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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/shiien-experiment-analyze
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: documents, research
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/experiment-analyze
What's inside
- Skill name
experiment-analyze- Description
- The EVALUATE stage of the research loop. Judges experiment results against the pre-registered contract (never against post-hoc criteria), classifies the outcome as supporting/contradicting/inconclusive evidence with a strength stamp, and persists the verdict to RESEARCH_STATE.md and EXPERIMENTS.json. Triggers when the user shares training logs, metrics, or asks "what do these results mean?", "are these results good?", or auto-chains after experiment-monitor reports completion.
- License
- MIT
- Instructions
- 55 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# experiment-analyze
**Trigger**: User shares experiment results, training logs, or metrics; asks
"what do these results mean?"; or an external run reaches `completed`.
**Principle**: an experiment is complete only when this stage has run.
Judgment happens against criteria written BEFORE the result existed.
**Process**:
1. **Retrieve the contract** for this run: `runs/<id>/contract.yaml` (harness
runs — prefer `python -m harness evaluate <run>`, which is deterministic
and tamper-checked), `logs/experiments/{exp_id}.yaml § contract`, or
`experiments/{exp_name}/plan.md § Contracts`. If no contract exists, say
so explicitly: the analysis is then EXPLORATORY and may generate
hypotheses but not evidence — offer to write a contract for a
confirmatory rerun.
2. **Parse the results** (tables, metrics, W&B, logs). Check
`required_diagnostics` exist; missing diagnostics → inconclusive.
3. **Judge deterministically** against `success_metric` /
`failure_condition`: prefer computed comparisons (mean ± std over seeds,
tests, invariants) over prose judgment. Keep `dev.*` and `confirm.*`
metrics separate; a claim confirmed only on dev metrics is not confirmed.
4. **Guardrails** (from `CLAUDE.md § Evaluation Guardrails`):
- a crash/NaN/OOM is a failure record, NOT negative evidence — route to
`code` (debug mode) / resume instead of a verdict;
- single noisy runs get `strength: weak (n=1)` — never "shows" or
"proves";
- if the observed result makes a different criterion look more
appropriate, record the pre-registered verdict FIRST, then propose the
new criterion as a new experiment.
5. **Persist the evidence** (this step is not optional):
- `RESEARCH_STATE.md § Established evidence`: `[date] {exp/run id}:
SUPPORTS/CONTRADICTS — {question} | {criterion detail} | strength: {stamp}`;
inconclusive outcomes go to `§ Unresolved uncertainties` with what would
sharpen them.
- `EXPERIMENTS.json`: set `status`, `run`, `verdict` on the ledger entry.
- If this is the hypothesis's **second consecutive `inconclusive`**
verdict, don't queue a third unchanged rerun: propose a narrower
hypothesis (tighter contract) or mark it `terminated`. When evidence
clears the bar either way, annotate `§ Active hypotheses` with
`[decision: supported|falsified, date, evidence ref]` (`CLAUDE.md §
Hypothesis Closure & Scope Discipline`) — don't leave a decided
hypothesis looking still-open.
6. **Propose the next experiment**: the cheapest run that resolves the
sharpest remaining uncertainty, one conceptual factor changed. Append to
`§ Next recommended experiments`.
**Inputs**: results + the pre-registered contract
**Outputs**: verdict + updated `RESEARCH_STATE.md` / `EXPERIMENTS.json`; analysis inline
**Token**: ~3-8K
**Composition**: supported core claim → `writing` (draft mode) + `checklist`
(update mode); contradicted → `decision-analyze`; crashed → `code` (debug mode);
inconclusive → `experiment-plan` for a sharper contract.From Shiien/Self-Evolved-Research-Framework, MIT.
More from Shiien/Self-Evolved-Research-Framework
- 0.534score
checklist
Shiien
All checklist-tree write operations in one mode-based skill — CREATE (add a leaf or branch task with L2 sub-checklist / paper audit template), UPDATE (mark…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
code
Shiien
Code-workflow scaffolding in one mode-based skill — BRANCH (git branch/worktree isolation with exp|feat|fix prefixes), ROADMAP (step-by-step implementation…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-dse
Shiien
Design-space exploration — systematic hyperparameter sweep over a search space defined by an `experiment-plan`.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
decision-analyze
Shiien
Structured analysis of a decision — for each option list pros/cons, effort, risk, milestone alignment, and reversibility, then produce a recommendation (user…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
paper-read
Shiien
Read a research paper (arXiv link, PDF, or pasted text) and produce structured reading notes saved to resources/papers/.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-plan
Shiien
Design the full experimental spec before any runs — map paper claims to experiments, define independent / dependent variables, pin baselines, schedule…
SkillCFreshness: C★ 7Claude Code +9 - 0.530score
experiment-monitor
Shiien
Thin wrapper over `python -m harness ext-status` for monitoring external GPU runs.
SkillCFreshness: C★ 7Claude Code +9 - 0.530score
experiment-run
Shiien
Launch a training / experiment run.
SkillCFreshness: C★ 7Claude Code +9 - 0.469score
paper-lit-search
Shiien
Multi-source literature search — query arXiv + Semantic Scholar + the local `resources/papers/` library for a topic, dedupe across sources, rank by relevance…
SkillDFreshness: D★ 7Claude Code +9 - 0.469score
peer-review-correctness
Shiien
Stage 4 of the peer-review pipeline.
SkillDFreshness: D★ 7Claude Code +9 - 0.469score
peer-review-critique
Shiien
Stage 6 of the peer-review pipeline.
SkillDFreshness: D★ 7Claude Code +9 - 0.469score
peer-review-evaluations
Shiien
Stage 3 of the peer-review pipeline.
SkillDFreshness: D★ 7Claude Code +9
Signals
- Stars
- 7
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- Jul 14, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from Shiien
- 0.534score
checklist
Shiien
All checklist-tree write operations in one mode-based skill — CREATE (add a leaf or branch task with L2 sub-checklist / paper audit template), UPDATE (mark…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-dse
Shiien
Design-space exploration — systematic hyperparameter sweep over a search space defined by an `experiment-plan`.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
code
Shiien
Code-workflow scaffolding in one mode-based skill — BRANCH (git branch/worktree isolation with exp|feat|fix prefixes), ROADMAP (step-by-step implementation…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
experiment-plan
Shiien
Design the full experimental spec before any runs — map paper claims to experiments, define independent / dependent variables, pin baselines, schedule…
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
paper-read
Shiien
Read a research paper (arXiv link, PDF, or pasted text) and produce structured reading notes saved to resources/papers/.
SkillCFreshness: C★ 7Claude Code +9 - 0.534score
decision-analyze
Shiien
Structured analysis of a decision — for each option list pros/cons, effort, risk, milestone alignment, and reversibility, then produce a recommendation (user…
SkillCFreshness: C★ 7Claude Code +9
Done for you
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Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
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Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
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- founders on every account
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- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
experiment-analyze skill. Agent Mart. https://agentmart.dev/skills/shiien-experiment-analyze (last updated 2026-10-24)