PluginBench
Skill
Pass
Audit score 90

ccf-experiment-designer

mikubaka88/ccfa-skills

Design CCF experiment protocols, evidence schemas, datasets, baselines, metrics, and ablations without fabricating results.

What is ccf-experiment-designer?

Structured skill for designing rigorous experiment protocols and evidence schemas for academic research. Use it to plan datasets, baselines, metrics, ablations, and result-table structures while preserving real values and avoiding fabrication. Delegates visual rendering to ccf-visual-composer and broad literature retrieval to ccf-literature-searcher.

  • Design experiment protocols with datasets, baselines, metrics, and ablations mapped to claims
  • Create result-table templates and evidence schemas with TBD placeholders for real values
  • Plan robustness, failure analysis, and efficiency tests tied to observed or claim-relevant scenarios
  • Verify complete method configurations for reported comparisons without smoke-test inflation
  • Integrate with ccf-humanization and ccf-common family rules before specialist execution
  • Map claims to sufficient evidence and resolve missing provenance through literature search

How to install ccf-experiment-designer

npx skills add https://github.com/mikubaka88/ccfa-skills --skill ccf-experiment-designer
Prerequisites
  • Install ccf-humanization and ccf-common skills (required preflights before use)
  • Clarify the central hypothesis or claim to be tested
  • Identify or retrieve dataset, baseline, and metric specifications (via ccf-literature-searcher if needed)
Claude Code
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How to use ccf-experiment-designer

  1. 1.Load ccf-humanization and ccf-common rules before starting any experiment work
  2. 2.Specify the mode: design (protocol planning), result-template (fill-in tables), or result-presentation (from real results)
  3. 3.Extract the storyline and map each major claim to required evidence, dataset, baseline, metric, and ablation
  4. 4.Resolve missing dataset or baseline provenance; verify compatibility with the central claim
  5. 5.Use references/evidence-design.md for substantive protocol design or references/result-templates.md for table schemas
  6. 6.Output the requested artifact (experiment plan, table, figure spec, or ablation list) with no fabricated numbers or improvements
  7. 7.Name the next CCFA owner (e.g., ccf-visual-composer for rendering, ccf-paper-writer for prose)

Use cases

Good for
  • Planning a benchmark comparison: specify datasets, baselines, metrics, and ablation scope before running experiments
  • Designing an ablation study: identify mechanism-relevant ablations and map each to a hypothesis
  • Preparing a result table: create a template with real values and explicit missing-value markers
  • Verifying experiment completeness: reconcile claims, numbers, and method configurations before publication
  • Scoping robustness tests: add only observed, plausible, or claim-relevant failure cases, not defensive enumerations
Who it's for
  • Researchers designing rigorous experiments and benchmarks
  • Paper authors planning evidence structure before writing results sections
  • Teams coordinating experiment design across multiple contributors
  • Reviewers or auditors verifying experiment-to-claim mapping

ccf-experiment-designer FAQ

Can I use this skill to fill in result numbers if I haven't run the experiments yet?

No. This skill builds result tables and evidence schemas only from supplied real values or explicit TBD placeholders. Never fabricate numbers, improvements, significance, or benchmark ranks.

Should I include defensive test cases that are unlikely but theoretically possible?

No. Add robustness or failure tests only when observed, plausible, claim-relevant, or venue-required. Do not enumerate remote defensive cases.

What if I don't know which baseline or dataset to use?

Use ccf-literature-searcher to resolve missing dataset, baseline, or metric provenance before fixing dependent comparisons. Mark unavailable evidence explicitly instead of guessing.

Does this skill handle table rendering and visual styling?

No. This skill designs the evidence schema and content. Delegate visual composition, layout, palette, and rendering to ccf-visual-composer.

When should I use smoke tests in my experiment plan?

Only for non-duplicative critical paths if executable code is actually changed. Planning or formatting alone does not require smoke tests. Keep them outside publication evidence.

Full instructions (SKILL.md)

Source of truth, from mikubaka88/ccfa-skills.


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." metadata: ccf_skill_controls: handoff_question_mode: partial respect_session_denylists: true protect_idea_scope_in_writing: true private_material_safety: moderate shared_controls: ../ccf-common/references/

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:

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.