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AC exploratory-testing

Design and execute structured exploratory testing sessions. Covers Session-Based Test Management (SBTM), charter writing, heuristic-based exploration (HICCUPS, FEW HICCUPS), bug discovery patterns, note-taking templates, and conversion of findings to automated tests. Use when: "exploratory testing," "SBTM," "manual testing," "bug hunting," "test charter," "heuristic testing." Not for: an AI browser agent autonomously exploring the app from a natural-language goal — use agentic-browser-testing. Not for: testing your product's own AI/LLM features — use ai-system-testing. Related: test-planning, ai-bug-triage, risk-based-testing, agentic-browser-testing.

petrkindlmann/qa-skills Agent Skills author: petrkindlmann MIT 3 files body ≈ 4 903 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Design and execute structured exploratory testing sessions.

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedurePlaywrightAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
98
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Result and completion w 14
40
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 70Execution cost. Instruction body is 4903 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 64 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 15 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 659: enough signal without eating the budget
    • +4Structure: 38 headings
    • +3Step-by-step instructions: 64 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 98.