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Nexent Spec Coding

OrganizationPopular
ModelEngine-Group
nexent-spec-coding

Run the Nexent requirement or bug lifecycle from SPEC analysis through feature catalog and D1-D5 case design, product implementation, fixed test implementation, local verification, and delivery evidence. Use for features, fixes, refactors, APIs, UI, and model or Agent runtime changes.

Overview

PublisherModelEngine-Group
Repositorynexent
Skill namenexent-spec-coding
Stars
5.9K
Forks
737
Bundled files
6
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ModelEngine-Group on GitHub. Read the source before you install it.

Installation

Install the Nexent Spec Coding 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/ModelEngine-Group/nexent/tree/develop/.agents/skills/nexent-spec-coding
  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/ModelEngine-Group/nexent.git /tmp/nexent
mkdir -p .claude/skills
cp -r /tmp/nexent/.agents/skills/nexent-spec-coding .claude/skills/nexent-spec-coding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nexent Spec Coding 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 Nexent Spec Coding 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 Nexent Spec Coding 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.

Nexent SPEC Coding

Develop Nexent from an evidence-backed SPEC while keeping product behavior, formal D1-D5 test assets, implementation, and verification synchronized.

Repository boundaries

  • Treat nexent/ as the Git-managed product repository and its sibling nexent-doc/ as the document repository.
  • Never run Git commands in the document repository or move SPEC documents into the product repository.
  • Preserve unrelated changes. Record the initial branch/status, applicable repository instructions, build configuration, and test layout.
  • Keep the existing tests under test/backend, test/sdk, and test/ext_components as Legacy UT. Do not map them into the formal D1-D5 manifest.

Read the right references

WorkRequired reference
Choose the SPEC maintenance modespec-maintenance-guide.md
Create or revise proposal.mdproposal-template.md
Create or revise design.md; design formal D1-D5 casesdesign-template.md and test-design-guide.md
Create or revise task.mdtask-template.md
Plan, execute, or close verificationverification-guide.md

Use nexent-test-assets for the concrete feature-catalog, case, automation, manifest, schema, and Excel formats. proposal.md owns the current-change acceptance criteria, design.md owns design rationale and test strategy, and task.md owns the change-level traceability and evidence record.

Gated workflow

1. Analyze

Search existing SPECs and choose the maintenance mode. Inspect the relevant code, callers, interfaces, persistence, services, UI, formal test assets, configuration, and runtime paths. Cite concrete paths, symbols, APIs, schemas, and configuration names. Do not implement production code during analysis.

2. Define behavior and formal D1-D5 cases

Create or update proposal.md, design.md, and task.md. Update the product feature catalog and define observable acceptance criteria. Design every applicable D1-D5 case before product implementation. Each case must have a stable Case ID, owning Feature ID, priority, precise preconditions, steps, expected results, forbidden side effects, and the stage-specific fields required by the repository schemas.

Create a requirement change record under test/changes/requirements/ or a lightweight bug record under test/changes/bugs/. Run the formal asset validators and regenerate the Excel view. Do not invent final script paths, selectors, implementation hashes, or passing results during design.

The design gate fails when an in-scope behavior lacks an applicable case or justified exclusion, a case lacks executable assertions, stage boundaries are violated, affected Feature/Case declarations are stale, or schema and traceability validation fail. Resolve material behavior conflicts before implementation. No separate manual test-case approval is required by this workflow.

3. Implement the product behavior

Implement the minimum change needed to satisfy the defined behavior, following existing architecture and contracts. If implementation reveals that a requirement, product rule, Scenario, or test contract is wrong, update and validate the formal design assets before continuing. Do not silently weaken a case to fit the implementation.

4. Implement fixed tests and the manifest

After product implementation, implement the affected formal D1-D5 scripts under test/automation/d1 through test/automation/d5. Bind each automated script or test item to its Case ID and incrementally update only the affected entries in test/manifests/d1-d5.yaml. Then run full manifest and traceability validation.

For a confirmed bug, a focused failing reproduction may be implemented before the product fix when that is the clearest way to preserve the regression. Existing formal cases should be strengthened instead of duplicated when they already own the behavior.

Legacy UT maintenance remains separate and uses nexent-python-tests only when the change breaks or intentionally updates that suite.

5. Verify affected behavior

Run the affected formal D1-D5 cases selected from the change record. Use fixed Playwright scripts for D4. Keep Mock and Real Smoke results distinct when both profiles apply. Missing, unimplemented, skipped, or expected-failure required cases do not pass. Keep product, test, environment, and external-provider failures distinguishable.

Record sanitized commands, results, evidence paths, and unresolved blockers in task.md. Do not claim API, browser, model, Agent, security, reliability, performance, or deployment acceptance from a lower layer.

6. Close out

Run the unified formal-asset validator, deterministic Excel check, affected tests, and relevant product checks. Confirm SPEC, feature catalog, cases, change record, scripts, manifest, implementation, and evidence agree. Report changed files, Case results, remaining risks, and unverified items. Formal completion requires every current required acceptance criterion to pass.

Stop conditions

Pause the affected work when a material behavior conflict remains unresolved, required access or evidence is unavailable, verification would mutate a system outside the authorized scope, or an acceptance criterion cannot be proved with the available surface. Continue safe independent work when the blocker is local.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nexent Spec Coding AI skill do?

Run the Nexent requirement or bug lifecycle from SPEC analysis through feature catalog and D1-D5 case design, product implementation, fixed test implementation, local verification, and delivery evidence. Use for features, fixes, refactors, APIs, UI, and model or Agent runtime changes.

Why use Nexent Spec Coding on TypingMind?

Because you install it once and use it with any model. Nexent Spec Coding 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 Nexent Spec Coding in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ModelEngine-Group/nexent/tree/develop/.agents/skills/nexent-spec-coding. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nexent Spec Coding?

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 Nexent Spec Coding?

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

Is the Nexent Spec Coding AI skill free?

Yes. It is published on GitHub by ModelEngine-Group 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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