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.
How do I install this agent skill?
npx skills add https://github.com/mikubaka88/ccfa-skills --skill ccf-experiment-designerIs this agent skill safe to install?
- Gen Agent Trust Hubpass
The skill is a functional tool for designing academic experiments and generating result tables. It is safe for its intended purpose, though it possesses a minor surface for indirect prompt injection because it processes user-provided research drafts and specifications without explicit sanitization.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
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 withTBDplaceholders.result-presentation: result tables, figure evidence plans, chart specs, caption facts, and missing-value markers from supplied real results.
Workflow
- 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.
- Extract the storyline from the idea or draft. Reuse the supplied claim/mechanism description. Read
../ccf-paper-writer/references/storyline-blueprint.mdonly when the central claim needs clarification, not for an already specified result table. - 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.
- Resolve missing dataset, baseline, metric, or protocol provenance through
ccf-literature-searcherbefore fixing dependent comparisons. Verify compatibility with the central claim. For a consequential unresolved claim-to-test mismatch, request a focusedccf-paper-reviewercheck and integrate its findings; do not create a full review report for a protocol question. Mark unavailable evidence instead of guessing. - Load
references/evidence-design.mdfor substantive protocol design orreferences/result-templates.mdfor table/schema work. Do not load both for a small task unless both are needed. - 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.
- 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.
- Use
ccf-visual-composerwhen 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. - Before finalizing reported comparisons, reconcile claims, numbers, and configurations; use
ccf-integrity-auditorfor material unresolved conflicts. Useccf-paper-writerfor needed manuscript prose andccf-submission-checkerwhen 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.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/mikubaka88/ccfa-skills/ccf-experiment-designer">View ccf-experiment-designer on skillZs</a>