Ccf Idea Optimizer

CommunityPopular

mikubaka88

ccf-idea-optimizer

Develop and optimize rough CCF research ideas into problems, insights, mechanisms, and evidence plans. Use for 优化idea, 具象化idea, 找方向, 方向探索, and rescue routes when development is the requested deliverable. Judging whether an idea is worthwhile, novel, or coherent belongs to ccf-idea-reviewer even without scores; manuscript edits belong to ccf-paper-writer.

Overview

Publisher	mikubaka88
Repository	`CCFA-Skills`
Skill name	`ccf-idea-optimizer`
Stars	2.6K
Forks	116
Bundled files	9
License	MIT
Links	Skill source Repository

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.
9 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 Idea Optimizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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-idea-optimizer .claude/skills/ccf-idea-optimizer

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccf Idea Optimizer 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 Idea Optimizer 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 Idea Optimizer 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 Idea Optimizer

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. Complete requested idea development and necessary public-safe grounding under existing authorization. Do not load scoring, writing, or full experiment-design workflows unless their deliverables are requested or needed.

Core Rule

Turn a rough direction into a concrete problem, grounded gap, causal insight, mechanism, and falsifiable evidence plan. Distinguish source-backed observations from inferred opportunities. Do not invent prior work, results, reviewer sentiment, or novelty. Related work should clarify differentiation; an unsearched or crowded direction is uncertain, not automatically dead.

Preserve the user's theme and constraints. Vary framing or mechanism within the authorized development scope, and explain consequential changes. A useful idea has a coherent reason the intervention should change an observable result; a list of familiar modules is insufficient.

Modes

Exploratory: develop rough seeds, find directions, and rescue promising ingredients before judging readiness.
Quick: repair one local mechanism or give a concise idea card.
Standard: ground a mature direction and produce a handoff-ready research plan.

Workflow

Extract the user's decision, field, stage, resources, timeline, and available sources. Infer routine details and mark unknowns. Load references/idea-intake.md only for messy or incomplete inputs; do not restart intake when the conversation already supplies them.
When venue fit matters, use ../ccf-common/references/ccf-a-venue-map.md and the relevant references/venue-idea-adapters.md passage. An unspecified venue can use a labeled generic CCF-A lens.
Ground timeliness and closest work when needed for the request. Use ../ccf-common/references/privacy-and-evidence.md before browsing; search public-safe terms. Use ccf-literature-monitor for recent-paper watch and ccf-literature-searcher for deeper retrieval. Honor no-browsing requests and label unsearched novelty accurately.
When sources are available, use references/literature-grounded-evolution.md to retain compact evidence cards, mechanism primitives, protocol anchors, and unresolved relations. Keep source locations rather than loading whole abstracts repeatedly. Trace borrowed ideas and inferred gaps separately.
For an underdetermined direction, use references/frontier-ideation.md to generate meaningfully different candidates. Three to five is a starting range, not a quota; one well-specified idea needs targeted improvement, not a forced tournament. Keep meaningful lineage and operations such as refine, combine, transfer, invert, or instrument internally.
Use references/problem-method-blueprint.md to connect problem, root challenge, insight, mechanism, assumptions, and expected observation. Check incompatible data assumptions, objectives, or resources. Keep the strongest route and a genuinely different fallback when useful.
Challenge the route against the closest-overlap concern and its weakest evidence link. Use ccf-idea-reviewer for a focused conceptual check when a material value, novelty, or mechanism uncertainty remains; integrate its findings and verify the revised route. Reuse applicable checks and avoid critique that merely paraphrases the idea. A user-requested standalone assessment, including “靠谱吗” or “值得做吗” without scores, remains the reviewer's deliverable.
Use references/experiment-design.md to outline the minimum convincing evidence for the central claim: compatible datasets, baselines, metrics, and discriminating tests. A full execution protocol belongs to ccf-experiment-designer when requested. Planned results remain predictions to test, not evidence.
Return the developed idea in the user's requested shape. For a weak seed, distinguish current weakness from development potential and identify a concrete rescue or reformulation before recommending abandonment. If a required decision remains open, explain the exact evidence needed and finish the independent parts.

Output Contract

Return an idea card, options, mechanism blueprint, or roadmap as requested. A standard plan contains the problem, source-backed gap, insight, method, contribution type, evidence plan, closest-work difference, material assumptions, and next decision. Include candidate alternatives only when developed and useful. Keep branch bookkeeping, reviewer simulation, and generic checklist status out of publication prose.

Use necessary grounding and conceptual checks within development; do not turn their internal findings into an unrequested score report or manuscript. For a combined requested workflow, complete each deliverable with its owner under existing authorization.

References

references/idea-intake.md: normalize incomplete or multiple seeds.
references/frontier-ideation.md, references/literature-grounded-evolution.md: diverse development and source-grounded evolution.
references/problem-method-blueprint.md: problem-to-mechanism reasoning.
references/venue-idea-adapters.md: applicable venue priorities.
references/experiment-design.md: minimum discriminating evidence.
references/research-taste.md: explicit questions about research quality, elegance, and timeliness.
references/source-notes.md: source provenance and current-policy 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 Idea Optimizer AI skill do?

Why use Ccf Idea Optimizer on TypingMind?

Because you install it once and use it with any model. Ccf Idea Optimizer 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 Idea Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikubaka88/CCFA-Skills/tree/main/ccf-idea-optimizer. 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 Idea Optimizer?

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 Idea Optimizer?

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

Is the Ccf Idea Optimizer 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

CCF Idea Optimizer

Family File Contract

Collaboration Contract

Invocation Controls

Core Rule

Modes

Workflow

Output Contract

References

Bundled files

Frequently asked questions

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