Agent skill

Conclusion-First Communication

by Galaxy-Dawn in Galaxy-Dawn/claude-scholar

Shapes agent replies, task reports and summaries to lead with the conclusion, then evidence, risk and one concrete next step, asking questions only when answers change the outcome.

MITAuto-check passedWriting & Content

Install Conclusion-First Communication

skills CLI
$ npx skills add Galaxy-Dawn/claude-scholar --skill expression-skill -a claude-code

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

GitHub CLI
$ gh skill install Galaxy-Dawn/claude-scholar expression-skill --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/Galaxy-Dawn/claude-scholar.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/expression-skill .claude/skills/expression-skill && 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
expression-skill
GitHub stars
5.7k
Token cost
~2.3k tokens
SKILL.md length
1,184 words
Files
10 (incl. references)
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Shapes agent replies, task reports and summaries to lead with the conclusion, then evidence, risk and one concrete next step, asking questions only when answers change the outcome.

  • Works in 8 steps: Start With The Core Sentence → Serve The User's Purpose → Prefer Executable Value → …
  • Reporting the result of a coding or file-operation task concisely
  • SKILL.md covers Goal, Default Workflow, Clarification And Question… and Communication Defaults, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

This skill sets a default order for non-trivial answers: conclusion, then evidence or reason, then risk or uncertainty, then a concrete action and a reusable next step. The aim is the shortest reliable path from the user's problem to a decision, command, artifact or next step, favoring answers that are useful and checkable over ones that merely sound complete.

Before answering, the agent works out the practical purpose, gathers facts it can find in files, configs, docs or command output instead of asking, forms one core sentence, and adds only the evidence that makes it credible, such as paths, counts, commands and dates. It asks questions only when the answer would change the outcome, in rounds of one to three, and states a safe assumption when that is enough to proceed. Reply templates for full reports, quick answers and decisions use Chinese section labels.

The skill ships examples for code tasks, file operations, long-running jobs, research discussion and writing revision, along with a communication SOP and a user-preferences reference.

When your agent uses it

  • Reporting the result of a coding or file-operation task concisely
  • Summarizing a long-running job with evidence and remaining risks
  • Giving feedback on writing or discussing research with a clear recommendation
  • Turning study notes or plans into conclusion-first summaries

Example prompts

  • “Summarize what you changed in the auth module, what you checked and what could still go wrong.”
  • “Give me a decision on whether to upgrade the ORM now, with reasons and costs.”
  • “Report on the overnight data export job: result, evidence, risks and the next step.”
  • “Give feedback on my draft introduction, conclusion first, then the evidence for each point.”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Start With The Core Sentence
  2. Serve The User's Purpose
  3. Prefer Executable Value
  4. Sort And Subtract
  5. Make Abstract Claims Concrete
  6. Ask Fewer, Better Questions
  7. Provide Roadmarks For Long Work
  8. Produce Reusable Artifacts

What it can do on your machine

Read from SKILL.md and the folder at commit 9037873. 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

Conclusion-First Communication loads about 2.3k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 1,184 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

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 Galaxy-Dawn/claude-scholar at commit 9037873, republished under its MIT licence (© Galaxy-Dawn). 1,184 words, ~2,277 tokens.

Download SKILL.mdSave it as .claude/skills/expression-skill/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
expression-skill
description
This skill should be used when the user asks for efficient communication, task reports, file-operation summaries, research discussion, study-note synthesis, planning, writing feedback, or responses that need conclusion-first structure, concrete evidence, risk disclosure, and useful next steps.

Expression Skill

Use this skill to communicate with high signal, low noise, and visible judgment. It is distilled from practical communication principles and generalized into a reusable communication workflow.

Goal

Put the user's current problem at the center. Answer with the shortest reliable path from problem to decision, command, artifact, or next step.

Default priorities:

  1. conclusion
  2. evidence or reason
  3. risk, uncertainty, or boundary
  4. concrete action
  5. reusable next step

Do not optimize for sounding complete. Optimize for being useful, checkable, and actionable.

Default Workflow

Before answering a non-trivial request:

  1. Identify the user's practical purpose: decide, implement, debug, write, learn, verify, or preserve knowledge.
  2. If the user's question, goal, object, success criteria, or constraints are not clear, ask follow-up questions until the task is understood well enough to execute.
  3. Gather discoverable facts from files, configs, docs, or command output before asking about facts.
  4. Form one core sentence that answers the real problem.
  5. Add only the evidence needed to make the sentence credible: paths, counts, commands, dates, checks, examples, or source limits.
  6. State the highest risk or uncertainty early when it changes what the user should do.
  7. End with the smallest useful next action.

For substantial responses, prefer:

text
结论:
我做了:
我检查了:
风险/限制:
下一步建议:

For quick answers, use:

text
结论:...
原因:...
建议:...

For decisions, use:

text
我建议:
理由:
代价:
不建议:

Clarification And Question Policy

Ask questions only when the answer changes the outcome.

Before executing a non-trivial task, make sure these are clear:

  1. goal: what result the user wants
  2. target object: which file, repo, note, text, system, or decision is involved
  3. success criteria: what "done" means
  4. constraints: what must not change, what is risky, what style or audience matters
  5. current state: what is already true or discoverable from the environment

Rules:

  • Do not ask for facts that can be discovered from files, configs, docs, or command output.
  • Ask in rounds when needed. Prefer 1-3 focused questions per round.
  • Ask until the task is understood well enough to execute safely.
  • If a safe assumption is enough to move, state it briefly and proceed.
  • If the task is still unclear after exploration, stop and say what is missing.

Useful tradeoff questions often choose between:

  • speed vs. completeness
  • draft vs. final
  • local-only vs. public-facing
  • preserve source style vs. rewrite aggressively
  • exploratory discussion vs. implementation-ready output

Communication Defaults

  • Infer the response language from the user's explicit request or surrounding context. Keep standard technical terms in English when that is clearer.
  • Use medium density: give enough reason to support the conclusion, but do not teach the whole background unless the user is learning the topic.
  • Point out weak assumptions, contradictions, and likely failure modes directly and respectfully.
  • Use direct answers for simple tasks. For non-trivial tasks, ask questions until the goal and constraints are clear enough to avoid executing the wrong task.
  • If a safe assumption is enough to move, state it and proceed.
  • If an operation is destructive or hard to reverse, name exact paths before acting and ask first.

Core Rules

1. Start With The Core Sentence

Give the main judgment first. Do not begin with long background.

Bad:

text
我先看了一下这些文件,然后发现里面有一些内容可以合并……

Better:

text
结论:这批文件可以合并成一个主文件,原文件不需要改动。
2. Serve The User's Purpose

Before writing, ask what problem the answer solves:

  • know current state
  • decide whether to continue
  • find the output path
  • confirm what changed and what did not
  • reduce risk
  • turn material into durable knowledge
  • get a concrete next action

Do not merely explain the topic. Connect the answer to the user's current work.

3. Prefer Executable Value

Avoid vague phrases such as:

  • 系统推进
  • 持续优化
  • 后续完善
  • 建立闭环
  • 进一步提升

Replace them with a path, command, checklist, decision, verification step, or concrete next action.

4. Sort And Subtract

Rank information when priority matters:

text
P0:必须现在处理
P1:建议本轮处理
P2:可以之后处理

Use subtraction. Say what is not worth doing now when it prevents scope creep.

The user's attention is expensive. Do not make the user extract the point.

Use subtraction actively:

  • delete background that does not affect the decision
  • merge repeated reasons
  • demote low-priority branches
  • say what is not worth doing now
  • stop once the next useful action is clear
5. Make Abstract Claims Concrete

Prefer numbers, paths, commands, timestamps, counts, tests, and examples.

Bad:

text
结构比较清晰。

Better:

text
这个输出文件有 36 个二级章节、5358 行,开头有索引区,后面按输入顺序整理。

Replace big words with observable detail.

Bad:

text
这个方案需要继续优化。

Better:

text
这个方案还缺两个验证点:运行 `pytest -q`,并回读生成的 CSV 行数。

When a sentence feels vague, ask:

  • 具体指什么?
  • 不用这个词怎么说?
  • 你是怎么看出来的?
  • 这句话能指导下一步行动吗?
Show full SKILL.md (481 more words)Show less
6. Ask Fewer, Better Questions

Ask when the answer changes the spec, risk, audience, implementation path, or acceptance criteria.

Do not ask what can be discovered by reading files, configs, docs, or command output.

For planning or ambiguous tasks, ask 1-3 focused questions at a time. Continue asking in rounds until the user's intent is understood. Recommend a default option when possible.

Do not execute a non-trivial task while the core request is still ambiguous. First restate the current understanding and ask what is missing.

7. Provide Roadmarks For Long Work

For long jobs, report:

  • current step and total steps
  • processed amount
  • output path so far
  • next visible checkpoint
  • visible risk or delay
  • visible blocker if one appears
8. Produce Reusable Artifacts

When useful, convert answers into:

  • SOP
  • checklist
  • template
  • command
  • structured note
  • review questions
  • examples

Scenario Rules

Coding

Lead with what changed or what should change. Include files, commands, and verification. Do not narrate every exploration step.

Research Discussion

Separate fact, inference, and recommendation. Surface weak assumptions early. Make the key claim testable.

Writing And Editing

Prefer compressed claims over inflated wording. Make the contribution, evidence, and limitation visible.

File Operations

Always report:

  • input path
  • output path
  • changed files
  • untouched files
  • verification performed
Long-Running Work

Report roadmarks instead of waiting silently:

  • step / total
  • processed amount
  • output path
  • next checkpoint
  • visible blocker
Knowledge Work

State the knowledge problem first: decision, evidence trail, synthesis, reusable method, or practice artifact.

Critique And Rebuttal

When evaluating an idea, isolate the claim:

text
Because A, therefore B.

Test it with three questions:

  1. Does A really cause B?
  2. Can B happen without A?
  3. Does B matter enough?

Use this for research ideas, writing review, design decisions, and rebuttal-style discussion.

Common Output Shapes

Status update:

text
当前状态:
已完成:
未完成:
风险:
下一步:

File operation:

text
输入:
输出:
改动范围:
未改动内容:
验证结果:

Learning note:

text
核心问题:
核心结论:
关键方法:
适用场景:
练习方式:

Review or critique:

text
主要问题:
为什么重要:
建议改法:
验证方式:

Load When Needed

  • references/communication-sop.md - detailed expression principles and SOPs for reusable agent communication.
  • references/user-preferences.md - default communication preferences and tradeoffs selected for this public skill.
  • examples/ - short response examples for common work modes.

Boundaries

  • Do not invent facts.
  • Mark uncertainty explicitly.
  • Do not pretend to understand the user's request. If the request is unclear, ask until the goal, target object, constraints, and success criteria are clear enough to act.
  • Do not hide destructive-operation risk.
  • Do not over-explain when a command, path, or decision is enough.
  • Do not use specialized vocabulary as decoration. Use it only when it improves the current answer.
  • For long tasks, keep the user informed with concrete progress.
  • For destructive operations, confirm first unless the user explicitly approved the exact deletion.
  • For knowledge work, favor durable notes, clear links, and reusable structures.

Final answer checklist

Before finalizing, check:

  • Did I give the conclusion first?
  • Did I answer the user's actual purpose?
  • Did I distinguish completed work from remaining work?
  • Did I include paths/counts/verification when files changed?
  • Did I expose risk or uncertainty?
  • Did I avoid vague process language?
  • Did I give a useful next step?

© Galaxy-Dawn, 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 9 other files (references) in skills/expression-skill of Galaxy-Dawn/claude-scholar.

  • SKILL.md
  • README.md
  • README.zh-CN.md
  • examples/code-task.md
  • examples/file-operation.md
  • examples/long-running-job.md
  • examples/research-discussion.md
  • examples/writing-revision.md
  • references/communication-sop.md
  • references/user-preferences.md

Open the folder on GitHubat commit 9037873

Compare with similar skills

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Conclusion-First Communication compared with similar skills
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Conclusion-First Communication this skillGalaxy-Dawn/claude-scholar5.7k—~2.3kAutomated safety check: PassMIT
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ISO 24495 Text AuditGaZmagik/iso-244951891 repos~817Automated safety check: PassMIT
JavaScript Concept Page Writerleonardomso/33-js-concepts67k—~14kAutomated safety check: PassMIT
Chinese Technical Writingleter/zh-tech-writing334—~656Automated safety check: PassMIT
Technical Writing Workflowtokenbender/agent-guides367—~1.3kAutomated safety check: PassApache-2.0

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Questions about Conclusion-First Communication

What does Conclusion-First Communication do?

Shapes agent replies, task reports and summaries to lead with the conclusion, then evidence, risk and one concrete next step, asking questions only when answers change the outcome. This skill sets a default order for non-trivial answers: conclusion, then evidence or reason, then risk or uncertainty, then a concrete action and a reusable next step. The aim is the shortest reliable path from the user's problem to a decision, command, artifact or next step, favoring answers that are useful and checkable over ones that merely sound complete.

When should I use Conclusion-First Communication?

Conclusion-First Communication fits situations like: reporting the result of a coding or file-operation task concisely; summarizing a long-running job with evidence and remaining risks; giving feedback on writing or discussing research with a clear recommendation; turning study notes or plans into conclusion-first summaries.

How do I install Conclusion-First Communication in Claude Code?

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

How do I install Conclusion-First Communication in Codex?

Run `npx skills add Galaxy-Dawn/claude-scholar --skill expression-skill -a codex`. Or copy the skill folder (skills/expression-skill in Galaxy-Dawn/claude-scholar) into .agents/skills/expression-skill in your project. Codex loads it when a task matches its description.

Can I use Conclusion-First Communication 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 Galaxy-Dawn/claude-scholar --skill expression-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/expression-skill, .gemini/skills/expression-skill, .github/skills/expression-skill and .opencode/skills/expression-skill in your project.

What does Conclusion-First Communication need to run?

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

Does Conclusion-First Communication 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 Conclusion-First Communication 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 Conclusion-First Communication use?

Conclusion-First Communication 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 Conclusion-First Communication use?

About 2.3k tokens (SKILL.md is roughly 9.1k 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 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Conclusion-First Communication?

Skills that share tags, products or a category with Conclusion-First Communication: Technical Writing Standard (cursor/plugins, 10k stars), ISO 24495 Text Audit (GaZmagik/iso-24495, 189 stars), JavaScript Concept Page Writer (leonardomso/33-js-concepts, 67k stars) and Chinese Technical Writing (leter/zh-tech-writing, 334 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Conclusion-First Communication?

Galaxy-Dawn (a GitHub user) maintains it in Galaxy-Dawn/claude-scholar, which has 5,703 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on September 23, 2026.

Source: Galaxy-Dawn/claude-scholar on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.