> result-to-claim
Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing. Codex MCP evaluates results against intended claims and routes to next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.
curl "https://skillshub.wtf/wanshuiyin/Auto-claude-code-research-in-sleep/result-to-claim?format=md"Result-to-Claim Gate
Experiments produce numbers; this gate decides what those numbers mean. Collect results from available sources, get a Codex judgment, then auto-route based on the verdict.
Context: $ARGUMENTS
When to Use
- After a set of experiments completes (main results, not just sanity checks)
- Before committing to claims in a paper or review response
- When results are ambiguous and you need an objective second opinion
Workflow
Step 1: Collect Results
Gather experiment data from whatever sources are available in the project:
- W&B (preferred):
wandb.Api().run("<entity>/<project>/<run_id>").history()— metrics, training curves, comparisons - EXPERIMENT_LOG.md: full results table with baselines and verdicts
- EXPERIMENT_TRACKER.md: check which experiments are DONE vs still running
- Log files:
ssh server "tail -100 /path/to/training.log"if no other source - docs/research_contract.md: intended claims and experiment design
Assemble the key information:
- What experiments were run (method, dataset, config)
- Main metrics and baseline comparisons (deltas)
- The intended claim these experiments were designed to test
- Any known confounds or caveats
Step 2: Codex Judgment
Send the collected results to Codex for objective evaluation:
mcp__codex__codex:
config: {"model_reasoning_effort": "xhigh"}
prompt: |
RESULT-TO-CLAIM EVALUATION
I need you to judge whether experimental results support the intended claim.
Intended claim: [the claim these experiments test]
Experiments run:
[list experiments with method, dataset, metrics]
Results:
[paste key numbers, comparison deltas, significance]
Baselines:
[baseline numbers and sources — reproduced or from paper]
Known caveats:
[any confounding factors, limited datasets, missing comparisons]
Please evaluate:
1. claim_supported: yes | partial | no
2. what_results_support: what the data actually shows
3. what_results_dont_support: where the data falls short of the claim
4. missing_evidence: specific evidence gaps
5. suggested_claim_revision: if the claim should be strengthened, weakened, or reframed
6. next_experiments_needed: specific experiments to fill gaps (if any)
7. confidence: high | medium | low
Be honest. Do not inflate claims beyond what the data supports.
A single positive result on one dataset does not support a general claim.
Step 3: Parse and Normalize
Extract structured fields from Codex response:
- claim_supported: yes | partial | no
- what_results_support: "..."
- what_results_dont_support: "..."
- missing_evidence: "..."
- suggested_claim_revision: "..."
- next_experiments_needed: "..."
- confidence: high | medium | low
Step 4: Route Based on Verdict
no — Claim not supported
- Record postmortem in findings.md (Research Findings section):
- What was tested, what failed, hypotheses for why
- Constraints for future attempts (what NOT to try again)
- Update CLAUDE.md Pipeline Status
- Decide whether to pivot to next idea from IDEA_CANDIDATES.md or try an alternative approach
partial — Claim partially supported
- Update the working claim to reflect what IS supported
- Record the gap in findings.md
- Design and run supplementary experiments to fill evidence gaps
- Re-run result-to-claim after supplementary experiments complete
- Multiple rounds of
partialon the same claim → record analysis in findings.md, consider whether to narrow the claim scope or switch ideas
yes — Claim supported
- Record confirmed claim in project notes
- If ablation studies are incomplete → trigger
/ablation-planner - If all evidence is in → ready for paper writing
Rules
- Codex is the judge, not CC. CC collects evidence and routes; Codex evaluates. This prevents post-hoc rationalization.
- Do not inflate claims beyond what the data supports. If Codex says "partial", do not round up to "yes".
- A single positive result on one dataset does not support a general claim. Be honest about scope.
- If
confidenceis low, treat the judgment as inconclusive and add experiments rather than committing to a claim. - If Codex MCP is unavailable (call fails), CC makes its own judgment and marks it
[pending Codex review]— do not block the pipeline. - Always record the verdict and reasoning in findings.md, regardless of outcome.
> related_skills --same-repo
> training-check
Periodically check WandB metrics during training to catch problems early (NaN, loss divergence, idle GPUs). Avoids wasting GPU hours on broken runs. Use when training is running and you want automated health checks.
> paper-slides
Generate conference presentation slides (beamer LaTeX → PDF + editable PPTX) from a compiled paper, with speaker notes and full talk script. Use when user says "做PPT", "做幻灯片", "make slides", "conference talk", "presentation slides", "生成slides", "写演讲稿", or wants beamer slides for a conference talk.
> paper-poster
Generate a conference poster (article + tcbposter LaTeX → A0/A1 PDF + editable PPTX + SVG) from a compiled paper. Use when user says "做海报", "制作海报", "conference poster", "make poster", "生成poster", "poster session", or wants to create a poster for a conference presentation.
> paper-illustration
Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Codex-supervised iterative refinement loop. Use when user says "生成图表", "画架构图", "AI绘图", "paper illustration", "generate diagram", or needs visual figures for papers.