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result-to-claim

result-to-claim is a skill for Claude Code from wanshuiyin/Auto-claude-code-research-in-sleep. It costs 63 tokens per session (4,581 once invoked), scanned A, original, MIT.

A review step that compares completed experiment results with the claims you intend to make. It judges which claims the evidence supports, which it does not, and what evidence is still missing.

In plain words
What is it for?
Use it after experiments and before writing a paper or review response, especially when deciding whether to confirm, supplement, or change a claim.
Why use it?
It helps prevent conclusions that go beyond the data. It also gives a clear next step when results are ambiguous, such as changing the claim or running additional work.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions CLAUDE.md; positional $N argument; mentions Claude Code.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):.

Part of the aris plugin — 87 skills, 1 command, 1 agent shipped together

not rated 19krepo +287 changed 19d ago A scan Socket: passSnyk: passSkillSpector: pass 63 tokens original MIT

Good fit Use it after experiments and before writing a paper or review response, especially when deciding whether to confirm, supplement, or change a claim.

Compare 6 skills from other repositories ↓
View source ↗ wanshuiyin/Auto-claude-code-research-in-sleep
About the project

ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.

wanshuiyin/Auto-claude-code-research-in-sleep · 19,107 stars · on GitHub

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
agentmods
npx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim

Made for: Claude Code.

Or install aris, the plugin that ships this one along with the rest of its 87 skills, 1 command, 1 agent.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for result-to-claim

README.md
[![agentmods](https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim/github.svg)](https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim)
Your own site
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for result-to-claim

Your own site · 80×15
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,581 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 24 Oct 2026
  • Snyk pass 24 Oct 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00063 $0.04581
Opus 5.5 $0.00025 $0.01832
Sonnet 5.5 $0.00013 $0.00916
Haiku 4.5 $0.00006 $0.00458

Measured 19d ago against content hash b12d10bdb514, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

result-to-claim scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/result-to-claim/SKILL.md · 325 lines

How it starts

The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Result-to-Claim Gate

Before each reviewer slot, follow Selectable Reviewers: resolve invocation → .aris/reviewer.json → Claude Code default (Codex MCP) with reviewer_route.py through the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only to backend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections are REVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.

🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. 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. See shared-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:

  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. idea-stage/docs/research_contract.md (legacy fallback: docs/research_contract.md): intended claims and experiment design

Read the full file on GitHub · 325 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 19d ago Changed b12d10bdb514
  2. 24d ago Changed · +13 lines 387612e7ee7b
  3. 2mo ago Changed ba53865e333f
  4. 2mo ago First seen · 312 lines · 63 tokens per session scan A 251940dc0a44

Subscribe to this mod's changes

result-to-claim is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (19,107 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 4,581 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Claude CodeResearchScientificai-researchautonomous-agentcodexmcpml-research
Related

result-to-claim compared with skills from other repositories

result-to-claim next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
result-to-claimwanshuiyin/Auto-claude-code-research-in-sleepSkill19k2d agoMITA63Claude Code
remote-compute-sshaipoch/open-scienceSkill5.5ktodayApache-2.0A52Claude Code, Codex
figure-styleaipoch/open-scienceSkill5.5ktodayApache-2.0A91Claude Code, Codex
paper-narrativeaipoch/open-scienceSkill5.5ktodayApache-2.0A58Claude Code, Codex
literature-reviewaipoch/open-scienceSkill5.5ktodayApache-2.0A54Claude Code, Codex
scvi-toolsaipoch/open-scienceSkill5.5ktodayApache-2.0A100Claude Code, Codex
esmfold2aipoch/open-scienceSkill5.5ktodayApache-2.0A223Claude Code, Codex