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Agent skill

Ccf Experiment Designer

by mikubaka88 in mikubaka88/CCFA-Skills

Design CCF experiment protocols and evidence schemas: datasets, baselines, metrics, ablations, and result-table contents.

MITAuto-check passed

Install Ccf Experiment Designer

skills CLI
$ npx skills add mikubaka88/CCFA-Skills --skill ccf-experiment-designer -a claude-code

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

GitHub CLI
$ gh skill install mikubaka88/CCFA-Skills ccf-experiment-designer --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/mikubaka88/CCFA-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ccf-experiment-designer .claude/skills/ccf-experiment-designer && 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
ccf-experiment-designer
GitHub stars
3k
Token cost
~1.9k tokens
SKILL.md length
803 words
Files
4 (incl. references)
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Design CCF experiment protocols and evidence schemas: datasets, baselines, metrics, ablations, and result-table contents.

  • Works in 9 steps: Identify the requested output after both… → Extract the storyline from the idea or… → Map every major claim to sufficient… → …
  • Benchmark planning
  • SKILL.md covers Family File Contract, Collaboration Contract, Invocation Controls and Core Rule, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ccf Experiment Designer is an agent skill from mikubaka88/CCFA-Skills. 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.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `agents/openai.yaml`, `references/evidence-design.md` and `references/result-templates.md`).

The repository describes itself as: A skill family for shaping the research storyline of CCF-A papers. The licence is MIT.

When your agent uses it

  • Benchmark planning

Example prompts

  • “/ccf-experiment-designer”

Workflow steps

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

  1. Identify the requested output after both family preflights. Raw protocol planning and evidence schemas use Humanization's baseline without…
  2. Extract the storyline from the idea or draft. Reuse the supplied claim/mechanism description. Read…
  3. Map every major claim to sufficient evidence, dataset/workload, confirmed baseline, metric, and mechanism-relevant ablation. Add…
  4. Resolve missing dataset, baseline, metric, or protocol provenance through ccf-literature-searcher before fixing dependent comparisons…
  5. Load references/evidence-design.md for substantive protocol design or references/result-templates.md for table/schema work. Do not load…
  6. For result presentation, preserve units, seeds, confidence intervals, dataset names, metric direction, and confirmed method…
  7. If executable experiment code is actually changed, retain only non-duplicative smoke tests for those critical paths. Planning or…
  8. Use ccf-visual-composer when the requested deliverable includes visual composition, layout, or rendering. Supply real values, units…
  9. Before finalizing reported comparisons, reconcile claims, numbers, and configurations; use ccf-integrity-auditor for material unresolved…

What it can do on your machine

Read from SKILL.md and the folder at commit 5969e6b. 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

Ccf Experiment Designer loads about 1.9k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 803 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

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 mikubaka88/CCFA-Skills at commit 5969e6b, republished under its MIT licence (© mikubaka88). 803 words, ~1,950 tokens.

Download SKILL.mdSave it as .claude/skills/ccf-experiment-designer/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
ccf-experiment-designer
description
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.

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.
Show full SKILL.md (406 more words)Show less

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.

© mikubaka88, 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 3 other files (references) in ccf-experiment-designer of mikubaka88/CCFA-Skills.

  • SKILL.md
  • agents/openai.yaml
  • references/evidence-design.md
  • references/result-templates.md

Open the folder on GitHubat commit 5969e6b

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Questions about Ccf Experiment Designer

What does Ccf Experiment Designer do?

Design CCF experiment protocols and evidence schemas: datasets, baselines, metrics, ablations, and result-table contents. Ccf Experiment Designer is an agent skill from mikubaka88/CCFA-Skills. Design CCF experiment protocols and evidence schemas: datasets, baselines, metrics, ablations, and result-table contents.

When should I use Ccf Experiment Designer?

Ccf Experiment Designer fits situations like: benchmark planning.

How do I install Ccf Experiment Designer in Claude Code?

Run `npx skills add mikubaka88/CCFA-Skills --skill ccf-experiment-designer -a claude-code`. Or copy the skill folder (ccf-experiment-designer in mikubaka88/CCFA-Skills) into .claude/skills/ccf-experiment-designer in your project. Claude Code loads it when a task matches its description.

How do I install Ccf Experiment Designer in Codex?

Run `npx skills add mikubaka88/CCFA-Skills --skill ccf-experiment-designer -a codex`. Or copy the skill folder (ccf-experiment-designer in mikubaka88/CCFA-Skills) into .agents/skills/ccf-experiment-designer in your project. Codex loads it when a task matches its description.

Can I use Ccf Experiment Designer 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 mikubaka88/CCFA-Skills --skill ccf-experiment-designer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ccf-experiment-designer, .gemini/skills/ccf-experiment-designer, .github/skills/ccf-experiment-designer and .opencode/skills/ccf-experiment-designer in your project.

What does Ccf Experiment Designer need to run?

SKILL.md names no scripts, command-line tools or credentials: Ccf Experiment Designer is instructions for the agent only.

Does Ccf Experiment Designer 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 Ccf Experiment Designer 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 Ccf Experiment Designer use?

Ccf Experiment Designer 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 Ccf Experiment Designer use?

About 1.9k tokens (SKILL.md is roughly 7.8k 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 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Ccf Experiment Designer?

Skills that share tags, products or a category with Ccf Experiment Designer: Frontend Slides (zarazhangrui/frontend-slides, 30k stars), Algorithmic Art with p5.js (anthropics/skills, 180k stars), Canvas Design (anthropics/skills, 180k stars) and Impeccable (bestofjs/bestofjs, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ccf Experiment Designer?

mikubaka88 (a GitHub user) maintains it in mikubaka88/CCFA-Skills, which has 3,015 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on September 16, 2026.

Source: mikubaka88/CCFA-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.