Agent skill

App User Story QA

by majiayu000 in majiayu000/spellbook

End-to-end app feature inventory and user-story testing workflow with a canonical tracker.

MITAuto-check passedProduct & Project Management

Install App User Story QA

skills CLI
$ npx skills add majiayu000/spellbook --skill app-user-story-qa -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install majiayu000/spellbook app-user-story-qa --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/majiayu000/spellbook.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/app-user-story-qa .claude/skills/app-user-story-qa && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
app-user-story-qa
GitHub stars
287
Token cost
~2.1k tokens
SKILL.md length
1,165 words
Files
2
Skills in repo
97
Repo updated
First seen
Licence
MIT

At a glance

End-to-end app feature inventory and user-story testing workflow with a canonical tracker.

  • Works in 4 steps: Inventory → Test → Fix → …
  • The user asks to audit every feature
  • SKILL.md covers Purpose, Route, Operating Contract and Gotchas, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

App User Story QA is an agent skill from majiayu000/spellbook. End-to-end app feature inventory and user-story testing workflow with a canonical tracker. Use when the user asks to audit every feature, derive expected behavior from code, test user journeys, or explicitly fix and retest documented UX or logistical defects.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Product & Project Management, covering User stories and Customer journey mapping. The repository describes itself as: Cross-runtime skills for Claude Code, Codex, and multi-agent workflows. The licence is MIT.

When your agent uses it

  • The user asks to audit every feature
  • Derive expected behavior from code
  • Test user journeys
  • Explicitly fix and retest documented UX

Example prompts

  • “/app-user-story-qa”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Inventory
  2. Test
  3. Fix
  4. Retest

What it can do on your machine

Read from SKILL.md and the folder at commit ed52af7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are csv).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

App User Story QA loads about 2.1k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 1,165 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/spellbook at commit ed52af7, republished under its MIT licence (© majiayu000). 1,165 words, ~2,097 tokens.

Download SKILL.mdSave it as .claude/skills/app-user-story-qa/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
app-user-story-qa
description
End-to-end app feature inventory and user-story testing workflow with a canonical tracker. Use when the user asks to audit every feature, derive expected behavior from code, test user journeys, or explicitly fix and retest documented UX or logistical defects.

App User Story QA

Purpose

Use this skill to turn a broad "test every feature in this app" request into a controlled loop with one source of truth. Anchor every feature to code, track expected behavior in a canonical spreadsheet, run tests against each user story, and classify failures. Apply fixes only when the current request explicitly authorizes them.

Route

Use plan_first for most runs. Switch to clarify_first only when the app boundary, writable checkout, production risk, or acceptance criteria are unclear enough that a wrong assumption would waste substantial work.

Before editing:

  1. Run a repo state snapshot: current path, branch, latest remote, dirty files, open PRs when relevant.
  2. If the checkout is dirty or user work is present, first decide which state the user asked to test. Preserve current user work in the isolated test worktree when the request targets the working tree; use a clean base only when the user asked for the base branch.
  3. Read applicable repo instructions such as AGENTS.md, README, architecture docs, and feature entrypoints.
  4. Search for existing QA trackers before creating a new one.
  5. Define the app boundary by user-facing surfaces, not internal helper functions.

Operating Contract

Select one mode before editing production code:

  • report_only is the default for audit, inventory, test, diagnose, or tracker requests. It may create or update the requested canonical tracker and run tests, but it must not change product behavior.
  • apply_fixes requires the current user request to explicitly ask for fixes. It covers only defects already reproduced and classified within the agreed app boundary. Earlier approval and a generic request to "test everything" do not authorize fixes.

If the mode is ambiguous, use report_only and record proposed fixes in the tracker.

Direct actions:

  • Inventory local code and docs, create or update the single canonical tracker, run local tests, and document failures.
  • In apply_fixes only, fix narrow in-scope logistical or UX defects and add focused coverage when user-observable behavior changes.

Escalate before:

  • Testing production systems, using paid external services, changing credentials or deployment state, deleting user data, or broadening the app boundary beyond the user's request.
  • Editing shared test infrastructure, weakening assertions, or changing product requirements instead of the implementation.

Evidence-backed pushback:

  • Challenge requests to skip the tracker, mark untested rows as passed, or fix symptoms without reproducing the failure. Cite the tracker row, command output, code anchor, or missing environment precondition.

Feedback loop:

  • Promote repeated setup failures, brittle manual paths, or recurring test gaps into tracker notes, scripts, docs, or follow-up issues instead of leaving them only in chat.

Gotchas

  • Do not create scattered notes or duplicate trackers. One canonical tracker is the audit log.
  • Do not invent expected behavior from product hopes. Expected behavior comes from current code, docs, and visible user surfaces.
  • Do not mark a feature passed from stale output. Fresh evidence from the current session is required.
  • Do not "fix" environment blockers unless the user asked for environment repair.
  • Do not infer fix authorization from words such as "audit", "test", "check", or "create a tracker". Keep those runs in report_only.

Canonical Tracker

Create or update exactly one tracker. Prefer a real spreadsheet when the runtime supports it; otherwise use a CSV and treat it as the canonical spreadsheet. Do not scatter status across side reports.

Use these columns:

csv
Feature ID,Surface,Feature / capability,User story,Expected behavior based on code,Code anchors,Initial test approach,Initial test command or route,Status,Test result,Errors,Fix status,Retest result,Notes

Rules:

  • Assign stable IDs such as F001, F002.
  • Require at least one code anchor per row. A row without an anchor is not complete.
  • Write expected behavior from current code and docs, not from aspirational specs.
  • Keep the tracker parseable: validate CSV/spreadsheet row widths after edits.
  • Update the tracker during the loop, not only at the end.

Status Values

Use a small, consistent status set:

  • Not Tested
  • Passed
  • Failed - Product
  • Failed - UX
  • Failed - Logistical
  • Failed - Test Infra
  • Blocked - Env
  • Fixed
  • Passed after fix

Use Failed - Logistical for repo-owned setup, script, port, packaging, or local workflow defects that block a valid user path. Use Blocked - Env for local machine issues such as missing credentials, occupied services outside the repo, stale PATH binaries, or unavailable optional runtimes. Do not "fix" the user's environment unless they explicitly ask.

Show full SKILL.md (482 more words)Show less

Defect Taxonomy

Classify every failure before fixing:

  • Product: implemented behavior violates the user story or loses data.
  • UX: behavior works but the user path is confusing, brittle, or poorly surfaced.
  • Logistical: scripts, ports, setup, packaging, or local workflow make valid behavior hard to exercise.
  • Test Infra: the test itself is flaky, racy, or asserts the wrong contract.
  • Env: external setup blocks execution and is not a repo defect.

Fix only defects that are in scope for the task. Record out-of-scope defects in the tracker with clear rationale.

Workflow

1. Inventory

Map all user-visible surfaces first:

  • CLI commands and flags
  • Web or desktop UI routes
  • API endpoints
  • background jobs or hooks that affect users
  • plugin, package, install, or release surfaces
  • import/export, backup, migration, and governance flows

For each feature, record the user story as:

text
As a <user>, I want <capability>, so <outcome>.

Keep stories practical. Do not create rows for private helpers unless the user can observe the behavior through a surface.

2. Test

For every tracker row, choose the strongest feasible evidence:

  • direct smoke test for the user path
  • focused unit or integration test for the exact contract
  • broad regression suite when a feature is already covered there
  • manual or browser test when automation is missing

Record the command, route, or manual steps in the tracker. Fresh output from the current session is required before marking a row passed.

3. Fix

In report_only, record the reproduced defect, evidence, and proposed fix, then continue testing without editing production code.

In apply_fixes, state the exact defect and files being changed before editing. Keep fixes narrow:

  • For UX defects, fix production code and add or update focused coverage.
  • For product defects, record the evidence and escalate unless the user's request explicitly authorizes product behavior fixes.
  • For logistical defects, improve scripts, defaults, setup checks, or error messages.
  • For test-infra defects, preserve the behavior contract and fix the fixture or race.
  • Do not weaken assertions to make the suite pass.

Stop and re-evaluate after three failed attempts on the same defect.

4. Retest

After each fix:

  1. Rerun the focused failing test or smoke.
  2. Rerun the relevant broader gate for the touched surface.
  3. Update Fix status and Retest result.
  4. Keep the original error text or summary in Errors so the tracker remains an audit log.

Before completion, run the repo's required formatting, build, typecheck, lint, and test commands when practical.

Completion Gate

Finish only when:

  • every feature row has a user story, expected behavior, code anchors, and a status;
  • every Failed row is fixed and retested in apply_fixes, or is explicitly recorded as proposed, out of scope, or blocked with evidence in report_only;
  • every applied fix has a retest result;
  • the tracker validates structurally;
  • the final answer names the tracker path, changed files, defects found, fixes made, and verification commands.

If the repo has existing dirty work that is not yours, mention the isolated worktree or scope boundary in the final answer.

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/app-user-story-qa of majiayu000/spellbook.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit ed52af7

Compare with similar skills

App User Story QA next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

App User Story QA compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
App User Story QA this skillmajiayu000/spellbook287—~2.1kAutomated safety check: PassMIT
User Story Mappingdeanpeters/Product-Manager-Skills7.2k2 repos~2.8kAutomated safety check: PassCustom licence
Super Product Ownersyahiidkamil/Software-Engineer-AI-Agent-Atlas401—~7.9kAutomated safety check: PassNone
09 Customer Insight Globalminhnv0807/ai-business-skills609—~3.6kAutomated safety check: PassMIT
Aidd Product Managerparalleldrive/aidd384—~845Automated safety check: PassMIT
User Researchyonatangross/orchestkit292—~1.6kAutomated safety check: PassMIT

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Questions about App User Story QA

What does App User Story QA do?

End-to-end app feature inventory and user-story testing workflow with a canonical tracker. App User Story QA is an agent skill from majiayu000/spellbook. End-to-end app feature inventory and user-story testing workflow with a canonical tracker.

When should I use App User Story QA?

App User Story QA fits situations like: the user asks to audit every feature; derive expected behavior from code; test user journeys; explicitly fix and retest documented UX.

How do I install App User Story QA in Claude Code?

Run `npx skills add majiayu000/spellbook --skill app-user-story-qa -a claude-code`. Or copy the skill folder (skills/app-user-story-qa in majiayu000/spellbook) into .claude/skills/app-user-story-qa in your project. Claude Code loads it when a task matches its description.

How do I install App User Story QA in Codex?

Run `npx skills add majiayu000/spellbook --skill app-user-story-qa -a codex`. Or copy the skill folder (skills/app-user-story-qa in majiayu000/spellbook) into .agents/skills/app-user-story-qa in your project. Codex loads it when a task matches its description.

Can I use App User Story QA in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/spellbook --skill app-user-story-qa -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/app-user-story-qa, .gemini/skills/app-user-story-qa, .github/skills/app-user-story-qa and .opencode/skills/app-user-story-qa in your project.

What does App User Story QA need to run?

SKILL.md names no scripts, command-line tools or credentials: App User Story QA is instructions for the agent only.

Does App User Story QA access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is App User Story QA safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does App User Story QA use?

App User Story QA is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does App User Story QA use?

About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to App User Story QA?

Skills that share tags, products or a category with App User Story QA: User Story Mapping (deanpeters/Product-Manager-Skills, 7.2k stars), Super Product Owner (syahiidkamil/Software-Engineer-AI-Agent-Atlas, 401 stars), 09 Customer Insight Global (minhnv0807/ai-business-skills, 609 stars) and Aidd Product Manager (paralleldrive/aidd, 384 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains App User Story QA?

majiayu000 (a GitHub user) maintains it in majiayu000/spellbook, which has 287 GitHub stars. The repository holds 97 skills in this directory. The repository was last updated on October 8, 2026.

Source: majiayu000/spellbook on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.