A skill your agent uses when working on substantial writing artifacts where structure, audience fit, critique, revision, translation, style learning, or decision-backed preference learning matters…

Apache-2.0Auto-check passedWriting & Content

Install Writers Loop

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill writers-loop -a claude-code

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins writers-loop --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/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/xxsang/writers-loop/skills/writers-loop .claude/skills/writers-loop && 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
writers-loop
GitHub stars
1.2k
Token cost
~2.9k tokens
SKILL.md length
1,500 words
Files
16 (incl. scripts, references)
Skills in repo
736
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when working on substantial writing artifacts where structure, audience fit, critique, revision, translation, style learning, or decision-backed preference learning matters…

  • Works in 12 steps: Frame: Identify artifact type, audience,… → Question Gate: Ask only blocking… → Plan: Create the outline, argument, task… → …
  • Working on substantial writing artifacts where structure
  • SKILL.md covers Core Rule, When to Use, Core Loop and Entry Modes, plus 7 more sections
  • Runs JavaScript scripts from its folder; calls npm

What it does

Writers Loop is an agent skill from hashgraph-online/awesome-codex-plugins. Use when working on substantial writing artifacts where structure, audience fit, critique, revision, translation, style learning, or decision-backed preference learning matters; also when learning reusable writing style from the user's own writing or permitted reference samples.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/artifact-types.md` and `references/business-writing.md`).

It sits in Writing & Content, covering Brand voice and tone and Translation. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Working on substantial writing artifacts where structure
  • Decision-backed preference learning matters
  • Also when learning reusable writing style from the users own writing
  • Permitted reference samples

Example prompts

  • “/writers-loop”

Requirements

  • Node.js

Workflow steps

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

  1. Frame: Identify artifact type, audience, purpose, constraints, success criteria, and desired tone.
  2. Question Gate: Ask only blocking questions. If context is enough, state assumptions and continue.
  3. Plan: Create the outline, argument, task sequence, scene plan, or section structure before drafting.
  4. Plan Checkpoint: Stop and wait for the user to approve or request changes before drafting.
  5. Replan: If the plan is rejected, revise only the criticized parts, reissue the full plan, and ask again.
  6. Draft: Write from the approved plan. Preserve declared constraints.
  7. Critique: Evaluate against the artifact-specific rubric. Every critique must point to a specific location and name a concrete problem…
  8. Propose: Suggest targeted revisions with reason and expected improvement. Stop and wait for the user's decision on each.
  9. Decide: Record the user's apply, reject, or adjust response. Do not skip this step — preference learning depends on explicit decisions.
  10. Revise: Apply accepted changes. Keep rejected changes out.
  11. Evaluate: Check the revised artifact against the original success criteria.
  12. Learn: Extract preference signals from decisions collected above. Record the signal type, applies-to scope, and evidence.

What it can do on your machine

Read from SKILL.md and the folder at commit 16b4156. 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

    Ships 2 files in scripts/ (JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • npm

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

  • Network

    No URLs in SKILL.md. Its commands use npm, 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

Writers Loop loads about 2.9k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 73 tokens; SKILL.md has 1,500 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~18k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 16b4156, republished under its Apache-2.0 licence (© hashgraph-online). 1,500 words, ~2,933 tokens.

Download SKILL.mdSave it as .claude/skills/writers-loop/SKILL.md (or your agent's skills folder). This skill also uses 15 other files; get the full folder from GitHub.
name
writers-loop
description
Use when working on substantial writing artifacts where structure, audience fit, critique, revision, translation, style learning, or decision-backed preference learning matters; also when learning reusable writing style from the user's own writing or permitted reference samples.

Writer's Loop

Core Rule

Extract preferences only from explicit user decisions:

  • Approved or rejected plans
  • Accepted, rejected, or undone edits
  • Manual rewrites that change style, voice, or structure (not fact corrections)
  • Explicit standing preferences declared by the user

Never extract preferences from unreviewed drafts, one-off comments, fact corrections, or current-task constraints. Learn from decisions, not raw drafts.

When to Use

Use for writing where quality depends on structure, audience fit, correctness, style, or iteration — typically 300+ words or any writing where feedback and refinement are expected:

  • Coding plans, implementation plans, and technical proposals
  • Business reports, research summaries, executive updates, and memos
  • Product specs, design docs, documentation, and tutorials
  • Fiction chapters, essays, scripts, speeches, and narrative outlines
  • Poetry (limited support — use custom rubric; see references/artifact-types.md)
  • Academic papers and whitepapers (limited support — adapt to venue requirements)
  • Brand-governed or legally constrained writing (legal, clinical, regulatory, corporate)
  • Style learning or style distillation from the user's own writing, permitted reference samples, files, pasted text, chapters, reports, docs, or codebase prose
  • Translation where meaning, source-language writing style, voice, rhythm, formatting, terminology, or cultural effect matters

Do not use for tiny one-off text edits unless the user asks for a reusable process or preference learning.

Core Loop

Follow the loop in order. Do not emit a later-stage section before its gate has passed:

  1. Frame: Identify artifact type, audience, purpose, constraints, success criteria, and desired tone.
  2. Question Gate: Ask only blocking questions. If context is enough, state assumptions and continue.
  3. Plan: Create the outline, argument, task sequence, scene plan, or section structure before drafting.
  4. Plan Checkpoint: Stop and wait for the user to approve or request changes before drafting. ⚠ Do not draft before approval. Exception: fast draft path (see Entry Modes).
  5. Replan: If the plan is rejected, revise only the criticized parts, reissue the full plan, and ask again.
  6. Draft: Write from the approved plan. Preserve declared constraints.
  7. Critique: Evaluate against the artifact-specific rubric. Every critique must point to a specific location and name a concrete problem — not a generic observation.
  8. Propose: Suggest targeted revisions with reason and expected improvement. Stop and wait for the user's decision on each. ⚠ Do not apply changes before the user responds.
  9. Decide: Record the user's apply, reject, or adjust response. Do not skip this step — preference learning depends on explicit decisions.
  10. Revise: Apply accepted changes. Keep rejected changes out.
  11. Evaluate: Check the revised artifact against the original success criteria.
  12. Learn: Extract preference signals from decisions collected above. Record the signal type, applies-to scope, and evidence.
  13. Distill: Promote repeated, high-confidence patterns into reusable rules.
  14. Reuse: Apply stable rules in later tasks, scoped to artifact type and stage.

Entry Modes

  • New artifact: start at Frame, then Question Gate, then Plan.
  • Fast draft (questions waived): Frame → state Assumptions → compact inline Plan → Draft → Learning Status. Use only when the user clearly waives questions and asks for an immediate draft. Do not use this path when the user asks to bypass, ignore, or override a known plan checkpoint; in that case, present the plan and stop at PLAN CHECKPOINT. Skipped questions are weak signals and will not become preferences.
  • Multi-agent: default to single-agent. Use multi-agent only for high-stakes, long, ambiguous, or multi-audience artifacts where independent planning or critique would materially improve quality. Read references/multi-agent.md first.
  • Existing draft: start at Frame, then Critique; plan only if structure is unclear. If source text is missing or placeholder-only, ask for it and use this wording: "preserve user intent, voice, plot facts, and continuity."
  • Targeted revision: start at Frame, then Propose; keep changes at sentence or paragraph level unless the user explicitly asked for section-scale changes.
  • Style learning: start at Frame, then read references/style-distillation.md. Output Frame, Style Versus Content, Style Pack, and Storage Decision. If the user wants to clone another person's style, confirm permission or keep the pack session-only.
  • Using a learned style: before asking artifact questions or drafting, load the style pack from the conversation or from .writers-loop/styles/ only when the user opted into local storage. State which pack is loaded. Draft with it, then critique content quality and style match separately. Do not copy source passages or facts from the style pack evidence. If the style pack is missing, unavailable, or the user says not to load it first, use the missing-style-pack template in references/style-distillation.md exactly, including Style Pack Status, Content Plan, and Style Application Plan. Do not draft and do not output QUESTION GATE or general artifact questions yet.
  • Translation: start at Frame, then read references/translation.md. Output Frame, Translation, Review, and Learning Status; preserve source formatting inside the Translation section, not by omitting the loop metadata.
  • Preference update: start at Learn. For storage-mode requests, confirm whether .writers-loop/, journal.jsonl, prefs.md, or style packs will be created before asking artifact questions. Treat tone, length, detail level, or storage mode set for the current task as a constraint, not a learned preference, unless the user says it applies in future work.

Planning Gates

Ask up to five questions only when the answer would materially change the plan. If the user skips questions, state working assumptions and continue. Skipped questions are weak or neutral signals, not strong preferences.

Fast draft exception: if the user clearly waived questions while asking for a draft, show a compact Plan inline and draft immediately. Do not pause for plan approval. End with Learning Status.

Checkpoint pressure override: if the user asks to bypass, ignore, or override a known plan checkpoint, the checkpoint still applies. Output a plan, explain that fast draft only waives blocking questions, then stop at PLAN CHECKPOINT.

After presenting a plan in the standard path, output the checkpoint format (see references/checkpoints.md) and wait. Do not draft until the user replies. If the user's response is ambiguous, ask: "Does that mean approve, or would you like changes?"

Show full SKILL.md (532 more words)Show less

Constraint vs. Preference

A constraint applies to one artifact ("for this memo, use short sentences"). A preference applies to future artifacts ("I always prefer short sentences in memos"). Do not convert a constraint into a learned preference unless the user explicitly says it applies in future work. When unsure, ask.

If the current request conflicts with an older preference, output Preference Handling: say that the current explicit instruction wins for this task, keep the older preference scoped, and do not archive it without repeated contradictory evidence.

Signal Rules

Extract preferences only from:

  • Approved plans (plan_approved)
  • Rejected plans with reasons (plan_revision_requested)
  • Accepted edits (proposal_applied)
  • Rejected edits (proposal_rejected)
  • Undone edits (proposal_undone)
  • Manual rewrites that change style, voice, or structure (manual_rewrite)
  • Explicit standing preferences (preference_declared)

Discard for preference learning:

  • Unreviewed drafts
  • Model-generated text that was never reviewed
  • One-off style comments or ambiguous praise
  • Fact, date, citation, or domain corrections
  • Current-task constraints ("this time", "for this memo", storage mode, tone, length, or detail level) unless the user says it is a standing preference

Distillation Rules

Promote a pattern into a reusable rule only when:

  • It repeats across at least two meaningful decisions, or the user explicitly declared it
  • It is specific enough to guide future output
  • It is not contradicted by more recent or stronger evidence

Format:

text
Rule: [short imperative instruction]
Applies to: [artifact-type/stage, e.g. coding-plan/planning or report/critique]
Evidence: [accepted/rejected/manual decision summary]
Confidence: low | medium | high

Apply only medium or high confidence rules without asking. Ask before applying low confidence rules. Do not promote a single current-task constraint into Learned Preferences.

Reference Loading

Load only the references needed for the current task. State which reference you are reading before using it. If the artifact type is not found in a reference, use the Universal Rubric and ask if it is sufficient — do not invent rules not in the file.

  • references/artifact-types.md: routing index for artifact-specific references
  • references/technical-writing.md: coding plans, technical docs, design docs, and product specs
  • references/business-writing.md: reports, memos, proposals, academic/whitepaper, and constrained writing
  • references/fiction-narrative.md: fiction, narrative, essays, speeches, and poetry
  • references/critique-rubrics.md: artifact-specific critique criteria
  • references/preference-signals.md: signal examples and weighting guidance
  • references/checkpoints.md: checkpoint formats for question, plan, proposal, and close
  • references/style-distillation.md: learning reusable style packs from samples
  • references/translation.md: translating while preserving source style
  • references/multi-agent.md: optional multi-agent workflow
  • references/preference-journal.md: optional durable journal format
  • references/validation-scenarios.md: pressure tests for validating this skill

Release validation includes tools/validate-skill.mjs through npm run validate.

Optional Tool

Use bundled scripts only when the user opts into durable local preferences or durable local style packs:

  • scripts/journal.mjs: initialize .writers-loop/, append decision events, and derive prefs.md.
  • scripts/style-pack.mjs: initialize .writers-loop/styles/, save reviewed style packs, list saved style packs, and show a selected style pack.

Common Mistakes

  • Drafting before framing audience and success criteria
  • Using multi-agent by default for ordinary tasks
  • Applying broad rewrites when targeted edits preserve intent better
  • Treating a single accepted edit as a permanent global rule
  • Converting "this time" or session-only storage into a durable preference
  • Reusing fiction style preferences in coding plans or reports without evidence
  • Loading a style pack without stating which one is loaded
  • Judging only content quality when a style-match review is also required
  • Treating a fact-correction rewrite as a style preference signal
  • Copying source passages or content facts into a reusable style pack
  • Flattening source-language voice or rhythm into generic target-language prose
  • Translating code, commands, file paths, URLs, or IDs

© hashgraph-online, Apache-2.0. 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 15 other files (scripts, references) in plugins/xxsang/writers-loop/skills/writers-loop of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • agents/openai.yaml
  • references/artifact-types.md
  • references/business-writing.md
  • references/checkpoints.md
  • references/critique-rubrics.md
  • references/fiction-narrative.md
  • references/multi-agent.md
  • references/preference-journal.md
  • references/preference-signals.md
  • references/style-distillation.md
  • references/technical-writing.md
  • references/translation.md
  • references/validation-scenarios.md
  • scripts/journal.mjs
  • scripts/style-pack.mjs

Open the folder on GitHubat commit 16b4156

Compare with similar skills

Writers Loop 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.

Writers Loop compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Writers Loop this skillhashgraph-online/awesome-codex-plugins1.2k—~2.9kAutomated safety check: PassApache-2.0
Translatorholaboss-ai/holaOS11k—~617Automated safety check: PassCustom licence
Multilingual Scoreindranilbanerjee/digital-marketing-pro8541 repos~3.2kAutomated safety check: PassMIT
Translate Contentindranilbanerjee/digital-marketing-pro8541 repos~2.8kAutomated safety check: PassMIT
Personal Chinese Writing Stylesugarforever/01coder-agent-skills136—~744Automated safety check: PassMIT
Jeg Writing Stylebrycewang-stanford/Awesome-Journal-Skills1.2k—~1.2kAutomated safety check: PassMIT

Similar skills

  • Translator

    holaboss-ai/holaOS

    Translate content across languages while preserving brand voice and cultural nuance.

    11k GitHub stars~617 tokensUpdated 1 mo ago
    Writing & ContentAuto-check passed
  • Multilingual Score

    indranilbanerjee/digital-marketing-pro

    Score translated or localized content into one composite quality verdict — technical translation scoring (length ratio, formatting, do-not-translate terms, placeholders), content quality in the…

    854 GitHub starsUsed in 1 repo~3.2k tokens
    Writing & ContentAuto-check passed
  • Translate Content

    indranilbanerjee/digital-marketing-pro

    Translate marketing content with automatic service routing per language pair, quality scoring across five dimensions (length ratio, formatting, key terms, placeholders, completeness), and a…

    854 GitHub starsUsed in 1 repo~2.8k tokens
    Writing & ContentAuto-check passed
  • Personal Chinese Writing Style

    sugarforever/01coder-agent-skills

    Apply the user's Chinese writing style when writing, translating, editing, proofreading, polishing, or publishing Chinese content, including blog posts, articles, subtitles, captions, tweets/X…

    136 GitHub stars~744 tokensUpdated 3 mo ago
    Writing & ContentAuto-check passed
  • Jeg Writing Style

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when polishing Journal of Economic Growth (JEG) prose — abstract, introduction, model exposition, empirical-result and magnitude-translation paragraphs, APA author-year…

    1.2k GitHub stars~1.2k tokensUpdated 10 days ago
    Writing & ContentAuto-check passed
  • Jpube Writing Style

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when polishing prose for a Journal of Public Economics (JPubE / JPubEc) manuscript — a 250-word abstract stating purpose, results, and policy conclusion; an intro that lands…

    1.2k GitHub stars~1.2k tokensUpdated 10 days ago
    Writing & ContentAuto-check passed

More from hashgraph-online/awesome-codex-plugins

All 736 skills in this repo
  • Anime Reaction Gif

    hashgraph-online/awesome-codex-plugins

    Create original anime-style reaction stickers as looping GIFs and MP4 previews, using generated character pose sheets and timed key poses.

    1.2k GitHub stars~922 tokensUpdated yesterday
    Auto-check passed
  • Calibredb

    hashgraph-online/awesome-codex-plugins

    Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).

    1.2k GitHub stars~1k tokensUpdated yesterday
    Auto-check passed
  • Rust API Test Harness

    hashgraph-online/awesome-codex-plugins

    A skill your agent uses when adding, changing, testing, or debugging Rust HTTP APIs and services, especially when Codex needs black-box integration tests, random-port app startup, real database test…

    1.2k GitHub stars~1.7k tokensUpdated yesterday
    Auto-check passed
  • Art

    hashgraph-online/awesome-codex-plugins

    Make a studio's game look like something at build time — a cover from a real frame of the game (free), painted covers, backdrops, textures and character plates from image models through the…

    1.2k GitHub stars~2.4k tokensUpdated yesterday
    Auto-check passed
  • Calle

    hashgraph-online/awesome-codex-plugins

    Use CALL-E from Codex through the calle CLI. An agent skill from hashgraph-online/awesome-codex-plugins.

    1.2k GitHub stars~2.9k tokensUpdated yesterday
    Auto-check passed
  • Game Balance Economy

    hashgraph-online/awesome-codex-plugins

    Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.

    1.2k GitHub stars~618 tokensUpdated yesterday
    Auto-check passed

Questions about Writers Loop

What does Writers Loop do?

A skill your agent uses when working on substantial writing artifacts where structure, audience fit, critique, revision, translation, style learning, or decision-backed preference learning matters…. Writers Loop is an agent skill from hashgraph-online/awesome-codex-plugins. Use when working on substantial writing artifacts where structure, audience fit, critique, revision, translation, style learning, or decision-backed preference learning matters; also when learning reusable writing style from the user's own writing or permitted reference samples.

When should I use Writers Loop?

Writers Loop fits situations like: working on substantial writing artifacts where structure; decision-backed preference learning matters; also when learning reusable writing style from the users own writing; permitted reference samples.

How do I install Writers Loop in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill writers-loop -a claude-code`. Or copy the skill folder (plugins/xxsang/writers-loop/skills/writers-loop in hashgraph-online/awesome-codex-plugins) into .claude/skills/writers-loop in your project. Claude Code loads it when a task matches its description.

How do I install Writers Loop in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill writers-loop -a codex`. Or copy the skill folder (plugins/xxsang/writers-loop/skills/writers-loop in hashgraph-online/awesome-codex-plugins) into .agents/skills/writers-loop in your project. Codex loads it when a task matches its description.

Can I use Writers Loop 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 hashgraph-online/awesome-codex-plugins --skill writers-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/writers-loop, .gemini/skills/writers-loop, .github/skills/writers-loop and .opencode/skills/writers-loop in your project.

What does Writers Loop need to run?

Going by SKILL.md and its folder, Writers Loop needs JavaScript for the scripts in its folder and the command-line tools its instructions call (npm). Our summary lists: Node.js.

Does Writers Loop access the network?

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

Is Writers Loop 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Writers Loop use?

Writers Loop is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Writers Loop use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 15k tokens, read only when the agent opens those files.

What are the alternatives to Writers Loop?

Skills that share tags, products or a category with Writers Loop: Translator (holaboss-ai/holaOS, 11k stars), Multilingual Score (indranilbanerjee/digital-marketing-pro, 854 stars), Translate Content (indranilbanerjee/digital-marketing-pro, 854 stars) and Personal Chinese Writing Style (sugarforever/01coder-agent-skills, 136 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Writers Loop?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,232 GitHub stars. The repository holds 736 skills in this directory. The repository was last updated on October 6, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.