping around.

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Curious how this kind of AI-powered QA setup could work for your team? Check out Boldare’s AI-Powered QA & Test Automation services — we build the same kind of tooling we use internally into production CI/CD pipelines, from an initial audit through full automation.

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