hypothesis

hypothesis is a skill for Claude Code, Codex from stellarshenson/claude-code-plugins. It costs 206 tokens per session (4,073 once invoked), scanned A, original, MIT.

A documentation workflow for testing research ideas, called hypotheses, through recorded experiments. It keeps an append-only experiment log and a separate document summarizing the approaches that performed best.

In plain words
What is it for?
Use it to document machine-learning or other technical experiments, compare competing ideas, and turn successful findings into a final design.
Why use it?
It prevents research decisions and results from being scattered across notes or lost between runs. Each experiment records its setup, prediction, result, and conclusion so it can be reproduced independently.

Skill for Claude CodeCodex

Part of the datascience plugin — 20 skills, 15 commands shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/stellarshenson/claude-code-plugins/hypothesis
Any agent
npx skills add stellarshenson/claude-code-plugins --skill hypothesis
Clone the repo
git clone --depth 1 https://github.com/stellarshenson/claude-code-plugins

Made for: Claude Code, Codex.

Or install datascience, the plugin that ships this one along with the rest of its 20 skills, 15 commands.

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 hypothesis

README.md
[![agentmods](https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/hypothesis.svg)](https://agentmods.dev/skills/stellarshenson/claude-code-plugins/hypothesis)
Your own site
<a href="https://agentmods.dev/skills/stellarshenson/claude-code-plugins/hypothesis"><img src="https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/hypothesis.svg" alt="Measured on agentmods" height="20"></a>
Per session 206 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,073 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00206 $0.04073
Opus 5 $0.00103 $0.02037
Sonnet 5 $0.00041 $0.00815
Haiku 4.5 $0.00021 $0.00407

Measured 3d ago against content hash 31d570e01a67, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

hypothesis 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 3d 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.

plugins/datascience/skills/hypothesis/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Hypothesis

Maintain two research docs: a canonical experiments log (every hypothesis with setup, prediction, result, verdict) and a SOTA document (survivors distilled into a final design). Log is append-only and grows across runs; SOTA is rewritten when the arc converges.

Style - terse technical-documentation: 1-2 overview sentences then factual bullets; one fact per bullet; numbers inline; no full stop ending a bullet; no em-dashes (use -), unicode arrows (→), escape \$. Prose only where an argument needs it. Full reference: the technical-documentation skill.

Mirror the closest examples/ doc before writing - its section order wins over this skill's guidance on any conflict. Do not invent structure. See Examples for which to pick.

Toolchain gate (MANDATORY - run before anything else)

Run this first, every session, before any other work. The upgrade always runs; a version mismatch blocks.

python3 -m pip install --user --upgrade stellars-claude-code-plugins 2>&1 | tail -1
LIB=$(python3 -c "import importlib.metadata as m;print(m.version('stellars-claude-code-plugins'))" 2>/dev/null) || { echo "FATAL: toolkit unavailable"; exit 1; }
PLUG=$(grep -m1 '"version"' "${CLAUDE_PLUGIN_ROOT}/.claude-plugin/plugin.json" 2>/dev/null | cut -d'"' -f4)
OLDER=$(printf '%s\n%s\n' "$LIB" "$PLUG" | sort -V | head -1)
[ -n "$PLUG" ] && [ "$LIB" != "$PLUG" ] && [ "$OLDER" = "$LIB" ] && { echo "STALE: library $LIB older than plugin $PLUG - refusing to run on an outdated CLI; re-run the upgrade"; exit 1; }
echo "toolkit $LIB"

Run the CLI without touching the caller's project. The gate above puts it on PATH, so the bare command name is the whole invocation. uv run instead resolves whatever project the working directory sits in and writes uv.lock and .venv into it, so if you reach for uv pass --no-project (uv run --no-project <cli> ...) - it skips project discovery, leaves the tree untouched and still finds the same PATH binary. --no-sync and --frozen are not substitutes; both still create .venv.

Read the full file on GitHub · 133 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. 3d ago First seen · 133 lines · 206 tokens per session scan A 31d570e01a67

Subscribe to this mod's changes

hypothesis is a skill published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed 4d ago), licensed MIT. It adds 206 tokens to every session and 4,073 once invoked, about $0.0010 per session on Opus 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 2026-08-31.

Related

Other skills, from other repositories

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

nature-statistics

Audit, revise, or draft manuscript statistical reporting for Nature / high-impact journal submissions. Use when the user asks to check statistical analysis sections, p values, confidence intervals, sample size, biological versus technical replicates, randomization, blinding, multiple-comparison correction, model…

Yuan1z0825/nature-skills · 139 tokens

evaluating-with-leakage-gates

Evaluate an OpenMed de-identification or clinical NER model against the leakage-first release gates G1a through G8, which gate releases on residual PHI leakage rather than on F1. Use when the user wants to run the OpenMed eval harness on a synthetic golden set, decide whether a de-id model is RELEASABLE or…

maziyarpanahi/openmed · 158 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens

mixed-precision

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

aiming-lab/AutoResearchClaw · 25 tokens

indication-dossier

Build a source-backed biomedical indication dossier. Use when a research task asks for disease biology, target rationale, patient segmentation, biomarkers, trials, drugs, competitive landscape, or translational evidence.

companion-inc/feynman · 43 tokens