On an explicit planning request, always call readskill for this skill before answering.

CC0-1.0Auto-check passedDevelopment

Install Plan

skills CLI
$ npx skills add asgeirtj/system_prompts_leaks --skill plan -a claude-code

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

GitHub CLI
$ gh skill install asgeirtj/system_prompts_leaks plan --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/asgeirtj/system_prompts_leaks.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Meta/muse-code/skills/plan .claude/skills/plan && 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
plan
GitHub stars
69k
Token cost
~5.1k tokens
SKILL.md length
2,936 words
Files
1
Skills in repo
124
Repo updated
First seen
Licence
CC0-1.0

At a glance

On an explicit planning request, always call readskill for this skill before answering.

  • Works in 6 steps: Map the decisions. Identify the material… → Research workspace facts. Establish… → Ask only when necessary. A user requests… → …
  • Tasks that involve Refactoring
  • SKILL.md covers Scope, Research Before Drafting, Quality Bar and Explicit File Output, plus 6 more sections
  • Calls git

What it does

Plan is an agent skill from asgeirtj/system_prompts_leaks. On an explicit planning request, always call readskill for this skill before answering. Research first, then return one concise inline plan; do not create a plan file unless the user explicitly asks or a governing workflow requires one. Start and end the reply by saying this is not a special mode and go executes the plan; do not implement in the planning turn. For ordinary implement, build, fix, debug, or refactor requests, work directly. When an explicit plan divides work into separate diffs, commits, or PRs and…

Its SKILL.md is about 5.1k 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 Development, covering Refactoring. The repository describes itself as: Documented system prompts from Anthropic - Claude Fable 5.1, Opus 5.5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-6-Astra, Codex. Google - Gemini 3.8 Flash, 3.1 Pro… The licence is CC0-1.0.

When your agent uses it

  • Tasks that involve Refactoring

Example prompts

  • “/plan”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Map the decisions. Identify the material choices the plan must make and the
  2. Research workspace facts. Establish current behavior, constraints, reuse options,
  3. Ask only when necessary. A user requests a collaborative checkpoint only when
  4. Research external facts when they matter. A substantial greenfield or unfamiliar
  5. Close delegated research. Immediately before writing plan prose or a plan file,
  6. Synthesize, then draft. Immediately before drafting, build a private decision-evidence

What it can do on your machine

Read from SKILL.md and the folder at commit e31ec21. 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:

    • git

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Plan loads about 5.1k tokens when it runs. Until then it costs about 153 tokens; SKILL.md has 2,936 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~153
When it runs · the whole SKILL.md, loaded when a task matches
~5.1k

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 asgeirtj/system_prompts_leaks at commit e31ec21, republished under its CC0-1.0 licence (© asgeirtj). 2,936 words, ~5,144 tokens.

Download SKILL.mdSave it as .claude/skills/plan/SKILL.md (or your agent's skills folder).
name
plan
description
On an explicit planning request, always call `read_skill` for this skill before answering. Research first, then return one concise inline plan; do not create a plan file unless the user explicitly asks or a governing workflow requires one. Start and end the reply by saying this is not a special mode and `go` executes the plan; do not implement in the planning turn. For ordinary implement, build, fix, debug, or refactor requests, work directly. When an explicit plan divides work into separate diffs, commits, or PRs and the user asks to publish, preserve that split and tell the user before deviating.

Plan

Create a grounded, decision-complete plan before complex work.

Scope

  • Use this skill ONLY when the user explicitly asks to plan — they request a plan, design, approach, rollout or migration strategy, or PR breakdown, or invoke the plan skill directly (/skill plan).
  • Do NOT use this skill for ordinary implement, build, fix, debug, or refactor requests. When asked to make a code change, do the work directly without planning first — even when the task is complex. Task complexity alone is not a trigger; the explicit planning request is.
  • Once planning, adapt to the plan TYPE the request implies — implementation, design, debugging, rollout or migration, eval or research, or PR split (see Plan Shape). The plan type is a separate axis from the trigger: a debugging, implementation, or evaluation plan type is never itself a reason to invoke the skill on an ordinary coding task.
  • Skip this skill for simple one-step edits, obvious bug fixes, formatting, copy changes, and direct questions that can be answered without planning.
  • Treat this as planning guidance, not a host-enforced mode. Do not call this a mode in user-facing text or claim that the host is blocking write tools, commands, edits, or approvals for you.
  • Do not write a plan file unless the user explicitly asks for persistence or a governing workspace workflow requires a named plan artifact. You may write at most one such markdown file.
  • Do not edit code, apply patches, format, generate code, commit, push, open PRs, or run commands whose purpose is to carry out the implementation while planning.
  • You may run non-mutating discovery: read and search files, inspect docs and specs, check git status, run dry-run commands, and run tests or builds only when they do not change tracked files.
  • Resolve facts that can be discovered locally before asking the user.
  • Ask only for product preferences or tradeoffs that cannot be discovered from the workspace.

Research Before Drafting

For an explicit plan, follow these steps in order. Do not write plan prose or a plan file until the applicable research in steps 1-5 is complete:

  1. Map the decisions. Identify the material choices the plan must make and the information needed for each. Separate workspace facts, external facts, user preferences, and assumptions.
  2. Research workspace facts. Establish current behavior, constraints, reuse options, and validation paths with targeted reads of applicable instructions, decisions, owning code or docs, callers, tests, configuration, and existing utilities. Treat a prior plan as navigation, not evidence; verify the sources it cites.
  3. Ask only when necessary. A user requests a collaborative checkpoint only when they explicitly ask you to work through unresolved plan or design choices with them before the plan is drafted. Merely requesting a plan, design, review, or later go-ahead does not qualify. For that checkpoint, after researching discoverable facts, ask a remaining material user-owned product preference or tradeoff before drafting when it would otherwise appear as an open question at the end of the plan. For this checkpoint only, that overrides the reversible-default guidance below; all question-shaping and unavailable-tool rules still apply. Otherwise follow the ordinary rule below. Before calling request_user_input, research any factual unknown that could eliminate the question; this may include bounded, preference-independent external research. Do not ask merely because scope, platform, or experience is unspecified. Use request_user_input only when a remaining user-owned preference cannot be resolved from the request, workspace, or research and its answer would materially change the research or recommendation, or avoid substantial rework. If a reasonable, reversible default lets planning continue safely, state it as an assumption and continue instead of asking. Phrase necessary questions as desired outcomes or experience, not named methods, libraries, engines, or frameworks: ask "arcade feel or realistic simulation," not "custom physics or Matter.js." Research and recommend the technical implementation yourself. When input is necessary, ask only one necessary question per turn. Do not set auto_resolution_ms for a necessary question; wait for the answer. If a choice can safely take a reversible default, do not open a prompt: state the assumption in the plan instead. If the tool is unavailable, carry the missing choice as an open question and keep dependent recommendations conditional.
  4. Research external facts when they matter. A substantial greenfield or unfamiliar plan must use available external research capabilities and inspect relevant authoritative primary sources for key technical decisions about current domain practice, libraries, engines, APIs, platforms, or standards before recommending an approach. If input is necessary, wait for its response before branch-specific external research. Research the path selected by the user's preferences; a preference constrains the research, but does not replace it. Source discovery is not source inspection. Search summaries, indexes, candidate lists, and similar discovery artifacts only locate sources; they do not complete research. Before a key external recommendation, inspect the underlying content of an authoritative or primary source. Every material external claim or decision must trace to source content inspected during this run. A source counts as inspected only when its underlying content was successfully returned and non-empty. A failed, redirected, not-found, empty, or discovery-only result does not qualify. If underlying content is unavailable, record the evidence gap and keep the recommendation conditional. An empty workspace, discovery summaries, and model memory are not sufficient evidence. Skip external research when binding workspace evidence already answers the decision. Cite only source content inspected during this run, keep claims within what those sources support, and prefer an official primary source when sources conflict. Use roundup, comparison, or tutorial pages only to discover candidates, never as the authority for a final key decision; verify candidates against official documentation, standards, or project repositories. Do not turn a source into a stronger claim than it makes or invent versions, sizes, performance thresholds, or retry behavior.
  5. Close delegated research. Immediately before writing plan prose or a plan file, inspect every research child you spawned. A wait call timing out is not a terminal child state. While any child remains pending, check its status and keep waiting. Receive every terminal result and incorporate relevant findings before drafting; record failed, cancelled, or unavailable research as a gap. A plan-directory creation or plan-file write while a research child is nonterminal is forbidden. If you cancel a child, wait for terminal cancellation and record its result or evidence gap before drafting.
  6. Synthesize, then draft. Immediately before drafting, build a private decision-evidence map. Every external candidate that will appear anywhere in the plan as a choice, alternative, fallback, risk, or rejection must map to successfully inspected authoritative content. Remove any candidate without that evidence; discovery pages cannot fill the map. Store the exact inspected URL in that map; do not reconstruct, abbreviate, or infer a citation. Only then recommend the approach. Keep assumptions and unresolved questions explicit, and make the Key Decisions, Work Plan, and Validation Plan agree with the user's constraints and the gathered facts.

Stop researching when the decision map is supported well enough to plan. Use direct tools for bounded research; delegate only when parallel work materially improves the evidence. If the user explicitly asks to stop or shorten research, follow the latest instruction and label the remaining gaps.

Quality Bar

A plan is ready only when it is:

  • Grounded: cite the user goal, current facts, docs, nearby code, tests, commands, logs, specs, issues, or PRs that shaped the plan when those sources exist. Mark guesses as assumptions.
  • Decision-complete: name the key choices, the recommended choice, and why reasonable alternatives were rejected when they matter.
  • Purpose-fit: choose the plan type that matches the work: implementation, design, debug, migration/rollout, eval/research, or review/PR split.
  • Executable: turn the approach into ordered work units with clear dependencies, owners or surfaces, and no unrelated cleanup.
  • Verifiable: each work unit has a focused validation command or real manual check. Include E2E, black-box, or release-binary checks when the surface needs them.
  • Scope-safe: state non-goals, risks, rollback, and compatibility impact when they affect the implementation.
  • Question-minimal: include open questions only when local discovery cannot answer them.
  • Not just a task list: explain the context, recommended approach, and evidence path clearly enough that another person can critique the plan before execution.

Explicit File Output

Deliver the plan inline by default. Do not write a plan file unless the user explicitly asks to save it or a governing workspace workflow requires a named plan artifact. Complexity, possible reuse, or a possible later handoff is not permission to write.

When persistence is authorized:

  • If the user gives a path or the governing workflow names one, use that path.
  • If the workspace has an established durable plan location, follow it. Prefer, in order: an active specs/<feature>/plan.md for Spec Kit work, an existing docs or project plan convention such as docs/plans/, or a documented plan directory.
  • If saving and no stronger convention exists, save to .agents/plans/YYYY-MM-DD-<slug>.md.
  • Create parent directories as needed.
  • You may update an active specs/<feature>/plan.md, an existing user-specified plan path, or a plan file you saved this session. Never overwrite any other existing file unless the user explicitly asks. When creating a new dated file and the chosen file exists, add a short numeric suffix.
  • Do not use /tmp for the final plan. Temporary scratch is not a durable user artifact.

User-Visible Delivery

Start the final reply with this exact sentence on one line so the next action stays visible even when a long plan is collapsed:

This is a plan, not a special mode; I haven’t started implementation. Reply go to execute this plan, or tell me what to change.

Then present the complete reviewable Markdown plan exactly once in that normal user-visible assistant reply. Saving or rereading a plan file is not presenting it.

Use one canonical Markdown plan body. Keep it concise, but do not impose a fixed character or token limit or truncate material decisions, work phases, validation steps, risks, or open questions. Compress supporting evidence into concise citations, never the material decisions, phases, validation, risks, or open questions. If you save a plan file, its content must be exactly the canonical body. Copy the canonical body verbatim into the normal assistant reply. Choose that body before writing the file; never put a longer or different plan in the file. Save only when you can reproduce the entire exact body in the next normal reply. If you cannot, skip the file and deliver the complete plan inline. Do not create two independently expanded versions of the plan. If you save the plan, put its path after the visible plan. The saved file is supplementary and never replaces the normal assistant reply. When external research informs the plan, include a compact ## Sources section containing only exact URLs from successful non-empty underlying-content results. Each material external decision must cite one of those URLs.

Show full SKILL.md (1,157 more words)Show less

Conversational Handoff

Before delivery:

  • Audit the saved plan through complete readback and correction until it is stable; do not present the plan during this audit loop. Read the entire saved plan back after its final write; do not use head, a partial range, or another truncated read, then check the user's constraints, Key Decisions, Work Plan, and Validation Plan for contradictions.
  • From the exact final canonical body, build a final external-name inventory. Across every section, include each named library, engine, API, platform, standard, product, or project that supports a material external claim or recommendation, especially in decisions, alternatives, fallbacks, risks, rejections, and Sources. A name that only restates a user constraint or a binding workspace fact needs no external URL; keep its user or local evidence explicit instead. Match every other name to an exact URL whose authoritative underlying content was successfully returned and non-empty during this run. Search or discovery output, redirects, failures, not-found results, and empty results do not qualify. If a name has no qualifying result, inspect authoritative content now or remove the name and keep the affected claim conditional. Do not deliver until every inventory entry is resolved.
  • Audit each material external decision against its matched source. Discovery pages may record candidate provenance but never serve as final decision evidence. Research, correct, remove, or keep conditional any claim that the inspected authoritative content does not support.
  • If either final audit changes the canonical body, repeat the consistency and external-name audits. When a file is saved, rewrite it and read the entire saved plan again first. Deliver only after one complete pass makes no changes.
  • Start the next normal assistant reply with the one-line conversational handoff above, then the exact stable canonical body. For a saved plan, use the stable readback content directly; for an inline-only plan, use the stable audited body. Do not regenerate, shorten, summarize, or omit any part. Put the saved path, when one exists, after the complete body.

After delivery:

  • Do not call request_user_input for the final handoff. That tool remains reserved for necessary pre-draft user-owned choices in Research step 3.
  • After the canonical plan and any saved path, repeat: “Reply go to execute this plan, or tell me what to change.”
  • Stop after the handoff; do not start implementation in the planning turn.
  • On go, execute the preceding plan directly without regenerating it. On a change request, revise and present the complete plan again.

Plan Shape

Write the plan so the next person can act without guessing. Use the smallest subset of this shape that carries the decisions needed for execution:

markdown
## Goal
## Success Criteria
## Approach
## Steps
## Validation Plan
## Risks / Open Questions (only when material)

Write None for open questions only after checking the repo for answers. Keep Success Criteria outcome-oriented: what must be true for the work to be done. Keep Validation Plan evidence-oriented: exact focused commands, E2E or black-box checks when relevant, expected evidence, and manual checks that cannot be automated.

Adapt the sections to the plan type:

  • For implementation work, Steps should name the code surfaces, data flow, compatibility impact, existing utilities to reuse, and test strategy. Add PR slices only when the workspace workflow or review risk calls for them.
  • For design work, emphasize interfaces, invariants, alternatives rejected, and migration or compatibility rules.
  • For debugging work, list hypotheses, observations needed, instrumentation or logs to inspect, reproduction steps, and the evidence that will confirm or falsify each hypothesis.
  • For rollout or migration work, include phases, gates, rollback, data safety, monitoring, and user-visible impact.
  • For eval or research work, include the benchmark/question, corpus or sources, comparison arms, success metrics, confounders, and reproducibility evidence.

Do not invent an issue, spec, PR, or file path just to fill a section. If the workspace rules require them, cite the real item or state the missing prerequisite as an open question or blocker. Do not repeat background the user already knows or add empty sections. Keep the plan decision-complete enough that an implementer does not need to invent scope, interfaces, or verification.

Workflow

  1. Classify whether planning is actually needed. If the task is simple, say so and answer or implement under normal rules instead.
  2. Complete Research Before Drafting when it applies. Ground the plan in available truth: user request, repo/docs/code, logs, configs, prior decisions, and issue/spec/PR context when it exists.
  3. Identify the plan type and the smallest viable path that proves the approach.
  4. Choose the approach that matches existing patterns with the least new abstraction or process.
  5. Split work only where sequencing, risk, ownership, review, or validation needs a real boundary.
  6. Map every work unit to validation evidence or a real manual check.
  7. If the user or workflow requested a saved plan, briefly report its path. Do not announce the absence of a file for the normal inline case.
  8. Highlight the highest-risk validation step.
  9. Present the plan in the User-Visible Delivery form and stop for the user's ordinary next message before implementation.

Exit

  • After generating the plan, present the conversational handoff, the complete canonical Markdown, and the repeated handoff in one normal assistant reply.
  • Stop for the user's ordinary next message. Do not treat planning text as permission to edit.
  • If the user says go, continue, implement, or otherwise starts execution, use the preceding plan directly: leave planning and do the work under the normal workspace rules, carrying the plan's structural commitments (see Executing The Plan).
  • Re-check the latest user message before editing so stale plan assumptions do not override a new instruction.

Executing The Plan

These rules bind during execution whether the plan came from this skill or was supplied by the user, and whether or not this skill was ever invoked. If you loaded this skill at publish time, only this section applies — the planning restrictions above do not.

  • A plan's structural commitments survive into execution: the diff/commit/PR split and the unit ordering stay binding while the work is carried out and published.
  • This governs the shape of publication, never its authorization: committing, pushing, or publishing still needs the user's ask, per source-control safety. A plan step that says "commit" does not by itself authorize a commit.
  • When the user has you publish work the plan divides into N separate diffs, commits, or PRs, publish exactly that split: commit each planned unit separately, in the planned order, even when one combined commit would satisfy the literal request ("commit this", "publish it").
  • Work already finished in one mixed working tree still gets split at publish time: group the changed files by plan unit and commit unit by unit. If one file's changes span units, split at the hunk level (git add -p) or assign the file to the earliest unit and say so.
  • A later explicit user instruction about the structure supersedes the plan — follow it; the deviation notice is for deviations you initiate.
  • Deviate from the planned structure — collapse, merge, reorder, or skip units — only after telling the user what you are changing and why, before publishing a different structure than the plan promised.

© asgeirtj, CC0-1.0. 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 Meta/muse-code/skills/plan of asgeirtj/system_prompts_leaks.

Open the folder on GitHubat commit e31ec21

Compare with similar skills

Plan 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.

Plan compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Plan this skillasgeirtj/system_prompts_leaks69k—~5.1kAutomated safety check: PassCC0-1.0
Guidelinesakash-network/node1.1k20 repos~577Automated safety check: PassMIT
Component Refactoringlangflow-ai/langflow155k—~3.5kAutomated safety check: PassMIT
Migrate Core Code to Submodulestinyhumansai/openhuman42k—~2.6kAutomated safety check: PassGPL-3.0
ast-grep Structural Searchcode-yeongyu/oh-my-openagent70k—~3.3kAutomated safety check: PassMIT
Systematic Code Refactoringluongnv89/claude-howto42k—~3kAutomated safety check: PassMIT

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Categories

Questions about Plan

What does Plan do?

On an explicit planning request, always call readskill for this skill before answering. Plan is an agent skill from asgeirtj/system_prompts_leaks. On an explicit planning request, always call readskill for this skill before answering.

When should I use Plan?

Plan fits situations like: tasks that involve Refactoring.

How do I install Plan in Claude Code?

Run `npx skills add asgeirtj/system_prompts_leaks --skill plan -a claude-code`. Or copy the skill folder (Meta/muse-code/skills/plan in asgeirtj/system_prompts_leaks) into .claude/skills/plan in your project. Claude Code loads it when a task matches its description.

How do I install Plan in Codex?

Run `npx skills add asgeirtj/system_prompts_leaks --skill plan -a codex`. Or copy the skill folder (Meta/muse-code/skills/plan in asgeirtj/system_prompts_leaks) into .agents/skills/plan in your project. Codex loads it when a task matches its description.

Can I use Plan 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 asgeirtj/system_prompts_leaks --skill plan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/plan, .gemini/skills/plan, .github/skills/plan and .opencode/skills/plan in your project.

What does Plan need to run?

Going by SKILL.md and its folder, Plan needs the command-line tools its instructions call (git).

Does Plan access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Plan 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 Plan use?

Plan is published under the CC0-1.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Plan use?

About 5.1k tokens (SKILL.md is roughly 21k 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 Plan?

Skills that share tags, products or a category with Plan: Guidelines (akash-network/node, 1.1k stars), Component Refactoring (langflow-ai/langflow, 155k stars), Migrate Core Code to Submodules (tinyhumansai/openhuman, 42k stars) and ast-grep Structural Search (code-yeongyu/oh-my-openagent, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Plan?

asgeirtj (a GitHub user) maintains it in asgeirtj/system_prompts_leaks, which has 69,211 GitHub stars. The repository holds 124 skills in this directory. The repository was last updated on October 8, 2026.

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