Agent skill

Kanban Refine

by cyanluna-git in cyanluna-git/cyanluna.skills

Refine backlog requirements through structured user interview.

MITAuto-check passedProductivity & Automation

Install Kanban Refine

skills CLI
$ npx skills add cyanluna-git/cyanluna.skills --skill kanban-refine -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cyanluna-git/cyanluna.skills kanban-refine --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cyanluna-git/cyanluna.skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/kanban-refine .claude/skills/kanban-refine && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
kanban-refine
GitHub stars
183
Token cost
~1.5k tokens
SKILL.md length
67 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Refine backlog requirements through structured user interview.

  • Tasks that involve Task management
  • Calls python3
  • Tasks that involve User research
  • Tasks that involve User stories

What it does

Kanban Refine is an agent skill from cyanluna-git/cyanluna.skills. Refine backlog requirements through structured user interview. Turns rough task descriptions into concrete, actionable requirements with goal, scope, acceptance criteria, and edge cases.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering Task management, User research and User stories. The licence is MIT.

When your agent uses it

  • Tasks that involve Task management
  • Tasks that involve User research
  • Tasks that involve User stories

Example prompts

  • “/kanban-refine”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit df5be37. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Kanban Refine loads about 1.5k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 67 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cyanluna-git/cyanluna.skills at commit df5be37, republished under its MIT licence (© cyanluna-git). 67 words, ~1,491 tokens.

Download SKILL.mdSave it as .claude/skills/kanban-refine/SKILL.md (or your agent's skills folder).
name
kanban-refine
description
Refine backlog requirements through structured user interview. Turns rough task descriptions into concrete, actionable requirements with goal, scope, acceptance criteria, and edge cases.
license
MIT

Shared context: read ../kanban/shared.md for DB path, pipeline levels, status transitions, DB operations, error handling, and agent context flow. Safety principles: read ../kanban/principles.md — mandatory, not optional.

/kanban-refine <ID> — Refine Backlog Requirements

Reads a rough backlog item and refines it into concrete, actionable requirements through structured user interview.

Target: tasks in todo status (backlog). If the task is not todo, warn the user and confirm before proceeding.

Procedure
① Read the task
   TASK = sqlite3 -json "$DB" "SELECT id, title, description, priority, level, tags FROM tasks WHERE id=$ID AND project='$PROJECT'"
   Extract: title, description, priority, level, tags

① ½. Look for prior implementation context (always run this before the interview)

   a. Check description and tags for dependency hints:
      - "Depends on: #NNN" lines in description
      - Tags like "after:NNN", "follows:NNN"

   b. If dependency found → fetch that card's implementation output:
      PRIOR = sqlite3 -json "$DB" "SELECT title, implementation_notes, plan FROM tasks WHERE id=$NNN AND project='$PROJECT'"
      Also inspect the actual codebase: read files, interfaces, schemas confirmed in that card.

   c. If no explicit dependency → ask ONE question before the main interview:
      "Is there a prior task whose implementation this builds on? (task ID or 'none')"
      If the user gives an ID, fetch it as in (b).
      If "none" or new work → skip, proceed with regular interview.

   d. Summarize what was confirmed from prior implementation:
      PRIOR_CONTEXT = {
        confirmed interfaces, schemas, file paths, component names, API routes, etc.
      }
      This context is injected into ③ (gap analysis) and ⑤ (description synthesis).

② Display current state
   Show the user their raw title + description as-is.
   If PRIOR_CONTEXT exists, also show: "Prior implementation context: [summary]"

③ Analyze for gaps
   Identify what's missing or vague across these dimensions:
   - WHAT: What exactly should be built/changed?
   - WHY: What problem does this solve? What's the motivation?
   - SCOPE: What's included vs excluded?
   - ACCEPTANCE: How do we know it's done?
   - CONSTRAINTS: Technical limitations, compatibility, performance?
   - EDGE CASES: Error states, boundary conditions?
   - DEPENDENCIES: Does it depend on other tasks or external systems?

④ Interview the user (MANDATORY)
   Use AskUserQuestion to ask about the gaps found in ③.
   Rules:
   - Ask 1–4 focused questions per round (AskUserQuestion limit)
   - Group related questions in one round
   - Run multiple rounds if needed (max 3 rounds)
   - Stop early if the user says "enough" or all gaps are filled
   - Don't ask about things that are already clear
   - Use concrete options when possible, not open-ended questions

⑤ Synthesize refined description
   Rewrite the description using this template.
   If PRIOR_CONTEXT exists, ground scope/requirements/constraints in confirmed interfaces
   and file paths from the prior implementation — not assumptions.

   ## Goal
   [1–2 sentences: what this task achieves and why]

   ## Prior Implementation Context  ← include only if PRIOR_CONTEXT exists
   [Confirmed interfaces, schemas, components, or file paths from the prior card
    that this task directly builds on. e.g. "POST /api/items → {id, name} per #201"]

   ## Scope
   - IN: [bulleted list of what's included]
   - OUT: [bulleted list of what's explicitly excluded]

   ## Requirements
   [Numbered list of concrete, testable requirements]

   ## Acceptance Criteria
   - [ ] [Checklist items — each verifiable]

   ## Constraints
   [Technical constraints, if any identified]

   ## Edge Cases
   [Edge cases to handle, if any identified]

   Omit sections that have no content (e.g., skip Constraints if none).

⑥ Present the refined description to the user
   Show the full refined description in a code block.
   Ask user to confirm with AskUserQuestion:
   - "Approve & save" (update the task)
   - "Edit more" (go back to interview)
   - "Cancel" (discard changes)

⑦ Save
   If approved:
   - sqlite3 "$DB" "UPDATE tasks SET description='...', updated_at=datetime('now') WHERE id=$ID AND project='$PROJECT'"
   - Also update title if it was clarified during interview
   - Update level/priority/tags if discussed
   - Append to agent_log (see shared.md → JSON 필드 조작):
     { "agent": "Refiner", "model": "<MODEL_REFINER>", "message": "Requirements refined. N questions across M rounds.", "timestamp": "..." }

### Model Routing

Resolve `MODEL_REFINER` from `../kanban/models.json`:

```bash
MODEL_PROVIDER=${KANBAN_MODEL_PROVIDER:-}
if [ -z "$MODEL_PROVIDER" ] && [ -n "${CODEX_THREAD_ID:-}${CODEX_CI:-}" ]; then MODEL_PROVIDER=codex; fi
if [ -z "$MODEL_PROVIDER" ] && [ -n "${CLAUDE_PROJECT_DIR:-}${CLAUDECODE:-}" ]; then MODEL_PROVIDER=claude; fi
if [ -z "$MODEL_PROVIDER" ] && [ -d .claude ]; then MODEL_PROVIDER=claude; fi
if [ -z "$MODEL_PROVIDER" ] && [ -d .codex ]; then MODEL_PROVIDER=codex; fi

MODEL_REFINER=$(python3 - "$MODEL_PROVIDER" <<'PY'
import json, pathlib, sys
d = json.loads(pathlib.Path("../kanban/models.json").read_text())
provider = sys.argv[1] or d["default_provider"]
print(d["providers"][provider]["refiner"])
PY
)

### Interview Tips

- If the user wrote "로그인 기능 추가" → ask: OAuth/email? Session/JWT? Which pages need auth guards?
- If the user wrote "성능 개선" → ask: Which page/API? Current latency? Target latency? Measurement method?
- If the user wrote "UI 수정" → ask: Which component? What's wrong now? Mockup/reference? Responsive?
- Prefer showing concrete options over open-ended "어떤 걸 원하세요?"

© cyanluna-git, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in kanban-refine of cyanluna-git/cyanluna.skills.

Open the folder on GitHubat commit df5be37

Compare with similar skills

Kanban Refine next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Kanban Refine compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Kanban Refine this skillcyanluna-git/cyanluna.skills183—~1.5kAutomated safety check: PassMIT
Backlog Groomingforcedotcom/salesforcedx-vscode1k—~1.5kAutomated safety check: PassBSD-3-Clause
Design Sprintwondelai/skills2.4k—~3.8kAutomated safety check: PassMIT
Customer Interviewsmenkesu/awesome-pm-skills434—~4.4kAutomated safety check: PassCustom licence
Interview Scriptkillvxk/pm-skills-zh167—~547Automated safety check: PassMIT
Building ProductGTM-Strategist/gtm-strategist-skills264—~5.8kAutomated safety check: PassMIT

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Questions about Kanban Refine

What does Kanban Refine do?

Refine backlog requirements through structured user interview. skills. Refine backlog requirements through structured user interview.

When should I use Kanban Refine?

Kanban Refine fits situations like: tasks that involve Task management; tasks that involve User research; tasks that involve User stories.

How do I install Kanban Refine in Claude Code?

Run `npx skills add cyanluna-git/cyanluna.skills --skill kanban-refine -a claude-code`. Or copy the skill folder (kanban-refine in cyanluna-git/cyanluna.skills) into .claude/skills/kanban-refine in your project. Claude Code loads it when a task matches its description.

How do I install Kanban Refine in Codex?

Run `npx skills add cyanluna-git/cyanluna.skills --skill kanban-refine -a codex`. Or copy the skill folder (kanban-refine in cyanluna-git/cyanluna.skills) into .agents/skills/kanban-refine in your project. Codex loads it when a task matches its description.

Can I use Kanban Refine in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cyanluna-git/cyanluna.skills --skill kanban-refine -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kanban-refine, .gemini/skills/kanban-refine, .github/skills/kanban-refine and .opencode/skills/kanban-refine in your project.

What does Kanban Refine need to run?

Going by SKILL.md and its folder, Kanban Refine needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Kanban Refine access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Kanban Refine safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Kanban Refine use?

Kanban Refine is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Kanban Refine use?

About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Kanban Refine?

Skills that share tags, products or a category with Kanban Refine: Backlog Grooming (forcedotcom/salesforcedx-vscode, 1k stars), Design Sprint (wondelai/skills, 2.4k stars), Customer Interviews (menkesu/awesome-pm-skills, 434 stars) and Interview Script (killvxk/pm-skills-zh, 167 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kanban Refine?

cyanluna-git (a GitHub user) maintains it in cyanluna-git/cyanluna.skills, which has 183 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on June 22, 2026.

Source: cyanluna-git/cyanluna.skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.