Ln 31 Delivery Plan Builder logo

Ln 31 Delivery Plan Builder

Community
levnikolaevich
ln-31-delivery-plan-builder

Builds dependency-ordered delivery plans from requirements and repository evidence; read-only.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-31-delivery-plan-builder
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 31 Delivery Plan Builder 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-31-delivery-plan-builder .claude/skills/ln-31-delivery-plan-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 31 Delivery Plan Builder 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 31 Delivery Plan Builder 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 31 Delivery Plan Builder 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.

Delivery Plan Builder

Goal: Return a decision-complete, proportionate delivery plan for the requested outcome. Keep planning read-only: do not edit files, create tracker items, implement, publish, or deploy.

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 capabilityFallback
Outcome and constraintsUser request, requirements and accepted design decisionsEquivalent conversation or repository evidence; no mandatory upstream skill
Implementation ownershipCode intelligence, focused definitions and consumer/configuration readsNarrow symbol search and direct causal tracing
Verification and deliveryRepository test/build/release definitions and environment contractsName exact missing prerequisite and feasible evidence action

Domain Rules

  • Plan complete observable increments rather than arbitrary file batches. Separate planned work, evidence and authorization.
  • Prefer existing project conventions and the smallest complete implementation; estimates are ranges with assumptions, not promises.
  • A migration plan owns detailed transition semantics when supplied; reference it without inventing a competing sequence.

Checklist

1. Establish the Delivery Contract

  • Resolve the business outcome, acceptance, protected behavior, authorized implementation boundary and non-goals.
  • Inspect repository instructions, relevant Git state, source requirements and accepted/proposed design status.
  • Trace affected entrypoints, owning logic, state, consumers and integrations; identify evidence gaps that could change the plan.
  • Resolve consequential intent, compatibility and external-state choices before presenting a ready plan.

2. Choose the Work Units

  • Identify the smallest complete approach, including no change, configuration, deletion or reuse where it satisfies acceptance.
  • Divide necessary work into independently verifiable outcomes with explicit inputs, owning boundaries and completion evidence.
  • Map every material requirement and protected invariant to at least one work unit and acceptance check.
  • Identify dependencies from contracts, data, deployment order and shared state; remove cycles or expose the required decision.
  • Allow parallel work only where interfaces and mutation ownership are independent; do not mandate agent delegation.
  • Specify integration checks between units and the final observable journey; local unit completion alone is insufficient.

3. Plan Verification and Recovery

  • 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.
  • Map material failure and regression risks to the smallest reliable checks, their prerequisites and pass criteria.
  • Reuse valid existing test evidence and strategy; identify needed additions, updates, retirements or justified no-test decisions.
  • Identify migration, feature-flag, compatibility and rollout dependencies with abort and recovery conditions where applicable.
  • State any irreversible step and authorized external boundary; never promise rollback where only roll-forward is viable.
  • Distinguish local implementation, release publication, deployment and product-outcome verification.

4. Review Plan Completeness

  • Check requirement coverage, dependency order, integration ownership and absence of hidden consequential decisions.
  • Size effort only when useful, with assumptions and uncertainty; identify the critical path without manufactured precision.
  • Return the plan in the response with source identities and explicit unresolved evidence; do not persist a tracker or document.
  • Identify what requirement or source changes would invalidate each affected work unit or check.

Verdict

  • READY: the plan covers acceptance and integration with executable units, credible verification and resolved consequential decisions.
  • REVISE: a usable plan has explicit gaps or conflicting dependencies to resolve.
  • BLOCKED: essential intent, ownership or evidence is unavailable and no bounded plan can be responsibly established.

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: Outcome and source state; requirement-to-unit-to-verification mapping; dependencies, integration, affected boundaries, recovery, estimates when useful, and exact unresolved prerequisites.

Frequently asked questions

What does the Ln 31 Delivery Plan Builder AI skill do?

Builds dependency-ordered delivery plans from requirements and repository evidence; read-only.

Why use Ln 31 Delivery Plan Builder on TypingMind?

Because you install it once and use it with any model. Ln 31 Delivery Plan Builder 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 31 Delivery Plan Builder 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-31-delivery-plan-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 31 Delivery Plan Builder?

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 31 Delivery Plan Builder?

As many as you like. As long as a model supports skills, you can use Ln 31 Delivery Plan Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 31 Delivery Plan Builder 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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