Speckit Specify Fix logo

Speckit Specify Fix

Organization
dotCMS
speckit-specify-fix

Create an issue/bug-resolution specification (defect-framed) from a bug report or issue description. dotCMS variant of /speckit-specify for fixes.

Overview

PublisherdotCMS
Repositorycore
Skill namespeckit-specify-fix
Stars
971
Forks
487
Bundled files
Instructions only
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 dotCMS on GitHub. Read the source before you install it.

Installation

Install the Speckit Specify Fix 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/dotCMS/core/tree/main/.claude/skills/speckit-specify-fix
  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/dotCMS/core.git /tmp/core
mkdir -p .claude/skills
cp -r /tmp/core/.claude/skills/speckit-specify-fix .claude/skills/speckit-specify-fix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speckit Specify Fix 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 Speckit Specify Fix 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 Speckit Specify Fix 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.

User Input

text
$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Purpose

This is the issue / bug-resolution counterpart to /speckit-specify. It produces a defect-framed specification (problem, reproduction, root-cause hypothesis, fix scope, regression risk, acceptance) using the dotCMS issue template, and then feeds the normal /speckit-plan → /speckit-tasks → /speckit-implement → /speckit-converge flow — so the plan phase's Legacy Impact and ADR Alignment gates still apply.

Use /speckit-specify instead when the work is a NEW FEATURE rather than fixing existing behavior.

Outline

  1. Create the feature directory (reuse the standard Spec-Kit script — do NOT reimplement numbering):

    Read feature_numbering from .specify/init-options.json first, and pass --timestamp when it is timestamp:

    bash
    .specify/scripts/bash/create-new-feature.sh --json --timestamp "<short issue description>"   # feature_numbering: timestamp
    .specify/scripts/bash/create-new-feature.sh --json "<short issue description>"               # feature_numbering: sequential or absent

    Why the flag is not optional. create-new-feature.sh does not read init-options.json — it only accepts --timestamp on the command line. The shipped /speckit-specify honours feature_numbering because it builds the directory name itself; this command delegates to the script instead, so without the flag a fix would keep numbering sequentially while features number by timestamp. Nothing would error: the two flows would simply drift apart, and the collision-free guarantee the setting exists for would not hold for fixes.

    Parse the JSON for BRANCH_NAME and SPEC_FILE. If the user explicitly provided a feature directory or branch name, honor it.

  2. Swap in the issue-resolution template. The script above seeds SPEC_FILE with the feature template; overwrite it with the issue template (respecting the override stack):

    • If .specify/templates/overrides/spec-issue-template.md exists, copy that over SPEC_FILE.
    • Otherwise copy .specify/templates/spec-issue-template.md over SPEC_FILE.
  3. Load .specify/memory/constitution.md for project principles (legacy-awareness, ADR rules) — they inform how you scope the fix.

  4. Fill the issue spec at SPEC_FILE, preserving section order and headings, using the bug report / issue text in the user input:

    1. Parse the report. If empty: ERROR "No issue description provided".
    2. Problem Statement + severity/impact.
    3. Reproduction: environment, numbered steps, expected vs actual, reproducibility. If the report lacks repro detail, mark with [NEEDS CLARIFICATION: ...] (max 3 total).
    4. Scope of Investigation: name the affected dotCMS area; note suspected surface (modern com.dotcms.* vs legacy com.dotmarketing.*) as a best guess — the plan confirms it. Do NOT do code-level root-causing here beyond a hypothesis.
    5. Root-Cause Hypothesis: current best theory (may be refined/replaced in planning).
    6. Fix Scope & Non-Goals: keep the fix bounded; list explicit non-goals to avoid scope creep and unintended legacy rewrites.
    7. Regression Risk: blast radius, backward compatibility, data considerations.
    8. Acceptance & Verification: measurable ACs tied to the reproduction, plus the specific test(s) to add/run.
  5. Quality validation. Create a checklist at <feature-dir>/checklists/requirements.md and validate the spec against it:

    markdown
    # Issue Spec Quality Checklist: [ISSUE TITLE]
    
    ## Content Quality
    - [ ] Problem and impact are clear to a non-author
    - [ ] Reproduction steps are concrete and ordered
    - [ ] Expected vs actual behavior are unambiguous
    
    ## Completeness
    - [ ] Affected area / suspected surface identified
    - [ ] Fix scope bounded with explicit non-goals
    - [ ] Regression risk (blast radius, back-compat, data) assessed
    - [ ] Acceptance criteria are measurable and tied to the repro
    - [ ] Verification method (specific test/steps) named
    - [ ] No unresolved [NEEDS CLARIFICATION] markers (or ≤3, most critical only)
    
    ## Readiness
    - [ ] Ready for /speckit-plan (Legacy Impact + ADR Alignment will run there)

    If items fail (excluding [NEEDS CLARIFICATION]), update the spec and re-validate (max 3 iterations). If [NEEDS CLARIFICATION] markers remain, present up to 3 numbered questions with suggested-answer tables and wait for the user's response.

  6. Report the branch, the issue SPEC_FILE path, and any open clarifications. Recommend next step: /speckit-plan (which will consult ADRs and assess legacy impact).

Guardrails

  • This skill scopes a fix; it does not write code. /speckit-implement does that later.
  • Never create, edit, or commit an ADR (here or in dotCMS/platform-adrs). If the fix implies a new architectural decision, it will be captured as a proposal in the plan's "Proposed ADRs" section. ADRs are authored only in dotCMS/platform-adrs via new-adr.sh.

Frequently asked questions

What does the Speckit Specify Fix AI skill do?

Create an issue/bug-resolution specification (defect-framed) from a bug report or issue description. dotCMS variant of /speckit-specify for fixes.

Why use Speckit Specify Fix on TypingMind?

Because you install it once and use it with any model. Speckit Specify Fix 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 Speckit Specify Fix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dotCMS/core/tree/main/.claude/skills/speckit-specify-fix. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Speckit Specify Fix?

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 Speckit Specify Fix?

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

Is the Speckit Specify Fix AI skill free?

It is published on GitHub by dotCMS. Check the repository for licensing terms. 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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