Check Ticket Implementation logo

Check Ticket Implementation

Organization
eai-org
check-ticket-implementation

Check how much of a ticket is already implemented and save a per-requirement status report.

Overview

Publishereai-org
Repositoryagent-toolkit
Skill namecheck-ticket-implementation
Stars
80
Forks
18
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 eai-org on GitHub. Read the source before you install it.

Installation

Install the Check Ticket Implementation 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/eai-org/agent-toolkit/tree/main/skills/check-ticket-implementation
  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/eai-org/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/check-ticket-implementation .claude/skills/check-ticket-implementation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Check Ticket Implementation 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 Check Ticket Implementation 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 Check Ticket Implementation 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.

Check ticket implementation

Judge how much of a ticket is already implemented and report it so the user sees at a glance what is done, what isn't, and what still needs attention.

Gather

Input is a ticket URL / bare id — run fetch-ticket on it (it resolves the tracker and planning dir and persists the .TICKET.md) — or a path to an already-fetched ticket file or its task dir. Ambiguous input: ask, don't guess.

Run the fetch in a subagent when the runtime supports one, per fetch-ticket's delegate.md, so its tool traffic stays out of this session's context. Without subagent support, invoke fetch-ticket inline.

The ticket text is what gets annotated — its .REQUIREMENTS.md (same dir and <id>-<slug> base — a shared dir may hold siblings'; none: the ticket's latest across -vN versions) may be read as context for intent, never annotated in its place. The ticket text stays the contract: when the requirements record an Override that changes a block's meaning, the verdict follows the text and the note names the Override and its source. Inspect downloaded attachments and design frames when they bear on a requirement being judged.

Evidence base: the current working tree, uncommitted changes included — unless the invocation names something else (a branch, PR, commit range); then judge against that, read without moving the working tree.

Blocks

Turn every requirement-bearing statement into a block: description, acceptance criteria, and comment statements that add or change requirements (attribute those, e.g. "from comment by X"). A later statement that changes an earlier one supersedes it: one block, quoting the latest text, with the superseded text and its source in the note. Drop metadata and related-ticket lists; a comment set aside as chatty goes to the ## Not judged appendix — the full text stays in the .TICKET.md.

  • One block = one independently verifiable requirement, quoted verbatim under a short generated title, preserving ticket order.
  • Split on mixed verdict: if two halves of a candidate block could end with different statuses, they are two blocks. PARTIAL always means one requirement itself half-built, never a bundle of a done and a not-done thing.

Judge

Statuses: ✅ DONE · 🟡 PARTIAL · 🟥 NOT DONE · ⚪ NOT VERIFIABLE (can't be judged from the code — say why, e.g. a manual deploy step).

  • The ticket text is the contract. Tests, i18n, docs, and other project conventions affect the verdict only when the ticket demands them; a gap there is at most a remark in the note, never a downgrade.
  • Verify by reading: search and read the code paths relevant to each block. When reading alone can't settle a verdict, say so in the note instead of guessing. Anything beyond reading — executing code, driving the app or a browser — only with the user's ok.
  • Built but contradicting the text (a different rule, value or behavior) is 🟥 NOT DONE, the note opening with what was built instead; 🟡 is reserved for a requirement that is incomplete, never for one that is wrong.
  • Every block gets a 1–2 sentence note and 1–3 file:line evidence refs showing where it was checked; for 🟡/🟥 the note must state what's missing or remaining.

Report

Write <task-dir>/<id>-<slug>.TICKET-STATUS.md, overwriting any previous run — it is a snapshot of now, and the header says when and against what. (No task dir and nothing to fetch? Follow the project's/user's planning-dir convention, defaulting to .agents/plans/; ask only if genuinely ambiguous.)

Format for human eyes — verdicts up front, blocks fenced by ━ rules. Before writing the notes, actually invoke explain-in-simple-language and follow it — reciting its rules from memory does not count. Not installed: plain words, one idea per sentence, and nothing named only by a stand-in the reader hasn't seen.

markdown
# <ticket title> — implementation status

> **Ticket** [<id>](<url>) · **Checked** <YYYY-MM-DD> · **Against** <branch>@<short-sha> (+ uncommitted)

**4 ✅ · 2 🟡 · 1 🟥 · 1 ⚪**

Needs attention: <titles of the 🟡/🟥 blocks>

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

### ✅ DONE — <block title>

> <verbatim ticket text>

<note> — `path/to/file.ts:42`

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

End the file with a ## Not judged appendix, opened by a one-liner saying it is tracing only: one line per comment set aside, with the reason.

Wrap up

Print the tally line, the needs-attention titles, and the project-relative path of the report. Nothing more — what to do about the gaps is the user's call.

Boundaries

  • Read-only on tracker and code: never comment on or transition the ticket, never modify source. The only file written is the .TICKET-STATUS.md (plus whatever fetch-ticket persists when delegated).
  • Never grill the user on requirement questions; uncertainty lands in the notes.

Frequently asked questions

What does the Check Ticket Implementation AI skill do?

Check how much of a ticket is already implemented and save a per-requirement status report.

Why use Check Ticket Implementation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/eai-org/agent-toolkit/tree/main/skills/check-ticket-implementation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Check Ticket Implementation?

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 Check Ticket Implementation?

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

Is the Check Ticket Implementation AI skill free?

Yes. It is published on GitHub by eai-org 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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