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).
Install / Use
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill result-to-claimInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Our assessment of result-to-claim
result-to-claim scores 98/100 on our quality scale, 82nd of 1,943 Automation skills we index (top 5%).
Its SKILL.md is 16 KB long, well organised into 18 sections with 6 code examples: a thorough specification that gives an agent plenty to work with.
With 16,644 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 9 days ago, so result-to-claim is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
result-to-claim compared with similar skills
All 4 of these similar skills score higher than result-to-claim; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| result-to-claim (this skill)by wanshuiyin | 98 | 16.6k | 9d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.8k | 12d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.0k | 1d ago | CLAUDE.md |
| rufloby ruvnet | 100 | 73.4k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.1k | today | CLAUDE.md |
Frequently asked questions
- How do I install result-to-claim?
- Run
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill result-to-claim. The install tabs above show the steps for each supported agent. - Which AI agents does result-to-claim work with?
- It is written for OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is result-to-claim safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is result-to-claim still maintained?
- The repository was last updated 9 days ago, so result-to-claim is actively maintained.
Skill content
View source on GitHubname: result-to-claim description: 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. argument-hint: "[experiment-description-or-wandb-run]" allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit, mcp__codex__codex, mcp__codex__codex-reply
Result-to-Claim Gate
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing — it judges whether results support a claim. Re-running that verdict on a wall-clock timer adds no new signal (the verdict changes only when the results change, not when the clock ticks). What you actually want to schedule is the external wait that precedes it — experiments done → then run this gate once. Seeshared-references/external-cadence.md.
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: $ARGUMENTS
When to Use
- After a set of experiments completes (main results, not just sanity checks)
- Before committing to claims in a paper or review response
- When results are ambiguous and you need an objective second opinion
Workflow
Step 1: Collect Results
Gather experiment data from whatever sources are available in the project:
- W&B (preferred):
wandb.Api().run("<entity>/<project>/<run_id>").history()— metrics, training curves, comparisons - EXPERIMENT_LOG.md: full results table with baselines and verdicts
- EXPERIMENT_TRACKER.md: check which experiments are DONE vs still running
- Log files:
ssh server "tail -100 /path/to/training.log"if no other source idea-stage/docs/research_contract.md(legacy fallback:docs/research_contract.md): intended claims and experiment design
Assemble the key information:
- What experiments were run (method, dataset, config)
- Main metrics and baseline comparisons (deltas)
- The intended claim these experiments were designed to test
- Any known confounds or caveats
Step 1.5: Deterministic evidence pre-check (before spending a Codex call)
For every claim that cites a specific number + a source file, verify the evidence
exists mechanically — no model call — to catch hallucinated evidence before
the jury runs (see shared-references/evidence-precheck.md).
1. Build the claims list. From the cited numbers and their result files, write
[{"id", "value", "source"}, ...] to .aris/claims.json (source is the result
file/glob relative to the project root; value is the cited number or string).
2. Run the pre-check — this is a real step, not a suggestion. Execute the block below (resolver per integration-contract §2, Policy B: warn-and-skip if the helper is unresolved — never block the audit):
# Policy B = warn-and-skip: nothing here may abort the audit. cd is non-fatal, the
# helper run is explicitly non-blocking, no pipefail-fragile pipe.
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" 2>/dev/null || true
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
fi
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
EVIDENCE_CHECK=".aris/tools/evidence_check.py"
[ -f "$EVIDENCE_CHECK" ] || EVIDENCE_CHECK="tools/evidence_check.py"
[ -f "$EVIDENCE_CHECK" ] || { [ -n "${ARIS_REPO:-}" ] && EVIDENCE_CHECK="$ARIS_REPO/tools/evidence_check.py"; }
[ -f "$EVIDENCE_CHECK" ] || EVIDENCE_CHECK=""
mkdir -p .aris
if [ -n "$EVIDENCE_CHECK" ]; then
# NB: evidence_check exits 1 when it FINDS hallucinated evidence (value_not_found /
# path_missing) — that is the useful signal, NOT a failure. So judge success by
# whether valid JSON was produced, never by exit code. `|| true` keeps set -e calm.
python3 "$EVIDENCE_CHECK" . --batch .aris/claims.json > .aris/evidence_precheck.json 2>.aris/evidence_precheck.err || true
if [ -s .aris/evidence_precheck.json ] && python3 -c "import json,sys;json.load(open('.aris/evidence_precheck.json'))" 2>/dev/null; then
cat .aris/evidence_precheck.json
else
echo "WARN: evidence_check produced no valid output (see .aris/evidence_precheck.err);" >&2
echo " pre-check skipped (Policy B); the Codex jury still runs." >&2
fi
else
echo "WARN: evidence_check.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2
echo " Pre-check skipped (Policy B); the Codex jury still runs. Fix: rerun" >&2
echo " bash tools/install_aris.sh, export ARIS_REPO, or copy the helper to tools/." >&2
fi
The output is {"results": [{id, value, source, status, ...}], "summary": {status: n}}
with status ∈ {verified, value_not_found, path_missing, unparseable}.
3. Act on the statuses. Any claim returned value_not_found or path_missing is
hallucinated evidence — mark it claim_supported: no with
integrity_status: evidence_not_found immediately; do NOT spend a Codex call defending a
number that isn't in the data. unparseable claims (no usable value/source) just go to
the jury normally.
4. Carry the per-claim status into Step 2. Feed a small
evidence pre-check: <id> → verified | value_not_found | path_missing | unparseable
table (from .aris/evidence_precheck.json) into the Step-2 Codex prompt so the jury knows
which claims have real evidence to read. If the pre-check was skipped (helper unresolved),
say so in that slot rather than omitting it.
verified here means only that the cited evidence exists — whether it
supports the claim is still the Codex jury's call in Step 2 (a deterministic
gate DRIVES, it does not ACQUIT).
Step 2: Codex Judgment
Send the collected results to Codex for objective evaluation. Include ONLY claims that passed the Step 1.5 pre-check — claims already terminally rejected (evidence_not_found) keep their deterministic verdict and are NOT re-litigated here:
mcp__codex__codex:
model: gpt-6-astra
config: {"model_reasoning_effort": "ultra"}
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]
Evidence pre-check (deterministic, from Step 1.5):
[per-claim: <id> → verified | value_not_found | path_missing.
A value_not_found/path_missing means the cited number is NOT in its result
file — treat that claim as having no evidence; do not defend it. `verified`
means the number exists in the file — YOU still judge whether it supports
the claim.]
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 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 Codex 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
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
Step 5: Update Research Wiki (if active)
Skip this step entirely if research-wiki/ does not exist.
If research-wiki/ exists, resolve $WIKI_SCRIPT per the canonical
chain documented in
shared-references/wiki-helper-resolution.md
(Variant B — warn-and-skip for caller skills). The verdict / idea-outcome
page edits below run on raw markdown and don't need the helper, but edges,
query-pack rebuild, and the log line do. This skill never edits a claim's
status field and never creates a claim node — claims are born (and their
proof status set) by /proof-checker; here we only attach experiment edges.
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
ARIS_REPO="${ARIS_REPO:-$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null)}"
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
WIKI_SCRIPT=".aris/tools/research_wiki.py"
[ -f "$WIKI_SCRIPT" ] || WIKI_SCRIPT="tools/research_wiki.py"
[ -f "$WIKI_SCRIPT" ] || { [ -n "${ARIS_REPO:-}" ] && WIKI_SCRIPT="$ARIS_REPO/tools/research_wiki.py"; }
[ -f "$WIKI_SCRIPT" ] || {
echo "WARN: research_wiki.py not found; verdict will be reported but wiki edges/query-pack/log will be skipped. Fix: bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or cp <ARIS-repo>/tools/research_wiki.py tools/." >&2
WIKI_SCRIPT=""
}
if research-wiki/ exists:
# 1. Create/refresh the experiment node FIRST (verdict OWNER → --update-on-exist so
# a re-judge overwrites the stale verdict). The supports/invalidates edges in #2
# point FROM exp:<id>, and add_edge does NOT verify node existence — so GATE those
# edges on the experiment node having been born
Truncated for display — read the full file on GitHub.
Related Skills
Agent-Reach
85.8kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
headroom
74.0kCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
ruflo
73.4k🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
CowAgent
47.1kOpen-source super AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-agent, multi-model, multi-channel. Lightweight, extensible, one-line install.
Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
