Result-to-Claim Gate

Experiments produce numbers; this gate decides what those numbers mean. Collect results from available sources, get an external-LLM judgment (via llm-chat MCP), 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

Reviewer LLM Configuration (mandatory, read first)

This skill calls an external LLM reviewer. Never hardcode a model name and never read the reviewer model from task.md / project READMEs / source comments. Project-level files may list available API keys for unrelated purposes (e.g., LLM-as-judge inside experiment code); those are not the reviewer config.

Resolve LLM_MODEL, LLM_BASE_URL, LLM_API_KEY strictly in this priority order before any reviewer call:

1. Project MCP config — ${PROJECT_ROOT}/.mcp.json, field mcpServers["llm-chat"].env.{LLM_MODEL,LLM_BASE_URL,LLM_API_KEY}.
2. User MCP config — ~/.claude/settings.json, same field.
3. Shell environment — $LLM_MODEL, $LLM_BASE_URL, $LLM_API_KEY.

Pre-flight check (run before Step 1, mandatory)

LLM_MODEL_SRC=""
if [ -f .mcp.json ] && jq -e '.mcpServers["llm-chat"].env.LLM_MODEL' .mcp.json >/dev/null 2>&1 ; then
  export LLM_MODEL=$(jq -r '.mcpServers["llm-chat"].env.LLM_MODEL' .mcp.json)
  export LLM_BASE_URL=$(jq -r '.mcpServers["llm-chat"].env.LLM_BASE_URL' .mcp.json)
  export LLM_API_KEY=$(jq -r '.mcpServers["llm-chat"].env.LLM_API_KEY' .mcp.json)
  LLM_MODEL_SRC="project .mcp.json"
elif [ -f ~/.claude/settings.json ] && jq -e '.mcpServers["llm-chat"].env.LLM_MODEL' ~/.claude/settings.json >/dev/null 2>&1 ; then
  export LLM_MODEL=$(jq -r '.mcpServers["llm-chat"].env.LLM_MODEL' ~/.claude/settings.json)
  export LLM_BASE_URL=$(jq -r '.mcpServers["llm-chat"].env.LLM_BASE_URL' ~/.claude/settings.json)
  export LLM_API_KEY=$(jq -r '.mcpServers["llm-chat"].env.LLM_API_KEY' ~/.claude/settings.json)
  LLM_MODEL_SRC="user ~/.claude/settings.json"
elif [ -n "$LLM_MODEL" ] && [ -n "$LLM_BASE_URL" ] && [ -n "$LLM_API_KEY" ] ; then
  LLM_MODEL_SRC="shell env"
fi
echo "[reviewer-config] LLM_MODEL=$LLM_MODEL  LLM_BASE_URL=$LLM_BASE_URL  source=$LLM_MODEL_SRC"

Hard-fail rule: If LLM_MODEL is empty after this resolution (none of the three sources provides it), the skill MUST abort with:

"Reviewer model not configured. Add mcpServers.llm-chat.env.{LLM_MODEL,LLM_BASE_URL,LLM_API_KEY} to .mcp.json (project) or ~/.claude/settings.json (user)."

Do not guess a default. Do not fall back to a model name read from task.md or any other project file.

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: External LLM Judgment

Send the collected results to the external LLM reviewer for objective evaluation. Always ask the external reviewer for strict, high-rigor feedback.

mcp__llm-chat__chat:
  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: pass | fail
       — `pass` means the data fully supports the claim as stated.
       — `fail` means the data does NOT fully support the claim. This includes:
           * the claim is refuted by the data
           * the claim is only partially supported (some sub-claims work, others don't)
           * coverage is too narrow for a general claim (e.g., only 1 dataset / 1 model / 1 seed)
           * the direction is right but the magnitude misses the pre-registered pass criterion
           * mixed results across metrics or splits
           * critical baselines / controls are missing
       Use `pass` only when the data unambiguously supports the claim. Anything borderline is `fail`.
    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 — that is `fail`, not `pass`.

Step 3: Parse and Normalize

Extract structured fields from the external reviewer's response:

- claim_supported: pass | fail
- what_results_support: "..."
- what_results_dont_support: "..."
- missing_evidence: "..."
- suggested_claim_revision: "..."
- next_experiments_needed: "..."
- confidence: high | medium | low

Step 3.5: Check Experiment Integrity (if audit exists)

Skip this step if EXPERIMENT_AUDIT.json does not exist.

if EXPERIMENT_AUDIT.json exists:
    read integrity_status from file
    attach to verdict output:
        integrity_status: pass | warn | fail

    if integrity_status == "fail":
        append to verdict: "[INTEGRITY CONCERN] — audit found issues, see EXPERIMENT_AUDIT.md"
        downgrade confidence to "low" regardless of external reviewer judgment

    if integrity_status == "warn":
        append to verdict: "[INTEGRITY: WARN] — audit flagged potential issues"
else:
    integrity_status = "unavailable"
    verdict is labeled "provisional — no integrity audit run"
    (this does NOT block anything — pipeline continues normally)

See shared-references/experiment-integrity.md for the full integrity protocol.

Step 4: Route Based on Verdict

fail — Claim not (fully) supported

Covers both outright refutation AND partial / borderline support — under the binary scheme, anything short of unambiguous support routes here.

1. Record postmortem in findings.md (Research Findings section):
   • What was tested, what worked, what fell short, hypotheses for why
   • If the gap is narrow (right direction, wrong magnitude / narrow coverage): list what supplementary experiments would close it; re-run result-to-claim after they complete
   • If the gap is wide (claim refuted, wrong direction): list constraints for future attempts (what NOT to try again)
2. Update CLAUDE.md Pipeline Status
3. Decide between three routes:
   • Narrow the claim to the subset that IS supported, then re-evaluate
   • Run supplementary experiments to fill the gaps and re-evaluate
   • Pivot to the next idea from IDEA_CANDIDATES.md
4. Multiple consecutive fail rounds on the same claim → record analysis in findings.md and lean toward narrowing the claim scope or pivoting.

pass — Claim supported

1. Record confirmed claim in project notes
2. If ablation studies are incomplete → trigger /ablation-planner
3. If all evidence is in → ready for paper writing

Rules

• The external LLM is the judge, not CC. CC collects evidence and routes; the external reviewer evaluates. This prevents post-hoc rationalization.
• Do not inflate claims beyond what the data supports. Borderline / partial results are fail, not pass; do not round up.
• 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 llm-chat MCP is unavailable (call fails), CC makes its own judgment and marks it [pending external review] — do not block the pipeline.
• Always record the verdict and reasoning in findings.md, regardless of outcome.

Review Tracing

After each mcp__llm-chat__chat reviewer call, save the trace following shared-references/review-tracing.md. Write files directly to .mechanist/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).