Ln 32 Test Strategy Planner logo

Ln 32 Test Strategy Planner

Community
levnikolaevich
ln-32-test-strategy-planner

Plans risk-based test portfolios and acceptance evidence; does not write or execute tests.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-32-test-strategy-planner
Stars
565
Forks
84
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by levnikolaevich on GitHub. Read the source before you install it.

Installation

Install the Ln 32 Test Strategy Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/levnikolaevich/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/plugins/delivery-planning-suite/skills/ln-32-test-strategy-planner .claude/skills/ln-32-test-strategy-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 32 Test Strategy Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ln 32 Test Strategy Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ln 32 Test Strategy Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Test Strategy Planner

Goal: Design a read-only, risk-based test portfolio decision for the requested scope. Maximize confidence in important local behavior while preventing test growth that lacks a unique defect signal, and define how affected evidence is retained, changed, consolidated, retired, or deliberately omitted.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred toolUse it whenFallback
Requirements and repository rulesNative file reads plus GitEstablishing scope, current work, acceptance criteria, and supported commandsUser-provided requirements with explicit limitations
Existing test surfaceFile listing, search, manifests, runner configuration, and CIMapping test levels, fixtures, environments, and conventionsRepository tree and known test entrypoints
Behavior and boundariesLanguage server or host-native code intelligenceTracing entrypoints, consumers, trust boundaries, persistence, queues, and external contractsNarrow search followed by direct inspection
Existing evidenceExisting test/CI reports and test/configuration readsCurrent proof can change the strategyUse static evidence with execution limits; do not execute tests during planning
Current external failure modesOfficial documentation, specifications, advisories, and primary field evidenceAn external contract or real user failure can change scenarios or priorityMark the claim UNVERIFIED; do not invent risk

Keep the run read-only. Do not create tests, fixtures, snapshots, tasks, or documentation, and do not update the reviewed implementation.

Evidence Rules

  • Coverage is discovery evidence, not proof. Require an oracle that would fail for the named defect.

  • Prioritize by impact, plausible failure, uniqueness, detectability, and recovery cost; do not convert those judgments into universal numeric thresholds.

  • Existing tests reduce a gap only when their setup and assertions prove the same behavior and failure mode.

  • Keep portfolio action separate from execution status. Use KEEP, ADD, UPDATE, MERGE, DELETE, or NO_TEST for the decision and PASS, FAIL, BLOCKED, or UNPROVEN only for evidence state.

  • NO_TEST is an explicit risk decision, not missing work. Name the existing proof, alternative control, or accepted residual risk.

  • A persistent test register is optional. Prefer repository-native test names, paths, tags, CI configuration, and task output unless scale or governance requires another maintained artifact.

  • External research is actionable only when it adds a concrete failure mode, boundary, or oracle to this plan.

Checklist

1. Establish Scope and Evidence

  • Resolve the feature, requirements, acceptance criteria, actors, explicit non-goals, and protected human or system outcomes; separate the requested mechanism from the result it must enable and return BLOCKED if there is no concrete behavior to plan for.
  • Read applicable repository instructions and inspect Git state so current work and unrelated changes are not mistaken for established behavior.
  • Detect languages, frameworks, runners, test directories, fixtures, factories, environments, CI gates, coverage, contract tests, and manual test surfaces.
  • Map existing evidence and every test affected by the requested behavior to each requirement; record coverage as complete, partial, missing, or unavailable based on the actual oracle, alongside the execution state defined in Evidence Rules; names and proximity are not proof.
  • Inspect manual, exploratory, incident, and production evidence when it reveals behavior that automated suites do not cover.
  • Identify environment, data, credentials, services, devices, browsers, and destructive-state constraints before proposing scenarios.
  • Record assumptions and unknowns that can change test level, priority, or feasibility, and ask one concise question only when different interpretations materially change the strategy.

2. Build the Risk Map

  • Trace critical flows from actor trigger through entrypoint, runtime wiring, state change, and durable or user-visible outcome.
  • Identify uniquely important local behavior involving money, authentication, authorization, ownership, data integrity, destructive actions, migrations, public contracts, or irreversible workflows.
  • Enumerate plausible defect classes: incorrect success, rejected valid input, accepted invalid input, boundary error, partial failure, duplicate delivery, ordering, timeout, retry, cancellation, race, rollback, recovery, and compatibility drift; state what protected outcome is lost or what concrete harm follows.
  • Separate product risks from implementation details and behavior already guaranteed by a dependency; exclude technically representable states that protect no unique local outcome or decision.
  • Identify privacy-sensitive or regulated test data and require synthetic, minimized, or explicitly approved fixtures.
  • Use current external evidence only when version-sensitive contracts, recurring user failures, abuse patterns, or interoperability risks can change the map.
  • Rank risks qualitatively and explain ties or uncertainty; do not manufacture precision from missing frequency or impact data.

3. Decide Portfolio Actions, Levels, and Oracles

  • Assign every material risk and affected test exactly one provisional action: KEEP when trusted unique proof remains valid; ADD for an unproved material risk; UPDATE when valuable intent remains but basis, boundary, setup, or oracle changed; MERGE for safely consolidatable proof; DELETE for obsolete, duplicate, trivial, or untrustworthy proof; or NO_TEST when another control or accepted risk is sufficient.

  • For DELETE or MERGE, prove that the test basis is obsolete or identify replacement evidence that preserves every still-required material behavior, failure mode, oracle, and useful failure localization; never retain obsolete proof merely because it already exists.

  • Test value and boundary: Require every test to detect a concrete defect in this product's business logic and name the protected business outcome. Prefer E2E through user or external-system boundaries; use integration or unit tests only for business scenarios difficult to exercise reliably through E2E. Reject platform, trivial-wiring, implementation-detail, and duplicate proof with no distinct business failure signal.

  • Define the minimum sufficient independent oracle, combining observations when the contract requires them: returned contract, durable state, emitted event, rendered behavior, external effect, invariant, or deterministic artifact.

  • Check that mocks and fakes do not bypass the boundary or failure semantics the scenario claims to prove.

  • UI test locators: Use stable project-native semantic locators (roles, accessible names, labels) or explicit IDs/test hooks according to the observable contract and locale strategy. Avoid styling, position, timing, and incidental structure. Treat exact-copy assertions separately when copy is a requirement; do not require product edits solely to add hooks when a robust semantic locator exists.

  • Include positive, invalid, boundary, authorization, error, recovery, concurrency, and compatibility cases only where the risk map makes them material.

  • Specify non-default configuration, time, locale, randomness, ordering, or data scale when defaults could conceal hard-coded behavior.

  • Add browser, device, operating-system, runtime, or version cells only when the supported contract or a known risk makes them decision-relevant.

  • Prefer deterministic setup and bounded data; identify where real dependencies, emulators, disposable environments, or production-like topology are necessary.

  • Define the repository gate or diagnostic role, entry prerequisites, and evidence-based completion criteria for each portfolio action; do not use test count or raw coverage as completion.

4. Produce a Prioritized Test Matrix

  • For every decision, name the test basis, protected outcome, risk, existing evidence or affected test, portfolio action, level, setup, oracle, expected evidence, environment, gate, and result state when known.
  • Define a review or retirement trigger for evidence whose value depends on a contract, migration, compatibility window, workaround, incident, dependency, or temporary risk; do not invent dates without an owned reason.
  • Order scenarios so safety-critical and high-information checks run before expensive breadth, while preserving prerequisite and state dependencies.
  • Identify which scenarios can run in parallel and which share mutable state, rate limits, accounts, devices, or environment setup.
  • Classify gates by failure consequence and required detection time; place slow diagnostic checks outside routine gates only when another control covers release-critical risk.
  • State exclusions explicitly, including scenarios with no unique protected outcome or defect signal, low-value duplication, framework behavior, infeasible environments, and accepted residual risks.
  • Map material requirements and operational risks to distinct evidence, the owning test boundary, prerequisites, and pass criteria; identify which checks remain valid after a requirement or environment changes.
  • Use READY when the strategy is executable and decision-complete, INCONCLUSIVE when useful partial planning is possible but material evidence is missing, and BLOCKED when requirements or a safety-critical boundary cannot be established.
  • Reconcile the risk map and decision ledger: no material risk or affected test lacks an action and supporting rationale.
  • State the smallest next evidence-gathering action for every INCONCLUSIVE or BLOCKED area.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Protected outcome → defect class → impact → existing proof → priority. Per affected test or gap: portfolio action, level/scenario/environment, independent oracle, required/diagnostic gate and result, and review/retirement trigger. Report net portfolio effect and justified NO_TEST, excluded low-value duplication, environment needs, and exact evidence actions for inconclusive areas.

Frequently asked questions

What does the Ln 32 Test Strategy Planner AI skill do?

Plans risk-based test portfolios and acceptance evidence; does not write or execute tests.

Why use Ln 32 Test Strategy Planner on TypingMind?

Because you install it once and use it with any model. Ln 32 Test Strategy Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ln 32 Test Strategy Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/delivery-planning-suite/skills/ln-32-test-strategy-planner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 32 Test Strategy Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ln 32 Test Strategy Planner?

As many as you like. As long as a model supports skills, you can use Ln 32 Test Strategy Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 32 Test Strategy Planner AI skill free?

Yes. It is published on GitHub by levnikolaevich under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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