Using Superpowers
farm-fe/farm
A skill your agent uses when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
Clarify requirements through targeted questions — uncovers unknown unknowns in specs
$ npx skills add jellydn/my-ai-tools --skill spec-interview -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jellydn/my-ai-tools spec-interview --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/spec-interview .claude/skills/spec-interview && rm -rf skills-srcUse ~/.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/
Install the "spec-interview" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/spec-interview into .claude/skills/spec-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-interview", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jellydn/my-ai-tools/tree/main/skills/spec-interviewType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jellydn/my-ai-tools --skill spec-interview -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jellydn/my-ai-tools spec-interview --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/spec-interview .agents/skills/spec-interview && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "spec-interview" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/spec-interview into .agents/skills/spec-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-interview", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jellydn/my-ai-tools --skill spec-interview -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jellydn/my-ai-tools spec-interview --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/spec-interview .cursor/skills/spec-interview && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "spec-interview" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/spec-interview into .cursor/skills/spec-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-interview", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jellydn/my-ai-tools.git --path skills/spec-interview--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jellydn/my-ai-tools --skill spec-interview -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jellydn/my-ai-tools spec-interview --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/spec-interview .gemini/skills/spec-interview && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "spec-interview" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/spec-interview into .gemini/skills/spec-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-interview", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jellydn/my-ai-tools spec-interviewInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jellydn/my-ai-tools --skill spec-interview -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/spec-interview .github/skills/spec-interview && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "spec-interview" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/spec-interview into .github/skills/spec-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-interview", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jellydn/my-ai-tools --skill spec-interview -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jellydn/my-ai-tools spec-interview --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jellydn/my-ai-tools.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/spec-interview .opencode/skills/spec-interview && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "spec-interview" agent skill from https://github.com/jellydn/my-ai-tools/tree/main/skills/spec-interview into .opencode/skills/spec-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-interview", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
spec-interviewClarify requirements through targeted questions — uncovers unknown unknowns in specs
Spec Interview is an agent skill from jellydn/my-ai-tools. Clarify requirements through targeted questions — uncovers unknown unknowns in specs
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: cline, claude, opencode, amp, codex, gemini, cursor, pi
It sits in Agent Workflows, covering Requirements gathering. The repository describes itself as: Comprehensive configuration management for AI coding tools - Replicate my complete setup for Claude Code, OpenCode, Amp, Li, Codex and Claude Code Switch with custom… The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 7a06584. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
cline, claude, opencode, amp, codex, gemini, cursor, pi
From compatibility in the SKILL.md frontmatter.
Spec Interview loads about 2.1k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 736 words of instructions outside code blocks.
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.
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.
The full file from jellydn/my-ai-tools at commit 7a06584, republished under its MIT licence (© jellydn). 736 words, ~2,100 tokens.
.claude/skills/spec-interview/SKILL.md (or your agent's skills folder).Use this skill when:
The agent interviews you to uncover unknown unknowns in your feature specification, focusing on questions that would change architectural decisions.
Analyze what the user provided:
Identify question categories:
Scope & Boundaries:
User Experience:
Technical Decisions:
Architecture Impact:
Sort by impact on implementation:
Use the ask_user_question tool for each question. Ask one question at a time — present it, wait for the answer, then proceed to the next. This keeps the interview focused and lets the user's answer to one question influence follow-ups.
Guidelines for using ask_user_question:
header to a short category label (max 16 chars), e.g. "Architecture", "Scope", "UX", "Edge Cases"question string with context about why you're askingoptions with concise label (1-5 words) and descriptive description explaining trade-offsExample — single question call:
ask_user_question(questions: [{
header: "Architecture",
question: "Where should the export process run? Large exports could time out or block the web process.",
options: [
{
label: "Synchronous HTTP",
description: "Simple, returns CSV directly in response — but risky for large datasets that could timeout"
},
{
label: "Background job + email",
description: "More robust: process asynchronously, email link when done — requires job queue and storage"
},
{
label: "Streaming download",
description: "Immediate start, handles large data, no queuing needed — but more complex to implement"
}
]
}])When to use open-ended instead of multiple choice:
options with 2-4 broader paths, or use ask_user_question's single-select custom answer ("Type something") for truly open explorationAfter each answer:
After all questions are answered:
These show how to translate each pattern into an ask_user_question call.
// One question at a time
ask_user_question(questions: [{
header: "Architecture",
question: "How should we handle authentication for this feature? This determines whether we modify the current auth flow or build a new one.",
options: [
{
label: "Extend existing auth",
description: "Adds to current flow — simpler but may create coupling"
},
{
label: "New auth abstraction",
description: "Clean separation — more upfront work, more flexible long-term"
},
{
label: "External auth service",
description: "Offload entirely — fastest to build, adds third-party dependency"
}
]
}])ask_user_question(questions: [{
header: "Scope",
question: "Should this feature support multiple organizations from the start?",
options: [
{
label: "Yes, initial release",
description: "Adds 2-3 more integration points and multi-tenant data isolation now"
},
{
label: "Later if needed",
description: "Simpler initial build, but may require data migration later"
},
{
label: "Not needed at all",
description: "Single-tenant only — keeps everything simple"
}
]
}])ask_user_question(questions: [{
header: "Edge Cases",
question: "What should happen when the external API is down during export? The codebase has no retry logic for this service yet.",
options: [
{
label: "Graceful degradation",
description: "Show partial results with a warning banner — best UX when service is degraded"
},
{
label: "Hard error to user",
description: "Show clear error message asking them to retry — simplest implementation"
},
{
label: "Auto-retry with queue",
description: "Queue the request and retry — most robust but requires background job infrastructure"
}
]
}])ask_user_question with a single-question array each time. This keeps the interaction focused and lets answers inform the next question.After each answer via ask_user_question:
After all questions:
A good spec interview:
ask_user_question for focused, one-at-a-time questioning© jellydn, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/spec-interview of jellydn/my-ai-tools.
Open the folder on GitHubat commit 7a06584
Spec Interview 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Spec Interview this skilljellydn/my-ai-tools | 123 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Using Superpowersfarm-fe/farm | 5.6k | 35 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Interview Meaddyosmani/agent-skills | 103k | 6 repos | ~3.8k | Automated safety check: Pass | MIT | |
| Grillingpietheinstrengholt/rssmonster | 564 | 32 repos | ~510 | Automated safety check: Pass | MIT | |
| Agentic Workflow Designerdotnet/Open-XML-SDK | 4.6k | 2 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Ask User QuestionMemTensor/MemOS | 12k | — | ~1k | Automated safety check: Pass | Apache-2.0 |
farm-fe/farm
A skill your agent uses when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
addyosmani/agent-skills
Asks one question at a time, each with a best guess attached, until the agent is about 95 percent sure what you really want, before any plan, spec or code.
pietheinstrengholt/rssmonster
Grill the user relentlessly about a plan, decision, or idea.
dotnet/Open-XML-SDK
Interviews you one question at a time about goal, trigger, permissions and data needs, then drafts a single agentic workflow markdown file.
MemTensor/MemOS
Shows a question as a modal in the interface to clarify a task, collect a preference or get approval, since the user cannot see terminal output.
jnMetaCode/superpowers-zh
Turns a rough idea into an approved design before any code is written, sorting the request into spike, bounded or architectural and enforcing an approval gate.
jellydn/my-ai-tools
A skill your agent uses when monitoring an open GitHub PR for CI failures, review feedback, mergeability, and safe retries or fixes.
jellydn/my-ai-tools
Posts a concise visual outline as a GitHub pull request comment.
jellydn/my-ai-tools
Manage project knowledge with qmd — captures learnings, decisions, and conventions
jellydn/my-ai-tools
Generate Product Requirements Documents from feature ideas — plans specs and requirements
jellydn/my-ai-tools
Build an interactive report or experiment when the user asks to explore model capabilities.
jellydn/my-ai-tools
Fix PR review comments by implementing requested changes. An agent skill from jellydn/my-ai-tools.
Categories
Clarify requirements through targeted questions — uncovers unknown unknowns in specs. Spec Interview is an agent skill from jellydn/my-ai-tools.
Spec Interview fits situations like: tasks that involve Requirements gathering.
Run `npx skills add jellydn/my-ai-tools --skill spec-interview -a claude-code`. Or copy the skill folder (skills/spec-interview in jellydn/my-ai-tools) into .claude/skills/spec-interview in your project. Claude Code loads it when a task matches its description.
Run `npx skills add jellydn/my-ai-tools --skill spec-interview -a codex`. Or copy the skill folder (skills/spec-interview in jellydn/my-ai-tools) into .agents/skills/spec-interview in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jellydn/my-ai-tools --skill spec-interview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/spec-interview, .gemini/skills/spec-interview, .github/skills/spec-interview and .opencode/skills/spec-interview in your project.
SKILL.md names no scripts, command-line tools or credentials: Spec Interview is instructions for the agent only. Compatibility (from SKILL.md): cline, claude, opencode, amp, codex, gemini, cursor, pi.
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.
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.
Spec Interview is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Spec Interview: Using Superpowers (farm-fe/farm, 5.6k stars), Interview Me (addyosmani/agent-skills, 103k stars), Grilling (pietheinstrengholt/rssmonster, 564 stars) and Agentic Workflow Designer (dotnet/Open-XML-SDK, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
jellydn (a GitHub user) maintains it in jellydn/my-ai-tools, which has 123 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 8, 2026.
Source: jellydn/my-ai-tools on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.