Agent skill

Next Steps

by swyxio in swyxio/skills

Turn an ongoing task or conversation into evidence-informed opportunities and an actionable decision workspace.

MITAuto-check passed

Install Next Steps

skills CLI
$ npx skills add swyxio/skills --skill next-steps -a claude-code

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

GitHub CLI
$ gh skill install swyxio/skills next-steps --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/swyxio/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/next-steps .claude/skills/next-steps && 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
next-steps
GitHub stars
175
Token cost
~1.9k tokens
SKILL.md length
997 words
Files
2
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

Turn an ongoing task or conversation into evidence-informed opportunities and an actionable decision workspace.

  • The user asks what next
  • SKILL.md covers Orient without reciting the…, Discover before recommending, Generate genuinely different… and Quantify honestly, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Invokes $next-steps

What it does

Next Steps is an agent skill from swyxio/skills. Turn an ongoing task or conversation into evidence-informed opportunities and an actionable decision workspace. Use when the user asks what next, invokes $next-steps, or wants to explore, compare, combine, and prioritize possible directions. Adapt from a short conversational recommendation to a richer interactive exploration when the decision warrants it.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

The repository describes itself as: Agent skills for Claude Code and other AI agents. The licence is MIT.

When your agent uses it

  • The user asks what next
  • Invokes $next-steps
  • Wants to explore
  • Prioritize possible directions

Example prompts

  • “/next-steps”

What it can do on your machine

Read from SKILL.md and the folder at commit 038ef34. 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

    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.

  • 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

Next Steps loads about 1.9k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 997 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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 swyxio/skills at commit 038ef34, republished under its MIT licence (© swyxio). 997 words, ~1,874 tokens.

Download SKILL.mdSave it as .claude/skills/next-steps/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
next-steps
description
Turn an ongoing task or conversation into evidence-informed opportunities and an actionable decision workspace. Use when the user asks what next, invokes $next-steps, or wants to explore, compare, combine, and prioritize possible directions. Adapt from a short conversational recommendation to a richer interactive exploration when the decision warrants it.

Next Steps

Do the useful thinking before presenting the menu. Help the user discover and choose opportunities, not merely maintain a task queue. Lead with a point of view about the outcome and what could move it forward.

Orient without reciting the conversation

Use the available context to recover the user's outcome, corrections, accepted choices, constraints, and unresolved questions. Newer decisions supersede older proposals. Do not reactivate abandoned work or ask the user to reconfirm settled preferences.

Keep completed work, user-reported changes, verified live state, proposals, and blockers distinct. Put routine operational status in a short line or expandable detail; let it dominate only when it genuinely blocks progress or presents material risk. Avoid ritual intent/confidence statements and repeated recaps.

Discover before recommending

Do a bounded read-only evidence pass when it could change the options or their ranking. Inspect readily available project data; browse relevant current primary sources, credible practitioner examples, or competing approaches where useful. Bring back an actual finding, example, or comparison instead of turning every answer into a proposal to research later.

Bound the pass around the decision: what uncertainty would change our next move? Stop when sufficient evidence exists to choose a useful next action. Larger investigations can themselves become options with a defined question and deliverable. Respect access, metering, privacy, and paid-call constraints.

Distinguish sourced patterns, observed results, inferences, and speculative ideas. Cite research close to the claims it supports. If discovery is unavailable, show the uncertainty rather than filling it with plausible facts.

Generate genuinely different opportunities

Explore different mechanisms, not several labels for the same administrative work. Depending on the task, consider a dependable improvement, an ambitious bet, and an adjacent or surprising opportunity. These are lenses, not mandatory slots; do not pad the answer with weak ideas.

For each serious candidate, make clear:

  • The opportunity or hypothesis and why it fits this user now.
  • The supporting evidence and the strongest uncertainty or counterargument.
  • A concrete output and first move: a draft, comparison, experiment, implementation, or decision.
  • What success would look like, and what would falsify the idea or stop the work.
  • Material effort, spend, risk, dependencies, or permissions.

Use the subset needed to choose; keep implementation detail expandable or deferred until selection. Rank by judgment and explain the important tradeoff. Avoid fabricated impact scores or numeric confidence. Include holding steady or stopping when that is genuinely the best choice, without allowing one waiting experiment to block independent opportunities.

Quantify honestly

Use dated baselines and explicit units, denominators, and comparable windows. Separate attribution from causation and measured outcomes from proxies. Missing coverage is unavailable, not zero.

Where helpful, size the opportunity with simple scenario or break-even math. Show the formula, assumptions, and sensitivity; use ranges when justified. A scenario is not a forecast, and a chosen threshold is not an industry benchmark. Do not invent data to make the answer feel quantitative.

Choose the presentation for the decision

There is no mandatory heading hierarchy, tag stack, nested Why bullet, or repeated recommendation section.

  • Small decision: a conversational recommendation and a few distinct options in chat.
  • Several comparable options: a compact table or opportunity cards showing the dimensions that change the choice.
  • Substantial exploration: an interactive decision workspace when selection, scenarios, or evidence drill-down materially improve the decision. Use available visualization skills for actual artifacts, not decorative dashboards.

Lead with the insight and recommended direction. Keep the overview scannable, usually three to five strong opportunities rather than an exhaustive backlog. Put detailed evidence, caveats, and implementation notes behind disclosure when the surface supports it.

Useful interactions include selecting or combining ideas, expanding evidence, filtering by effort or goal, and changing scenario assumptions. Recompute scenario outputs transparently and label them as estimates. Implement real controls when creating an artifact; do not imply Markdown labels are working buttons. Check rendered controls and responsive behavior. If an artifact is not warranted or tools are unavailable, offer the equivalent conversation in plain text.

Show full SKILL.md (342 more words)Show less

Make the conversation composable

Assign short stable IDs when offering multiple selectable options: A/B/C, or 1A/2A only when grouping genuinely helps. Pair an ID with its short title when referring back. Preserve IDs and selected/rejected/deferred state across follow-ups; do not silently remap an existing ID. Keep continuity in the conversation or authorized task artifact, not unsolicited memory writes.

Make useful follow-ups natural: "research B," "combine A+C," "make C bolder," or "show the cheaper version." Offer one relevant invitation, not a generic questionnaire. Ask only a question whose answer materially changes the direction; otherwise give a recommendation with stated assumptions.

Selection means assemble or pursue the selected scope as requested. It does not automatically authorize spending, publishing, changing access, or other consequential external effects. Do not hide mutations behind interactive controls or treat a brainstorming request as execution approval.

Turn choices into parallel work

Show which moves are independent, which are alternatives, and which have a real prerequisite. A lightweight statement such as "A gathers evidence while B produces the draft; C waits for the result" often suffices. Use a dependency visual only when the relationships are hard to follow in prose.

Recommend a small complementary portfolio without repeating every option verbatim. Identify the output of each track and the decision where results reconverge. Parallelizable does not mean automatically spawning agents: delegate only when authorized and when independent ownership and expected time savings justify it.

On execution follow-ups, carry forward the selected options and approvals, take the first safe in-scope steps, and report outputs and remaining decisions rather than generating another menu.

Final quality check

Before answering, ask:

  • Is there an insight or opportunity here beyond verify, monitor, and wait?
  • Did evidence shape the choices, or did I just attach numbers to generic advice?
  • Can the user tell what each choice produces and combine independent work?
  • Does the presentation help a decision without duplicating text or adding ceremony?
  • Are uncertainty, permissions, and the distinction between proposed and completed work clear?

Richness means better thinking and useful interaction, not more words or more agents.

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

Files

SKILL.md and 1 other file in next-steps of swyxio/skills.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 038ef34

Compare with similar skills

Next Steps 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.

Next Steps compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Next Steps this skillswyxio/skills175—~1.9kAutomated safety check: PassMIT
Modeling Conversion MetricsPostHog/posthog40k—~1.4kAutomated safety check: PassCustom licence
Os Step By Stepkharmanskyi/open-steps1.3k—~1.9kAutomated safety check: PassMIT
Structured Step By Step Reasoningaiming-lab/MetaClaw3.5k—~212Automated safety check: PassMIT
Cost Conversationruvnet/ruflo74k—~407Automated safety check: NotesMIT
Conversation Memorydavila7/claude-code-templates32k4 repos~440Automated safety check: PassMIT

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Questions about Next Steps

What does Next Steps do?

Turn an ongoing task or conversation into evidence-informed opportunities and an actionable decision workspace. Next Steps is an agent skill from swyxio/skills. Turn an ongoing task or conversation into evidence-informed opportunities and an actionable decision workspace.

When should I use Next Steps?

Next Steps fits situations like: the user asks what next; invokes $next-steps; wants to explore; prioritize possible directions.

How do I install Next Steps in Claude Code?

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

How do I install Next Steps in Codex?

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

Can I use Next Steps 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 swyxio/skills --skill next-steps -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/next-steps, .gemini/skills/next-steps, .github/skills/next-steps and .opencode/skills/next-steps in your project.

What does Next Steps need to run?

SKILL.md names no scripts, command-line tools or credentials: Next Steps is instructions for the agent only.

Does Next Steps 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 Next Steps 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 Next Steps use?

Next Steps is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Next Steps use?

About 1.9k tokens (SKILL.md is roughly 7.5k 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 Next Steps?

Skills that share tags, products or a category with Next Steps: Modeling Conversion Metrics (PostHog/posthog, 40k stars), Os Step By Step (kharmanskyi/open-steps, 1.3k stars), Structured Step By Step Reasoning (aiming-lab/MetaClaw, 3.5k stars) and Cost Conversation (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Next Steps?

swyxio (a GitHub user) maintains it in swyxio/skills, which has 175 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 5, 2026.

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