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Aris Result To Claim

OrganizationPopular
OpenLAIR
aris-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.

Overview

PublisherOpenLAIR
Repositorydr-claw
Skill namearis-result-to-claim
Stars
1.2K
Forks
136
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by OpenLAIR on GitHub. Read the source before you install it.

Installation

Install the Aris Result To Claim AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-result-to-claim
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git /tmp/dr-claw
mkdir -p .claude/skills
cp -r /tmp/dr-claw/skills/aris-result-to-claim .claude/skills/aris-result-to-claim
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aris Result To Claim in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aris Result To Claim on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aris Result To Claim is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

  1. W&B (preferred): wandb.Api().run("<entity>/<project>/<run_id>").history() — metrics, training curves, comparisons
  2. EXPERIMENT_LOG.md: full results table with baselines and verdicts
  3. EXPERIMENT_TRACKER.md: check which experiments are DONE vs still running
  4. Log files: ssh server "tail -100 /path/to/training.log" if no other source
  5. 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:

markdown
- 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
  1. 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)
  2. Update CLAUDE.md Pipeline Status
  3. Decide whether to pivot to next idea from IDEA_CANDIDATES.md or try an alternative approach
partial — Claim partially supported
  1. Update the working claim to reflect what IS supported
  2. Record the gap in findings.md
  3. Design and run supplementary experiments to fill evidence gaps
  4. Re-run result-to-claim after supplementary experiments complete
  5. Multiple rounds of partial on the same claim → record analysis in findings.md, consider whether to narrow the claim scope or switch ideas
yes — Claim supported
  1. Record confirmed claim in project notes
  2. If ablation studies are incomplete → trigger /aris-ablation-planner
  3. If all evidence is in → ready for paper writing

Step 5: Update Research Wiki (if active)

Skip this step entirely if research-wiki/ does not exist.

if research-wiki/ exists:
    # 1. Create experiment page
    Create research-wiki/experiments/<exp_id>.md with:
      - node_id: exp:<id>
      - idea_id: idea:<active_idea>
      - date, hardware, duration, metrics
      - verdict, confidence, reasoning summary

    # 2. Update claim status
    for each claim resolved by this verdict:
        if verdict == "yes":
            Update claim page: status → supported
            python3 tools/research_wiki.py add_edge research-wiki/ --from "exp:<id>" --to "claim:<cid>" --type supports --evidence "<metric>"
        elif verdict == "partial":
            Update claim page: status → partial
            python3 tools/research_wiki.py add_edge research-wiki/ --from "exp:<id>" --to "claim:<cid>" --type supports --evidence "partial"
        else:
            Update claim page: status → invalidated
            python3 tools/research_wiki.py add_edge research-wiki/ --from "exp:<id>" --to "claim:<cid>" --type invalidates --evidence "<why>"

    # 3. Update idea outcome
    Update research-wiki/ideas/<idea_id>.md:
      - outcome: positive | mixed | negative
      - If negative: fill "Failure / Risk Notes" and "Lessons Learned"
      - If positive: fill "Actual Outcome" and "Reusable Components"

    # 4. Rebuild + log
    python3 tools/research_wiki.py rebuild_query_pack research-wiki/
    python3 tools/research_wiki.py log research-wiki/ "result-to-claim: exp:<id> verdict=<verdict> for idea:<idea_id>"

    # 5. Re-ideation suggestion
    Count failed/partial ideas since last /aris-idea-creator run.
    If >= 3: print "💡 3+ ideas tested since last ideation. Consider re-running /aris-idea-creator — the wiki now knows what doesn't work."

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 confidence is 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.

Frequently asked questions

What does the Aris Result To Claim AI skill do?

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.

Why use Aris Result To Claim on TypingMind?

Because you install it once and use it with any model. Aris Result To Claim is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aris Result To Claim in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-result-to-claim. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aris Result To Claim?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aris Result To Claim?

As many as you like. As long as a model supports skills, you can use Aris Result To Claim with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aris Result To Claim AI skill free?

Yes. It is published on GitHub by OpenLAIR under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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