talont-org/make-experiment-diag
src/autoskillit/skills_extended/make-experiment-diag/SKILL.md
Interactive selection of experimental design lens for visualizing experiment methodology. Routes to the appropriate exp-lens-* skill.
$ install --global
skillsauthnpx skillsauth add talont-org/autoskillit make-experiment-diagInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: May 9, 2026, 5:20 AM154.9s1 file scanned
SKILL.md
- name:
- make-experiment-diag
- categories:
- [exp-lens]
- activate_deps:
- [exp-lens]
- description:
- Interactive selection of experimental design lens for visualizing experiment methodology. Routes to the appropriate exp-lens-* skill.
- - matcher:
- *
- - type:
- command
- command:
- echo 'make-experiment-diag - Selecting experimental design lens...'
- once:
- true
Experimental Design Diagram Selection
Select the right experimental design lens for your analysis. Each lens asks one primary question about an experiment's design, surfacing assumptions and failure modes specific to that epistemic dimension.
When to Use
- Want to visualize or audit experimental design methodology
- Need to select the right lens for a specific experimental concern
- User says
/autoskillit:make-experiment-diag - User wants to examine a specific aspect of an experiment
Critical Constraints
NEVER:
- Skip the selection step — always confirm which lens to use
- Modify any source code or experimental artifacts
- Combine multiple lenses in a single invocation
ALWAYS:
- Present the selection table to help the user choose
- Match user description to the most relevant lens
- Load the selected lens skill using the Skill tool
- Follow the loaded skill's instructions exclusively; the delegated skill handles completion
Core Lenses (always recommended)
| Your experiment involves... | Use this lens | Question it answers | Philosophical Mode | |-----------------------------|---------------|---------------------|--------------------| | Unclear or shifting claims | Estimand Clarity | What exactly is the claim? | Evidential | | Causal attribution without explicit assumptions | Causal Assumptions | What causal assumptions support this design? | Causal-Structural | | Baseline quality or fairness | Comparator Construction | Is the comparator fair and relevant? | Counterfactual | | Data preprocessing, splits, or feature pipelines | Pipeline Integrity | Could data handling create optimistic bias? | Integrity | | Nondeterminism, seed sensitivity, run-to-run noise | Variance & Stability | Is the signal larger than the noise? | Stability | | Asymmetric tuning, compute, or engineering effort | Fair Comparison | Are alternatives compared symmetrically? | Fairness | | Reproducing results from artifacts alone | Reproducibility & Artifacts | Could an independent party reproduce this? | Transparency | | Metric choice, proxy validity, score interpretation | Measurement Validity | Do measurements justify the interpretation? | Psychometric | | Robustness to preprocessing or modeling choices | Sensitivity & Robustness | Which assumptions are load-bearing? | Robustness | | Generalization claims beyond the test suite | Benchmark Representativeness | Does this generalize beyond the test bed? | Generalizability |
Extended Lenses (enable by experiment class)
| Your experiment involves... | Use this lens | Question it answers | Philosophical Mode | |-----------------------------|---------------|---------------------|--------------------| | Shared resources, network effects, spillovers | Unit & Interference | What is the unit, and can treatments spill over? | Causal-Structural | | Power, multiplicity, sequential monitoring | Error Budget | Are error risks sized and controlled? | Statistical | | Theory claims needing adversarial stress | Severity Testing | Would this design have caught the error? | Falsificationist | | Assignment mechanism, blocking, stratification | Randomization & Blocking | Where does comparability come from? | Design-Structural | | Confounds, history effects, co-interventions | Validity Threats | What alternative explanations survive? | Adversarial | | Multi-step exploration, adaptive allocation | Iterative Learning | How does this maximize learning per cost? | Decision-Theoretic | | Mixing discovery and confirmation in one study | Exploratory vs Confirmatory | Is this discovery or test? | Boundary | | Deployment risk, fairness, stakeholder harm | Governance & Risk | What risks arise from acting on this result? | Governance |
Workflow
Step 1: Prompt User
Ask the user:
What aspect of your experimental design would you like to examine?
Example prompts:
- "I want to check if my causal claims are well-supported"
- "I need to verify my data pipeline doesn't leak"
- "Are my baselines fair comparisons?"
- "Could someone reproduce my experiment?"
- "Is my benchmark representative enough?"
Step 2: Match to Lens
Based on the user's description, match to the most appropriate lens using the selection tables above. If ambiguous, present the top 2-3 candidates and ask the user to choose.
Step 3: Load the Selected Lens
Use the Skill tool to load the selected /autoskillit:exp-lens-{slug} skill.
If the Skill tool cannot be used (disable-model-invocation) or refuses this invocation, do NOT proceed with the exp-lens analysis. Abort this step and inform the user that the lens skill is unavailable.
Step 4: Execute the Loaded Skill
The loaded lens skill takes over and runs its full analysis workflow.
Alias Table
| Alias | Lens | |-------|------| | estimand | exp-lens-estimand-clarity | | causal | exp-lens-causal-assumptions | | comparator | exp-lens-comparator-construction | | pipeline | exp-lens-pipeline-integrity | | variance | exp-lens-variance-stability | | fairness | exp-lens-fair-comparison | | reproducibility | exp-lens-reproducibility-artifacts | | measurement | exp-lens-measurement-validity | | sensitivity | exp-lens-sensitivity-robustness | | benchmark | exp-lens-benchmark-representativeness | | unit | exp-lens-unit-interference | | error | exp-lens-error-budget | | severity | exp-lens-severity-testing | | randomization | exp-lens-randomization-blocking | | validity | exp-lens-validity-threats | | iterative | exp-lens-iterative-learning | | exploratory | exp-lens-exploratory-confirmatory | | governance | exp-lens-governance-risk |
Related Skills
/autoskillit:mermaid- Shared diagram styling (loaded by individual lenses)/autoskillit:make-arch-diag- Software architecture lens counterpart/autoskillit:verify-diag- Verify diagram accuracy against codebase
Related Skills
talont-org/write-recipe
development
VerifiedTrustedCommunity
Generate YAML recipes for .autoskillit/recipes/. Use when user says "make script skill", "generate script", "script a workflow", "write a script", "create a script", "new recipe", "write a pipeline", or when loaded by other skills for script formatting.
2SKILL.mdUpdated May 9, 2026
talont-org/vis-lens-uncertainty
data-ai
VerifiedTrustedCommunity
Create Uncertainty Representation visualization planning spec showing error bar definitions, distribution-aware alternatives, and multi-seed variance protocols. Statistical lens answering "How is uncertainty honestly represented?"
2SKILL.mdUpdated May 9, 2026
talont-org/vis-lens-temporal
data-ai
VerifiedTrustedCommunity
Create Temporal Dynamics visualization planning spec showing axis scaling (linear vs log), smoothing disclosure, epoch/step alignment, run aggregation (mean + variance bands), early-stopping markers, and wall-clock vs step-count x-axis. Temporal lens answering "Are training dynamics shown clearly and honestly?"
2SKILL.mdUpdated May 9, 2026
talont-org/vis-lens-story-arc
data-ai
VerifiedTrustedCommunity
Create Narrative Story Arc visualization planning spec showing visual consistency across the report (same color = same model everywhere), logical figure progression, redundant figure detection, and narrative dependency between figures. Narrative lens answering "Do the figures tell a coherent story across the report?"
2SKILL.mdUpdated May 9, 2026