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Ccf Experiment Designer

CommunityPopular
mikubaka88
ccf-experiment-designer

Design CCF experiment protocols and evidence schemas: datasets, baselines, metrics, ablations, and result-table contents. Use for 设计实验, 消融, benchmark planning, and 结果表证据结构. Preserve real values. Table styling/rendering belongs to ccf-visual-composer; broad retrieval belongs to ccf-literature-searcher.

Overview

Publishermikubaka88
RepositoryCCFA-Skills
Skill nameccf-experiment-designer
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 Experiment Designer 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-experiment-designer .claude/skills/ccf-experiment-designer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccf Experiment Designer 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 Experiment Designer 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 Experiment Designer 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 Experiment Designer

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

Before specialist execution, read and apply ccf-humanization first, then ccf-common. At every handoff, reuse their applicable active rules or refresh missing/changed ones. Both preflights are required even without prose; detailed editing, experiment, and maintenance modes run only when relevant.

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 Humanization and Common before all experiment work, including raw protocol planning and evidence schemas. When producing publication prose/tables/captions or changing executable experiments, load ../ccf-humanization/references/experiment-discipline.md as applicable, minimize smoke tests to unique changed critical paths, and verify complete method configurations for reported comparisons. These detailed checks are conditional; the family baseline is not. Describe the method and scientifically relevant configuration without exposing internal approval status. Keep unresolved version decisions outside publication artifacts without hiding material facts.

Core Rule

Design the smallest sufficient experiment package that distinguishes the central hypothesis from plausible alternatives. Use supplied specifications for planned methods; verify complete configurations for reported full-method comparisons. Build result tables and evidence-bound figure specs only from supplied real values or explicit placeholders. Never fabricate numbers, improvements, significance, benchmark ranks, or user-study outcomes. Do not expand protocols with repetitive smoke tests or implausible defensive cases. Publication-grade layout, palette, caption placement, and render QA belong to ccf-visual-composer. Follow the user's requested output shape: experiment plan, table, LaTeX table, figure spec, ablation list, or execution queue.

Modes

  • design: datasets, baselines, metrics, ablations, robustness, efficiency, failure analysis, and execution priority.
  • result-template: fill-in tables with TBD placeholders.
  • result-presentation: result tables, figure evidence plans, chart specs, caption facts, and missing-value markers from supplied real results.

Workflow

  1. Identify the requested output after both family preflights. Raw protocol planning and evidence schemas use Humanization's baseline without a manuscript rewrite. Select detailed prose/experiment checks only when applicable, and establish claims and available evidence before method-version checks.
  2. Extract the storyline from the idea or draft. Reuse the supplied claim/mechanism description. Read ../ccf-paper-writer/references/storyline-blueprint.md only when the central claim needs clarification, not for an already specified result table.
  3. Map every major claim to sufficient evidence, dataset/workload, confirmed baseline, metric, and mechanism-relevant ablation. Add robustness or failure tests only when observed, plausible, claim-relevant, or venue-required; do not enumerate remote defensive cases.
  4. Resolve missing dataset, baseline, metric, or protocol provenance through ccf-literature-searcher before fixing dependent comparisons. Verify compatibility with the central claim. For a consequential unresolved claim-to-test mismatch, request a focused ccf-paper-reviewer check and integrate its findings; do not create a full review report for a protocol question. Mark unavailable evidence instead of guessing.
  5. Load references/evidence-design.md for substantive protocol design or references/result-templates.md for table/schema work. Do not load both for a small task unless both are needed.
  6. For result presentation, preserve units, seeds, confidence intervals, dataset names, metric direction, and confirmed method version/configuration. Mark missing values explicitly; never fill them with simplified runs.
  7. If executable experiment code is actually changed, retain only non-duplicative smoke tests for those critical paths. Planning or formatting alone does not call for smoke tests. Keep them outside publication evidence and do not use them as substitutes for full experiments.
  8. Use ccf-visual-composer when the requested deliverable includes visual composition, layout, or rendering. Supply real values, units, uncertainty, metric direction, and caption facts; integrate and check the returned figure/table. A raw evidence schema does not require rendering.
  9. Before finalizing reported comparisons, reconcile claims, numbers, and configurations; use ccf-integrity-auditor for material unresolved conflicts. Use ccf-paper-writer for needed manuscript prose and ccf-submission-checker when package readiness is in scope. These are conditional contributions, not stages to run for every plan.

Adaptive Output Contract

Return the requested artifact first. For a result table request, output the table. For a figure request, output the evidence-bound figure spec and caption facts, then name ccf-visual-composer as next owner for visual composition when needed. For a full experiment-design request, use this default structure:

text
Mode:
Venue and assumptions:
Claim-evidence matrix:
Dataset / benchmark needs:
Confirmed method / baseline versions:
Baseline matrix:
Main experiments:
Ablations:
Robustness / failure / efficiency:
Smoke scope and deduplication:
Result tables or figure specs:
Missing values:
Execution priority:
No-fabrication status:
Next CCFA owner:

References

  • references/evidence-design.md: experiment and benchmark design.
  • references/result-templates.md: fill-in result tables and presentation scaffolds.
  • ../ccf-humanization/references/experiment-discipline.md: confirmed full method gate, simplified-version prohibition, smoke-test scope, and experiment-to-paper checks.
  • ../ccf-humanization/references/humanization-policy.md: warning-only, non-injection, and defensive-case removal policy.

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 Experiment Designer AI skill do?

Design CCF experiment protocols and evidence schemas: datasets, baselines, metrics, ablations, and result-table contents. Use for 设计实验, 消融, benchmark planning, and 结果表证据结构. Preserve real values. Table styling/rendering belongs to ccf-visual-composer; broad retrieval belongs to ccf-literature-searcher.

Why use Ccf Experiment Designer on TypingMind?

Because you install it once and use it with any model. Ccf Experiment Designer 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 Experiment Designer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikubaka88/CCFA-Skills/tree/main/ccf-experiment-designer. 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 Experiment Designer?

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 Experiment Designer?

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

Is the Ccf Experiment Designer 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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