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Ccf Humanization

CommunityPopular
mikubaka88
ccf-humanization

Required first preflight before every CCFA skill, including research, review, retrieval, experiments, visuals, and maintenance. Keep reasoning and communication direct, remove empty defensive framing, and preserve evidence and uncertainty. Also use for 去防御性 and 论文人性化. Apply prose edits only within the authorized task; specialist skills retain ownership.

Overview

Publishermikubaka88
RepositoryCCFA-Skills
Skill nameccf-humanization
Stars
2.6K
Forks
116
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mikubaka88 on GitHub. Read the source before you install it.

Installation

Install the Ccf Humanization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mikubaka88/CCFA-Skills.git /tmp/CCFA-Skills
mkdir -p .claude/skills
cp -r /tmp/CCFA-Skills/ccf-humanization .claude/skills/ccf-humanization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccf Humanization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ccf Humanization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ccf Humanization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CCF Humanization

Family File Contract

Before writing, resolve the canonical output and one stable working directory per task/artifact. Reuse explicit or established task paths; otherwise use project-root ccfa-workfiles/<purpose>/<artifact-id>/, with source/, assets/, cache/, and build/ only as needed. Update current files in place; do not scatter intermediates or create iteration copies. Preserve inputs and required evidence; clean only verified disposable files created by this task. Use UTF-8 text I/O and check Chinese text after saving or rendering. For file work, apply artifact-contracts.md and reuse the same paths across skill transitions.

Collaboration Contract

This is the first required family preflight. Apply the baseline below, then activate ccf-common before specialist work. The two preflights bootstrap once without recursively re-entering each other; reuse applicable rules across contributors.

Keep one integrating owner and actively use other skills to resolve missing prerequisites or check material findings. Reuse applicable evidence; do not skip necessary groundwork to save tokens. Before finalizing, integrate contributions and verify affected results. Follow the conditional cooperation routes; avoid unrelated stages and duplicate reports.

Invocation Controls

CCFA Handoff Mode: PARTIAL (Recommended). Follow metadata.ccf_skill_controls.handoff_question_mode, ../ccf-common/references/handoff-modes.md, and ../ccf-common/references/task-modes.md.

Activate this first for every CCFA task and contributor, including planning, retrieval, review, auditing, rendering, scaffolding, and maintenance. Apply the family baseline below before ccf-common and specialist execution. Reuse an already loaded, applicable baseline at handoffs; refresh changed or lost rules. Prose editing and experiment checks depend on the actual task, but baseline activation is universal. Specialist skills retain ownership and explicit user/host constraints still apply.

Family Baseline

Read and apply this entry's baseline before specialist work. Communicate the concrete task, evidence, and decisions directly; avoid imagined objections, empty assurances, repetitive warnings, and unnecessary process narration. Preserve real risks, critical review findings, uncertainty, scientific facts, source quotations, and mandatory checks. Do not turn critical assessment into praise or change scores to sound less defensive. Review-only tasks diagnose without rewriting; non-prose tasks apply these rules without inventing a prose-edit pass. Enable only relevant detailed modes below. This preflight creates no report, warning file, or separate agent by default.

Core Rule

Write the scientific argument directly: problem, insight, mechanism, evidence, and supported implication. Remove authorial self-defense, imagined reviewer objections, apologetic novelty positioning, denial-led statements, empty assurances, stacked hedging, repeated scope disclaimers, and obligatory cautionary endings. Replace a defensive sentence with its scientific payload or delete it if it adds none.

Humanization preserves rigor: retain meaningful uncertainty, actual assumptions, negative results, protocol facts, citations, equations, numbers, terminology, and required disclosures. Do not turn suggests into proves, omit a known failure, or hide a real comparison limitation. Do not treat isolated words such as only, not, or may as errors.

Keep method confirmation and version-gate status internal. Describe the actual method and relevant configuration naturally. Use supplied specifications for method drafting, cited evidence for prior work, and verified full configurations for reported comparisons; do not require new experiments just to edit supported prose.

Modes

  • family-preflight: always apply the baseline before any CCFA specialist; no manuscript or experiment artifact is required.
  • manuscript-humanization: revise defensive prose while preserving scientific content and source format.
  • experiment-humanization: apply the same prose standard to final experiment descriptions and tables; preserve full-method comparisons and labeled ablations.
  • warning-only: identify a concrete unresolved scientific decision without modifying its dependent artifact.

Workflow

For family-preflight, apply the baseline, ensure ccf-common is active, and continue the requested specialist task. The following editing workflow applies only when the task includes relevant prose or experiment artifacts; do not run it merely to complete the universal preflight.

  1. Identify the requested artifact, existing authorization, and whether the input is prose, a proposed design, or reported results. Read references/humanization-policy.md for the sentence decisions and bilingual repair examples.
  2. Recover each paragraph's scientific message. State the observation, operation, assumption, or inference directly. Delete empty self-defense instead of moving it into a warning block.
  3. Remove repeated caveats across sections. Express scope where it changes interpretation; do not require every abstract, paragraph, caption, or conclusion to end with a limitation.
  4. Preserve material facts and calibrated uncertainty in the authorized edit. If a decision requires new evidence or a research-scope change, isolate that decision and continue unaffected work. Do not edit a source file merely to encode a warning.
  5. For actual publication comparisons or executable experiment changes, load references/experiment-discipline.md. Preserve a verified identity and full configuration in the internal gate; keep legitimate ablations clearly labeled. Run this step only when the task needs it.
  6. Apply the existing punctuation and terminology preferences after the scientific argument is sound. For full sections or papers, run ../ccf-paper-writer/scripts/check_prose_quality.py when available and inspect its candidate locations in context. Do not rewrite correct scientific language merely to clear a heuristic warning.
  7. Read the final prose once. Check that rhetorical caution has not been replaced by hype, factual omission, or a different scientific claim. Rerun a check only for changed text or an unresolved finding.
  8. Return the requested artifact in its original format. When acting as a sidecar, return ownership to the content skill; do not append a process report to ordinary prose.

Warning Contract

Use only for a concrete decision that cannot be resolved from supplied evidence and existing authorization:

text
CCF Humanization Warning: not inserted into artifacts
Affected claim / file / experiment:
Evidence gap and material consequence:
Decision needed:
Materiality: advisory / blocking
File changes made for this warning: none

blocking applies to the dependent claim or change, not the entire task. Style choices, already documented limitations, and accurate local claim narrowing are ordinary authorized edits.

References

  • references/humanization-policy.md: direct scientific voice, sentence/paragraph repair, bilingual examples, material facts, warning-only decisions, punctuation, and checksum policy.
  • references/experiment-discipline.md: full-method comparisons, supplied specifications, ablations, and proportionate smoke checks.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ccf Humanization AI skill do?

Required first preflight before every CCFA skill, including research, review, retrieval, experiments, visuals, and maintenance. Keep reasoning and communication direct, remove empty defensive framing, and preserve evidence and uncertainty. Also use for 去防御性 and 论文人性化. Apply prose edits only within the authorized task; specialist skills retain ownership.

Why use Ccf Humanization on TypingMind?

Because you install it once and use it with any model. Ccf Humanization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ccf Humanization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikubaka88/CCFA-Skills/tree/main/ccf-humanization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ccf Humanization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ccf Humanization?

As many as you like. As long as a model supports skills, you can use Ccf Humanization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ccf Humanization AI skill free?

Yes. It is published on GitHub by mikubaka88 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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