Review Ticket logo

Review Ticket

Organization
eai-org
review-ticket

Triage a ticket or ticket set before work starts, saving a review with the questions worth raising.

Overview

Publishereai-org
Repositoryagent-toolkit
Skill namereview-ticket
Stars
80
Forks
18
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by eai-org on GitHub. Read the source before you install it.

Installation

Install the Review Ticket 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/review-ticket
  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/review-ticket .claude/skills/review-ticket
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Ticket 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 Review Ticket 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 Review Ticket 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.

Review ticket

A pre-pickup triage glance: read a ticket — or a set of tickets forming one feature — usually before anyone has started it, and decide whether it can be picked up or something must be clarified first. The output briefs the developer on the feature and lists the questions worth asking whoever owns the requirements.

Input

  • Local ticket document(s): a single .TICKET.md, several, a ticket-set directory, or a .TICKET.md whose ## Ticket set section lists siblings.
  • One or more ticket URLs or bare ids/keys: delegate — even for a single one — to a subagent running fetch-ticket per its delegate.md, so the tickets land on disk without the fetch bloating this session's context, then review the local files. No subagents in the harness → run fetch-ticket inline.

Missing attachments (a non-empty warn list, or a ticket file marking one not downloaded) stop for one question: supply the files now (/attach-to-ticket when available) or review without them. An /attach-to-ticket run in answer resumes the review with the files it saved; each attachment still missing is named as unchecked material.

If the input is ambiguous or unrecognized, ask rather than guess.

Set mode applies to multiple tickets, a set directory, or a .TICKET.md listing a ## Ticket set. For a lone member file whose intended scope isn't obvious from the request, ask whether to review the whole set or just that ticket.

Read the materials

Read the input ticket file(s), visually inspecting the downloaded images and design frames — they are part of the spec and often where the gaps hide.

Widen only on demand. When the input already tells the feature clearly — the journey, the why, what each ticket delivers — review it as-is: reading beyond the input is never a required step. Settle a specific doubt as cheaply as possible, and only to answer it:

  • first, material already on disk: set siblings, a parent feature/epic, designs — a technical ticket's journey often lives in its parent or a sibling;
  • remotely, only for what is still missing: a design inspected through its tool's MCP, a related ticket deep-dived from the ticket file's shallow related list (title, status, type), a missing parent fetched through the same fetch-ticket subagent (its family routing lands it flat in the set directory).

Unsure the extra context is worth it? Ask. Stop once the doubt is answered; never recurse the related-ticket graph — except to kill a candidate question (see the question bar). Context never widens scope: the review covers only the input tickets, never a parent's other children.

An earlier review

Look for a .TICKET-REVIEW.md of the same ticket or set, beside it or beside an earlier -vN fetch. Its shipped questions are the first candidates: settle each against the ticket text as fetched now (comments included) and its Challenge log, then run the normal bar. Never copy its verdict or walkthrough; the ticket files and the code win.

Compare against the codebase

Weigh the ticket against the current code at adaptive depth: shallow by default, deeper only to (a) ground the walkthrough in how the code behaves today and (b) settle whether a real blocker exists. This is not an exhaustive both-sides verification: go as deep as a specific doubt demands, no more.

The question bar: both gates, or stay silent

A question reaches the output only when it clears both:

  1. Decision-expensive: the answer changes what this developer builds — it blocks starting, or would be costly to reverse because it shapes the implementation (architecture, data model, approach). Cheap, easily-changed details (a color, a label, wording, spacing) are dropped even when unspecified. A real gap whose fix lies wholly outside the input tickets' scope (another repo, another team) is a handoff, not a question: it ships as an action item with a paste-ready message to its owner, and starting never waits on it.
  2. Survives the challenge: a challenger (next section) hunted for the answer across every checkable source and came back empty-handed, with a complete evidence trail.

Cheap-settle first. Before challenging, settle each candidate as far as material already read or trivially reachable allows — set siblings on disk, code already open, an obvious lead (a telling title in an already-fetched relative's related list, a parent's other children included, a code path an earlier check surfaced). What a check settles, settle silently: never ask what you can read. The exhaustive hunts — whole-repo sweeps, tracker keyword search, design-tool exploration — are the challenger's job alone: never duplicate them here, and pass any lead you didn't follow into the challenger prompt.

Zero questions is a clean, common result: the ticket is ready to pick up. Never pad to look thorough. Zero candidates → zero challengers.

The challenge

Before the first challenger, resolve every repo the feature spans, as named by the tickets and the project's governing docs, the other side of each integration included (a backend ticket's frontend, an API's clients). Any that can't be located: one batched ask for their paths; a repo the user skips is listed in the prompt as unavailable.

Each surviving candidate gets one challenger — a subagent or equivalent isolated context, fresh per question, spawned in parallel, strictly read-only — prompted from the template in challenger-prompt.md. When the harness cannot isolate a context, run the same template procedure inline over the same sources and flag each surviving question's why-note "challenged same-context (weaker)".

Judge the verdicts asymmetrically, in both directions:

  • KILLED / RESHAPED — verify before accepting. Accept only on a verifiable citation (file path + lines + verbatim quote, ticket id + quoted text, design frame id): open the load-bearing citation yourself and confirm it exists as quoted and answers the question as asked — related evidence is not an answer. Either check fails → reject the verdict, the question stays. A derivation kill rests on multiple quotes composing an inference: verify each quote and that the conclusion follows. RESHAPED is a partial kill: same citation bar for the answered part, and the trail must explicitly cover the remainder; the narrowed question is not re-challenged.
  • OPEN — accept only with a complete trail: every source class probed, with what it said. On an incomplete trail (a dead or errored challenger counts as one): unless the invocation preset a re-challenge budget, ask the user — one batched ask covering all weak trails — whether to spend a re-challenge aimed only at the unchecked sources. Still incomplete after that → keep the question, naming the unchecked sources in its why-note; an unavailable source (rate-limited tool, missing repo) is recorded as unchecked, never exhausted. A question is dropped only by a verified kill, never by challenger failure.

Fold the verdicts in: KILLED → settled silently, the answer surfacing as a walkthrough fact when it changes what the developer must know; RESHAPED → the question narrows; OPEN → the question ships, its trail feeding the why-note (exhausted and unchecked sources). No OPEN or narrowed question ships before the session has rerun the Derive moves (challenger prompt, step 6) itself over the returned trail — a challenger can return every fact yet miss the inference. A derived answer is a kill, held to the same citation bar; when enacting it lies outside the input tickets' scope, the question converts to a handoff.

Output

Before writing the walkthrough and the why-it-matters 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.

  1. Verdict line first, so the answer lands at once, e.g. 2 questions to resolve before starting or Looks ready to pick up, no blockers. Handoffs never count as blockers here.

  2. Feature walkthrough, in the voice of a senior dev briefing a colleague ("we need to implement X, Y and Z, and there are a couple of questions we might want to ask the PO first"). A good structure could be layered, most important first, so the reader can stop at any depth:

    • Nutshell — the whole feature in 2–3 plain sentences.
    • Flow-line — the journey's shape in one line: log in → client area (**klantportaal**) → submit request → status updates.
    • Journey — one concrete user doing real actions: who they are, what they see and do, why the feature exists — never abstract capability-speak ("users are able to…"). Grounded in the real current behavior you saw in the code; where the contrast clarifies, a bold-led Today / After pair. Must fit one terminal screen (~25 lines).
    • Ticket mapping (set mode) — how each input ticket maps into the journey, one block per ticket, led by its linked ticket id and short title.

    Introduce each core domain term in plain, memorable words at first use — a contrast or direction hook beats a bare definition.

    Technical depth caps at "this screen calls endpoints X and Y to get its data": generally no need to go deep into architecture decisions. Single ticket: same layers, proportionally shorter.

  3. Questions, when any survived the question bar: a numbered list, each item bracketed top and bottom by a ~40-char rule of ━ so the eye jumps between them. Each item carries the question (paste-ready, citing the ambiguous part of the ticket or the relevant code to stay concrete) and a short why-it-matters note to you for deciding whether to forward it. A handoff uses the same block format, ### Handoff · <short label> in place of the number. Actually invoke use-conversational-language and follow it before writing the questions or handoff messages — reciting its rules from memory does not count; runs with neither skip it. Not installed: write plain prose instead, no dashes and no AI tells. Nothing survived: this part holds the handoffs only, or is left out.

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

### 1 · <short label>

Question to ask, a sentence or two in a real person's voice.

Why it matters: the cost if we guess wrong.

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

The review is read by a human — raw in the terminal and later rendered as markdown, so format for both: small blocks of a few sentences, each opened by a bold lead-in, blank lines between them, ~40-char ━ rules between major parts (nutshell + flow-line, journey, ticket mapping) — never tables, nested lists, or a wall of text; no # headings apart from the ### <n> · <label> line opening each question item. In the saved file, embed the one or two on-disk design frames that show the journey's main screens — a picture beats a paragraph. User-facing texts in another language? Follow each mentioned page, area, or label with the name the user sees, bold, in parentheses: "the client area (klantportaal)".

Persist

Print the review and always save it too:

  • Set mode: <dir-name>.TICKET-REVIEW.md in the set directory, named after it (the directory carries the parent id when there is one), so it sorts next to the .TICKET.md files.
  • Single ticket: <id>-<slug>.TICKET-REVIEW.md next to its .TICKET.md, under the ticket file's current base (a -v2 ticket gets a -v2 review).

An earlier review of the same base is replaced, after saying so.

The file must stand alone: carry relative links to each reviewed ticket file and to the parent (its local file when on disk, else its tracker URL), so a later session finds everything from the review file.

End the saved file — never the printed review — with a ## Challenge log appendix, opened by a one-liner saying it's tracing only and safe to ignore: each killed question (a reshape's answered part included) with the answer that settled it and its citation, and each cheap detail the question bar dropped, with the default assumed.

Close by handing off each next phase as its own fenced block under the label quoted below, holding one copy-pasteable launch command in the syntax of the agent tool in use (claude is only the example), session named <phase>-<slug>. A block only when its skill is available; the refine blocks only with no blockers, one per ticket in the set's suggested execution order, each label naming its ticket:

"Verify your understanding of the set first:"

claude --name verify-<slug> "/verify-understanding <review-file>"

"Refine :" (the first starting "or" when the verify block precedes it)

claude --name refine-<slug> "/refine-ticket <ticket-file>"

Then the alternative: one line offering the tool's clear command (/clear is only the example), then the same labelled blocks with the bare slash commands. When Today already seems to deliver the After, also suggest /check-ticket-implementation <ticket-file>, only when that skill is available.

Boundaries

  • Read-only on tracker and code. Never modify the tracker (comments, transitions, edits) or any source file. The only files written are fetch-ticket's outputs, the .TICKET-REVIEW.md, and what an /attach-to-ticket run in answer to the attachments question writes (assets beside the ticket, the ticket's attachment entries, the appended Context line of an existing same-ticket REQUIREMENTS).
  • Non-interactive on requirements. The questions are the output: never grill the user to resolve them. Ask the user only operational things (an ambiguous input, a fetch that needs input, the set-scope call, whether extra context is worth fetching, missing repo paths, missing attachments, whether to spend a re-challenge), never requirement decisions.

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 Review Ticket AI skill do?

Triage a ticket or ticket set before work starts, saving a review with the questions worth raising.

Why use Review Ticket on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/eai-org/agent-toolkit/tree/main/skills/review-ticket. 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 Review Ticket?

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 Review Ticket?

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

Is the Review Ticket 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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