Plan Writing
xenitV1/Antigravity-Workflows
Structured task planning with clear breakdowns, dependencies, and verification criteria.
Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/.
$ npx skills add dzhng/skills --skill write-spec -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install dzhng/skills write-spec --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/dzhng/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/engineering/write-spec .claude/skills/write-spec && 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 "write-spec" agent skill from https://github.com/dzhng/skills/tree/main/skills/engineering/write-spec into .claude/skills/write-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "write-spec", 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/dzhng/skills/tree/main/skills/engineering/write-specType 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 dzhng/skills --skill write-spec -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install dzhng/skills write-spec --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhng/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/engineering/write-spec .agents/skills/write-spec && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "write-spec" agent skill from https://github.com/dzhng/skills/tree/main/skills/engineering/write-spec into .agents/skills/write-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "write-spec", 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 dzhng/skills --skill write-spec -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install dzhng/skills write-spec --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhng/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/engineering/write-spec .cursor/skills/write-spec && 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 "write-spec" agent skill from https://github.com/dzhng/skills/tree/main/skills/engineering/write-spec into .cursor/skills/write-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "write-spec", 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/dzhng/skills.git --path skills/engineering/write-spec--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 dzhng/skills --skill write-spec -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install dzhng/skills write-spec --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhng/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/engineering/write-spec .gemini/skills/write-spec && 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 "write-spec" agent skill from https://github.com/dzhng/skills/tree/main/skills/engineering/write-spec into .gemini/skills/write-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "write-spec", 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 dzhng/skills write-specInstalls 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 dzhng/skills --skill write-spec -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/dzhng/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/engineering/write-spec .github/skills/write-spec && 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 "write-spec" agent skill from https://github.com/dzhng/skills/tree/main/skills/engineering/write-spec into .github/skills/write-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "write-spec", 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 dzhng/skills --skill write-spec -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install dzhng/skills write-spec --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhng/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/engineering/write-spec .opencode/skills/write-spec && 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 "write-spec" agent skill from https://github.com/dzhng/skills/tree/main/skills/engineering/write-spec into .opencode/skills/write-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "write-spec", 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.
write-specBreak large features into independently verifiable, human-reviewable slices under a feature directory in specs/.
Write Spec is an agent skill from dzhng/skills. Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/. Use for risky or multi-step feature work that needs upfront questioning, API seams, browser-playable checkpoints, HTML visualizations, screenshot gates, staged implementation plans, recursive fog-of-war reslicing, or proactive research into reference implementations/best practices before slicing. Pairs with your project's verification harness and screenshot gates (the browser checkpoints)…
Its SKILL.md is about 4.7k 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 Planning and Refactoring. The repository describes itself as: Reusable AI agent skills for software factories: explore ideas, write specs, implement, review, and run autonomous research. Works with Claude Code, Codex, and other… The licence is MIT.
10 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d513228. 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.
Write Spec loads about 4.7k tokens when it runs. Until then it costs about 220 tokens; SKILL.md has 2,559 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 dzhng/skills at commit d513228, republished under its MIT licence (© dzhng). 2,559 words, ~4,650 tokens.
.claude/skills/write-spec/SKILL.md (or your agent's skills folder).Turn a large feature into a ladder of small contracts. Each rung should be understandable to the human, testable by an agent, and useful before the whole feature is done.
Grill before planning. Ask one question at a time until you know the desired outcome, non-goals, review surface, sacred contracts, missing assets, and first useful playable checkpoint. Give your recommended answer with each question so the user can accept, reject, or edit it. Inspect the repo instead of asking questions the code can answer. Close the interview by asking whether the plan must carry backward compatibility or data migrations — the default is neither: hard cutovers, no compat shims, no migration scaffolding, no deploy-order dances. Only the user opting in puts them in the plan.
Slice at API seams. Each slice should behave like a tiny library where possible: named module boundary, typed inputs/outputs, deterministic fixtures, and tests at the seam. If a slice needs three unrelated systems booted before it can be checked, sharpen the seam.
Research the fog. When the feature depends on an unfamiliar domain, high-fidelity visual target, named reference, benchmark, external repo, library, or "how do people usually do this?" question, do targeted online research before finalizing slices. Prefer primary sources: official docs, source repos, papers, case studies, talks, and shipped examples. If a reference implementation exists, add a replication spike before translation or approximation.
Make progress visible. For visual or interactive work, every slice should produce something playable: a route, fixture page, harness, CLI probe, or HTML visualization the human can run, inspect, screenshot, and critique. Tests prove contracts; demos expose taste and intent.
Optimize feedback loops. Slice so the next useful question can be answered quickly. Prefer tiny runnable surfaces, hot-reloadable harnesses, sample fixtures, and self-contained workbenches over plans that require the whole feature to exist before anyone can learn from it. For asset-heavy work, plan an asset app/workbench where humans and artists can add samples, upload replacements, preview them live, and see validation failures fast.
Use the repo's natural shape. If the repo is a monorepo, plan apps and packages instead of forcing everything into the current app. Give each testable surface a first-class route or command; avoid piling new behavior behind opaque query flags when a small dedicated app would be clearer.
Do not block on missing inputs. If art, data, credentials, or external assets are missing, plan generated placeholders plus a replacement contract. The feature should advance with placeholders, while a separate handoff path explains exactly what the human or external partner must provide later.
Draft in parallel, then synthesize. For any multi-slice feature, don't trust one pass to find the right cut. Fan out a few independent drafts and merge the best into one plan (see the Workflow). Divergence is the point — so engineer it on two axes: give each draft a different bias (a lens it optimizes for) and, when more than one model family is available, a mix of models. Blind, differently-biased drafts surface slices, seams, and risks a lone plan misses — and where they independently agree, you know the cut is solid.
Recursively uncover fog of war. The first slice graph is a scouting pass, not proof the field is known. After drafting, inspect each high-risk slice as if it were its own feature. If it hides multiple variables, unknown external practice, unproven architecture, or "we'll figure it out during implementation," reslice that subset and repeat until every next slice has one question, one seam, one review surface, and one verdict.
One visual variable per slice. Visual slices fail when they ask one pass to match the final hero image. Split by the thing being judged: density, silhouette, colour, texture, lighting, fog, water placement, water material, label legibility, animation rhythm. Each slice gets a crop/mask and a verdict for that variable only. Whole-frame comparison belongs at compose/integration, after the variables have their own evidence.
Interview: keep asking until you can name the slices without hand-waving. Stop when remaining unknowns can safely be discovered by the first slice.
Research: inspect the repo and research unfamiliar external practice before drafting when the feature names a reference, library, technique, standard, visual target, or performance pattern. Capture the discovered source/repo/article/paper links in the spec and turn any exemplar into a reproduction spike before a porting slice. If prior experiments already established an accepted approach, apply From research to implementation below before drafting alternatives.
Draft in parallel: for a multi-slice feature, spawn at least three independent subagents to draft the whole plan — fresh context each, a git worktree apiece if they must run or build to validate, otherwise have them return the plan inline. Three is the floor, not the count: scale the pool with the feature's complexity, adding a drafter for each genuinely distinct approach or lens the problem supports. Give each the same brief from the interview and nothing else (never another draft) — but assign each a distinct bias so their divergence is structured, not accidental. The baseline trio:
Swap in or add lenses when the feature demands them (e.g. asset-pipeline bias, perf bias, migration-safety bias), but keep the biases orthogonal — grow the pool by adding a new lens, never by running the same lens twice. Also mix model families: if you're currently instructed to draft with codex, run at least one draft with claude — and vice versa — so the pool balances different models' blind spots, not just different prompts. Family means vendor (claude vs codex), not tier: every draft uses a state-of-the-art model; never diversify by dropping to a weaker tier of the same family. Each drafter: recon the real code and tests (measured facts, failed approaches, scope firewalls, greppable file/test names), then propose the slice graph, package/app boundaries, dependencies, API seams, playable deliverables, verification gates, and human review checkpoints. Skip the fan-out only for a genuinely single-slice problem.
Synthesize: read every draft and build the canonical plan yourself — don't anoint one. Take the strongest slicing, union the seams, risks, and firewalls each caught alone, and where drafts disagree pick the better-justified call and record the genuine alternative for the human. Where the drafts independently agree you're on firm ground; where they split is where to think hardest. When the feature has any visual surface, make screenshot-critique a standing verification gate in the README so every visual slice inherits it: the spec must tell the implementing agent to run an unbiased screenshot-critique as the last check on any visual shot before accepting it. Whenever a slice has something to compare its shot against — a prior look it changes, or a reference/inspiration image added for the feature — the spec must also name compare-screenshots as the gate that judges candidate-against-target: the telemetry and less-wrong verdict that screenshot-critique's single-shot eyes do not give.
Recursive fog audit: review the canonical graph slice by slice. For any slice with hidden variables, broad verbs ("make it realistic", "match the reference", "add the backend"), missing research, or more than one visual variable/API seam, run this same slicing logic on that slice as a sub-feature. Keep repeating until the next implementation slice can be accepted or rejected by one focused artifact. Record deferred variables as later slices, not prose inside the current slice. The exit test is the decision budget: a slice is fully specified when the implementing agent inherits decisions rather than making them — every freedom left open is either named as delegated in the slice file or the slice needs another pass.
Materialize: create specs/<feature>/ when the feature has more than
one slice or needs assets/visualizations.
Refactor-clean the plan: run refactor-clean over the materialized spec — the plan is architecture too, and it must describe the shape the codebase would want if designed today, not the old shape with the feature bolted on. Name each concept that should have one owner (projection, environment, data contract, renderer phase, state machine, test oracle) and confirm no slice introduces a parallel abstraction, duplicated concept, or compatibility layer that a later slice must delete. Any transitional scaffolding a slice genuinely needs must be named as a short-lived seam with an explicit removal condition and the slice that removes it — collapsed the instant its consumers migrate, never carried to the end by default. Encode the resulting single-owner invariants and the end-state ("reads as designed today, not tacked on") in the README so every implementing pass inherits them.
Scrollback audit: the conversation dies; the spec survives. Before calling the plan done, sweep the full conversation and every earlier planning artifact (maps, interview notes, drafts) for content that exists only there — decisions with their rationale, rejected alternatives with why they lost, mid-stream scope changes, user-supplied constraints and throwaway remarks that decided something. Each either lands in its owning spec file or is deliberately dropped; a scope change propagates to every spot that references it, not just where it landed. Then re-read each surviving pre-plan artifact the spec supersedes (a map's kickoff prompt, open-questions list, or proposed plan) and mark superseded sections with a pointer to the plan — stale instructions must not be able to misroute a fresh agent. Done when every conversation decision is findable in the spec and no surviving artifact contradicts the ladder.
Build slice by slice: leave each slice with a runnable artifact and verification before depending on it. Keep each artifact small enough to iterate on quickly. Keep the README's "Next Agent Prompt" written as the handoff text a future agent should read and follow.
Reslice when the work says so: if implementation hits a snag and the slice starts changing unrelated variables — or the choices ledger keeps filling from one slice — stop broadening the patch. Update the spec first: split the slice into smaller contracts, name the frozen inputs, move the extra visual variables to later slices, and rewrite the Next Agent Prompt to resume from the first new slice. Then continue. Reslicing is progress, not failure.
Freeze the accepted spike's runnable code, dependencies, configuration, inputs, prompts/skills, and evidence by immutable identity. Inspect what it actually does; option names and summaries are not proof of behavior.
Map proven behavior → evidence → owning slice → parity gate in the spec. Give every drafter this contract and the limits of the findings. Separate user-required differences from untested proposals; cleanup must not silently redesign the winner.
Plan an early production-entry-point comparison against the frozen reference with matched inputs and controlled responses. Check computed requests, decisions, and complete outputs; name permitted differences. Every preservation row needs an owner and a check. Run parity before expensive confirmation and retain the original quality gates.
Use specs/<feature>.md only for a small, single-slice problem. Large
features live in:
specs/<feature>/README.md — goal, context, slice graph, review map,
contracts, firewalls, known unknowns, and a "Next Agent Prompt" section with
the current status, next pickup point, global TODO checklist, and handoff
instructions for the next pass.specs/<feature>/slices/<NN>-<name>.md — one independently verifiable
slice per file.specs/<feature>/visualizations/*.html — roadmap diagrams, prototypes,
harness mockups, generated reports, contact sheets, or other
human-reviewable artifacts.specs/<feature>/assets/ — reference images, fixtures, captures, and other
inputs needed to judge the work.For visual work, keep feature-owned visual evidence in the spec folder: inspiration images, reference screenshots, archived baselines, comparison contact sheets, generated candidate captures, and critique artifacts. If those files start outside the spec folder, copy them into the spec folder when they become part of the feature's review context. Product snapshot folders may still hold the active regression baselines their harnesses own, but do not rely on those mutable outputs or external paths as the only record of what the feature was judged against.
Each slice file answers:
Every multi-slice spec README needs a "Next Agent Prompt" near the top. Write it in second person, as the prompt a future agent should read when they resume the feature. The README should not merely describe that it is live handoff state; the section itself must directly tell the next agent what to do next. It should include:
The point is that a fresh agent can open the README and know what to do next without reading the chat.
The feature plan is done when a fresh agent can start at slice 1 without the conversation, and the human can review the roadmap without reverse-engineering a wall of text.
Once the slices have all shipped, close-spec archives
the plan to specs/done/ and rewrites it from a build ladder into a durable
rationale record.
© dzhng, 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/engineering/write-spec of dzhng/skills.
Open the folder on GitHubat commit d513228
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in dzhng/skills, which our catalogue first saw on October 7, 2026.
Write Spec 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 |
|---|---|---|---|---|---|---|
| Write Spec this skilldzhng/skills | 1k | — | ~4.7k | Automated safety check: Pass | MIT | |
| Plan WritingxenitV1/Antigravity-Workflows | 130 | 8 repos | ~994 | Automated safety check: Pass | MIT | |
| Workflowbrianlovin/agent-config | 376 | 1 repos | ~687 | Automated safety check: Pass | None | |
| Evanflow Improve Architectureevanklem/evanflow | 418 | — | ~950 | Automated safety check: Pass | Custom licence | |
| Improvefossasia/eventyay-interpretation | 1.6k | 10 repos | ~3.7k | Automated safety check: Warn | MIT | |
| Claude Code Clawdbotwin4r/claude-code-clawdbot-skill | 123 | — | ~2.5k | Automated safety check: Warn | None |
xenitV1/Antigravity-Workflows
Structured task planning with clear breakdowns, dependencies, and verification criteria.
brianlovin/agent-config
Workflow orchestration for complex coding tasks. An agent skill from brianlovin/agent-config.
evanklem/evanflow
Identify refactoring opportunities by surfacing architectural friction.
fossasia/eventyay-interpretation
Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute.
win4r/claude-code-clawdbot-skill
Run Claude Code (Anthropic) from this host via the claude CLI (Agent SDK) in headless mode (-p) for codebase analysis, refactors, test fixing, and structured output.
Tommy-yw/RunbookHermes
Structured decision-making framework for technical proposals and trade-off analysis.
dzhng/skills
Compare screenshots against the intended design, distinguishing approved references from historical baselines.
dzhng/skills
Use Claude Code as an independent claude -p subagent when the user explicitly asks for Claude, wants a second-agent opinion from Claude, or asks to delegate a well-scoped task to Claude.
dzhng/skills
Refactor cleanly instead of layering sediment. An agent skill from dzhng/skills.
dzhng/skills
Create or revise agent skills. An agent skill from dzhng/skills.
dzhng/skills
Use the local Codex CLI as an independent second agent. An agent skill from dzhng/skills.
dzhng/skills
Run [implement-spec](../implement-spec/SKILL.md) with Codex doing the implementation passes while you orchestrate, integrate, and review.
Categories
Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/. Write Spec is an agent skill from dzhng/skills. Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/.
Write Spec fits situations like: multi-step feature work that needs upfront questioning; browser-playable checkpoints; HTML visualizations; screenshot gates.
Run `npx skills add dzhng/skills --skill write-spec -a claude-code`. Or copy the skill folder (skills/engineering/write-spec in dzhng/skills) into .claude/skills/write-spec in your project. Claude Code loads it when a task matches its description.
Run `npx skills add dzhng/skills --skill write-spec -a codex`. Or copy the skill folder (skills/engineering/write-spec in dzhng/skills) into .agents/skills/write-spec 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 dzhng/skills --skill write-spec -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/write-spec, .gemini/skills/write-spec, .github/skills/write-spec and .opencode/skills/write-spec in your project.
SKILL.md names no scripts, command-line tools or credentials: Write Spec is instructions for the agent only.
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.
Write Spec is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.7k tokens (SKILL.md is roughly 19k 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 Write Spec: Plan Writing (xenitV1/Antigravity-Workflows, 130 stars), Workflow (brianlovin/agent-config, 376 stars), Evanflow Improve Architecture (evanklem/evanflow, 418 stars) and Improve (fossasia/eventyay-interpretation, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
dzhng (a GitHub user) maintains it in dzhng/skills, which has 1,016 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on October 5, 2026.
Source: dzhng/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.