Implementation Readiness logo

Implementation Readiness

Organization
l-gevity
implementation-readiness

Determines whether graph-structured requirements are ready for architecture, development, and verification, then derives the smallest coherent build-preparation package without inventing requirement meaning. Use when producing capability maps, epics or workstreams, implementation increments, dependency sequencing, riskiest-assumption ordering, parallel-ready increments, contract candidates, domain-model seeds, aspects, acceptance-test references, ADR seeds, technical questions, or explicit ready/partly-ready/not-ready decisions. Do not use for initial discovery or requirement graph normalization.

Overview

Publisherl-gevity
Repositoryl-gevity-skills
Skill nameimplementation-readiness
Stars
72
Forks
13
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 l-gevity on GitHub. Read the source before you install it.

Installation

Install the Implementation Readiness 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.
    https://github.com/l-gevity/l-gevity-skills/tree/main/.agents/skills/implementation-readiness
  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/l-gevity/l-gevity-skills.git /tmp/l-gevity-skills
mkdir -p .claude/skills
cp -r /tmp/l-gevity-skills/.agents/skills/implementation-readiness .claude/skills/implementation-readiness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Implementation Readiness 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 Implementation Readiness 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 Implementation Readiness 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.

Implementation Readiness

Convert a validated requirements topology into a traceable package developers, architects, QA engineers, and technical product owners can use to prepare a build.

Core Directives

  1. Readiness is a decision, not document polish. Missing evidence, permissions, dependencies, or acceptance conditions remain blockers.
  2. The requirements remain authoritative. The handoff is a derived build-preparation view.
  3. Do not turn uncertainty into design. Preserve open product, domain, policy, privacy, security, and architecture decisions.
  4. Graph edges guide sequence, not boundaries. A dependency does not imply an API, event, service, or synchronous call.
  5. Prefer the smallest coherent increment. Prove an end-to-end outcome before expanding implementation surface.
  6. Riskiest assumption first. Sequence experiments and increments so the assumption most likely to invalidate the plan is tested earliest, by the cheapest vehicle that can invalidate it.

Boundary

Use requirements-grounding when the problem, source basis, actor, scope, priority, or complete-when conditions are unclear. Use requirements-topology when stable IDs, typed edges, duplicate/conflict checks, or dependency order are missing.

Read project instructions, architecture constraints, domain glossaries, source catalogs, and policy files when present. Treat them as inputs; do not generalize their domain rules into this skill.

After this skill admits an increment into implementation, use requirements-traceability to maintain implementation anchors, executed test or operational evidence, reverse traceability, and stale-reference checks. Readiness defines the evidence obligation; traceability records whether later work fulfills it.

Minimum Inputs

Require enough information to make readiness falsifiable:

  • stable requirement IDs and atomic statements;
  • source lineage and validation status;
  • usable complete-when conditions;
  • owning problem scope or foundation capability;
  • typed dependencies and dependency order;
  • actors, permissions, and relevant constraints;
  • external prerequisites and known decision/watch items.

If the user supplies raw requirements, do not pretend they are implementation ready. Run a light grounding/topology pass and label the result incomplete, or route to the missing stage.

Workflow

  1. Verify topology stability: no blocking cycles, unresolved ID transformations, likely duplicates, must-have verification gaps, or ownerless prerequisites.
  2. Group requirements into cohesive capabilities and workstreams without changing meaning.
  3. Pull foundation capabilities, aspects, data ownership, and evidence primitives early in the sequence.
  4. Check every external prerequisite for an existing artifact or an explicit minimal contract. Mark dependent work partial or blocked when neither exists.
  5. Convert prerequisite edges into implementation order while keeping architecture choices open. Within that order, place first the increment or experiment that tests the riskiest unvalidated assumption: the one most likely to be wrong, weighted by the cost of learning that late. Choose the cheapest vehicle that can invalidate it (see Assumption Vehicles).
  6. Derive only supported domain-model seeds, contract candidates, integration points, data boundaries, and non-functional constraints.
  7. Identify ADR candidates where multiple viable designs or unresolved forces remain.
  8. Define the smallest coherent increment — one cut through every layer, so it demonstrates the core outcome end to end (a vertical slice). Mark an increment parallel-ready only on criteria readable from the artifacts: it is isolated (needs little context beyond its own scope), shares no unmerged prerequisite, carries no unresolved contract, is mergeable on its own, and is independent of every other increment in its group. Record parallel-ready groups in the implementation order. Who will pick an increment up is a scheduling fact, not a property of the increment; it cannot be read from any artifact and is not a criterion.
  9. Reference grounding's complete-when conditions as acceptance tests; add only concrete fixtures, expected values, and edge cases still needed.
  10. Separate ready work, blockers, risks, and technical questions.
  11. Seed stable requirement and acceptance-criterion references for downstream traceability without claiming implementation or verification evidence yet.

Assumption Vehicles

Match the vehicle to the assumption it must be able to invalidate. Each is a bounded experiment, not the product:

VehicleInvalidatesConstraint
Proof of conceptOne named feasibility or technical assumptionAny technology, disposable; throwaway code never crosses the merge boundary without the enforcement architecture-as-code requires
PrototypeA functional or interaction assumption, with stakeholdersOne-off, without the non-functional obligations; demonstrates, never ships
Minimum viable productThe linked outcome hypothesis, through real useComplete enough for one full build-measure-learn turn, and the first end-to-end exercise of the delivery path

Record the vehicle, the assumption, and the invalidation criterion. For a need or outcome assumption, carry the criterion from the grounding experiment it serves or from the hypothesis's Decision threshold; do not author a new one. For a feasibility or technical assumption, state the criterion in the technical question or ADR seed that raised it; that is derived build preparation, not requirement meaning. A vehicle without an invalidation criterion is speculative build, not an experiment.

An assumption invalidatable by inspection is resolved, not scheduled. If reading the code, schema, configuration, or contract answers the question, answer it now and record the fact. Carrying a lookup as an open assumption inflates the risk list, and — because ordering is riskiest-first — displaces the assumption that actually needed the first increment. Only questions no available artifact can settle earn a vehicle and a position in the order.

Readiness Gate

Mark a requirement or increment ready only when it has:

  • stable, traceable requirement IDs;
  • usable complete-when conditions;
  • a clear owner and actor;
  • known prerequisites with existing artifacts or named minimal contracts;
  • identified data ownership and lifecycle;
  • relevant security, privacy, accessibility, compliance, audit, and operational constraints, with topology's Aspect coverage: at Pass for every aspect that holds across the increment's capability, or each Fail carried as a blocking gap with an owner;
  • no unresolved decision that changes the required outcome;
  • the retirement of every criterion the increment's mechanism replaces, admitted as part of the increment;
  • an accepted validation decision, or an explicitly reversible experiment.

Mark it partly ready when a bounded implementation can proceed behind an explicit assumption, adapter, configuration point, or reversible decision.

Mark it not ready when source meaning, actor permission, data availability, acceptance conditions, external ownership, or dependency order prevents a responsible implementation decision.

Readiness, implementation, and verification are independent states. A READY decision permits work to start; it does not mean an artifact exists or evidence has passed.

Do not use architecture uncertainty alone as a blocker when the requirement is clear and the design decision can be captured as an ADR. Do treat product or policy uncertainty as a blocker when different answers change the required outcome.

Derived Artifacts

Produce only what the current audience needs:

text
Capability:
- Purpose:
- Requirement IDs:
- Owner:
- Inputs and outputs:
- Constraints:
- Evidence:
- Prerequisites:
- Ready state:
text
Implementation increment:
- Outcome:
- Requirement IDs:
- Actor workflow:
- Prerequisites:
- In scope / out of scope:
- Acceptance-test references:
- Data touched and ownership:
- Aspects touched:
- Roles and permissions:
- Evidence and operations:
- Parallel-ready: yes | no + missing criterion
- Risks and watch items:
- Developer / architect questions:
text
Contract candidate:
- Kind: command | query | event | import | export | API
- Purpose:
- Producer / consumer:
- Domain terms:
- Validation:
- Security and privacy:
- Evidence and operations:
- Requirement traceability:
text
ADR seed:
- Decision needed:
- Requirement traceability:
- Forces and constraints:
- Viable options:
- Recommendation, if supported:
- Consequences:
- Enforcement candidate:
- Revisit trigger:

These are design inputs, not automatic commitments. Use architecture-guidelines when turning them into subsystem or service designs, and use functionality-complexity-tradeoff when a proposed capability still needs a worth decision.

Output Contract

Every application emits a decision record before any longer readiness package:

text
Subject:            <requirement scope, capability, or increment>
Decision:           READY | PARTLY-READY | NOT-READY
Requirement IDs:    <stable IDs covered by the decision>
Prerequisites:      <ready, assumed, or missing>
Blocking gaps:      <source, outcome, owner, data, permission, verification, or contract>
Smallest increment: <smallest coherent outcome supported now, or none>
Open decisions:     <product / policy blockers versus architecture ADRs>
Next action:        <build, decide, source, contract, split, or return upstream>
Verification:       <readiness checks run, or Not run + reason>
Revisit when:       <required for PARTLY-READY or NOT-READY>

A complete readiness package additionally contains:

text
Implementation readiness:
- Meta-context:                 # standalone artifacts only
- Canonical grounding/topology:
- Readiness decision:           # ready | partly ready | not ready
- Blocking gaps and owners:
- Capability map:
- Workstreams or epics:
- Implementation order:         # riskiest assumption first; parallel-ready groups marked
- Smallest coherent increment:
- Aspects:                      # aspect × capability matrix, projected from topology
- Data ownership and lifecycle:
- Evidence and operational needs:
- Contract candidates:
- Domain-model seeds:
- ADR seeds:
- Acceptance-test references:
- Technical questions:
- Traceability:

Aspects is the aspect × capability matrix: one row per aspect (a constraint node with holds_across), one column per capability in the package, and in each cell covered by <ID>, open + owner, or excluded by <grounding or topology record>. It is a projection of topology's holds_across and constrains edges onto the capability map — derived build preparation, never a second authority: an exclusion cites a record and is never a readiness reason. An increment's Aspects touched names the rows it must satisfy.

For a standalone artifact, state audience, purpose, completion date, source artifacts, source currency, and caveats once near the top. Keep every derived item traceable to requirement IDs and keep blockers visible beside the readiness decision.

Guardrails

  • Do not rewrite source requirements to fit a preferred architecture.
  • Do not invent payloads, entities, integrations, or non-functional targets that the requirements do not support.
  • Do not restate complete-when conditions as a competing acceptance-criteria set.
  • Do not admit a replacement without the retirement it implies. An increment that removes or replaces a mechanism carries the lapsing criteria and closes them.
  • Do not turn every graph edge into a contract or runtime dependency.
  • Do not mark an increment parallel-ready while it shares an unmerged prerequisite or an unresolved contract with another increment in its group.
  • Do not schedule an assumption that reading an existing artifact would settle; resolve it and record the answer.
  • Do not bury product, domain, security, privacy, compliance, or source-currency decisions inside developer notes.
  • Do not mark a requirement implemented or verified in a readiness package; hand the admitted IDs and evidence obligations to requirements-traceability.
  • If grounding or topology changes materially, refresh the readiness package or state exactly what is stale.

See also

  • requirements-grounding — problem, source, evidence, and canonical meaning.
  • requirements-topology — stable IDs, typed graph, repository validation, and order.
  • requirements-traceability — implementation and executed verification after readiness.

Frequently asked questions

What does the Implementation Readiness AI skill do?

Determines whether graph-structured requirements are ready for architecture, development, and verification, then derives the smallest coherent build-preparation package without inventing requirement meaning. Use when producing capability maps, epics or workstreams, implementation increments, dependency sequencing, riskiest-assumption ordering, parallel-ready increments, contract candidates, domain-model seeds, aspects, acceptance-test references, ADR seeds, technical questions, or explicit ready/partly-ready/not-ready decisions. Do not use for initial discovery or requirement graph normaliz...

Why use Implementation Readiness on TypingMind?

Because you install it once and use it with any model. Implementation Readiness 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 Implementation Readiness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/l-gevity/l-gevity-skills/tree/main/.agents/skills/implementation-readiness. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Implementation Readiness?

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 Implementation Readiness?

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

Is the Implementation Readiness AI skill free?

Yes. It is published on GitHub by l-gevity 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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