Agent skill

Run Analysis

by xinzhuwang-wxz in xinzhuwang-wxz/OpenPE

Initialize and run the full automated analysis pipeline from analysis question to final documentation

GPL-3.0Auto-check passed

Install Run Analysis

skills CLI
$ npx skills add xinzhuwang-wxz/OpenPE --skill run-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install xinzhuwang-wxz/OpenPE run-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/xinzhuwang-wxz/OpenPE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/run-analysis .claude/skills/run-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
run-analysis
GitHub stars
101
Token cost
~2.7k tokens
SKILL.md length
1,162 words
Files
1
Skills in repo
5
Repo updated
First seen
Licence
GPL-3.0

At a glance

Initialize and run the full automated analysis pipeline from analysis question to final documentation

  • Works in 6 steps: Parse Inputs → Read Methodology → Scaffold the Analysis Directory → …
  • SKILL.md covers Step 1: Parse Inputs, Step 2: Read Methodology, Step 3: Scaffold the Analysis… and Step 4: Write…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Run Analysis is an agent skill from xinzhuwang-wxz/OpenPE. Initialize and run the full automated analysis pipeline from analysis question to final documentation

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is GPL-3.0.

Example prompts

  • “/run-analysis”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Parse Inputs
  2. Read Methodology
  3. Scaffold the Analysis Directory
  4. Write analysis_config.yaml
  5. Initialize STATE.md
  6. Execute the Pipeline

What it can do on your machine

Read from SKILL.md and the folder at commit f29b438. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash, yaml and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Run Analysis loads about 2.7k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 1,162 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from xinzhuwang-wxz/OpenPE at commit f29b438, republished under its GPL-3.0 licence (© xinzhuwang-wxz). 1,162 words, ~2,719 tokens.

Download SKILL.mdSave it as .claude/skills/run-analysis/SKILL.md (or your agent's skills folder).
name
run-analysis
description
Initialize and run the full automated analysis pipeline from analysis question to final documentation
user-invocable
true

/run-analysis -- Main Analysis Pipeline Entry Point

You are the pipeline orchestrator. You manage the analysis by spawning specialist agents, running review cycles, tracking state, and advancing through phases. You do NOT perform analysis work yourself.

Arguments: $ARGUMENTS

The argument is either:

  • An analysis question as inline text, OR
  • A path to a .md file containing the analysis question, OR
  • A path to a .md question followed by a path to a .yaml config file

Step 1: Parse Inputs

  1. If $ARGUMENTS contains a path ending in .md, read that file as the analysis question. Otherwise, treat the full argument text as the question.
  2. If $ARGUMENTS contains a path ending in .yaml, read that as the analysis config. Otherwise, you will create a default config in Step 4.
  3. Derive a short snake_case analysis_name from the analysis question.

Step 2: Read Methodology

Read the following files to understand the phase requirements and orchestration protocol:

  • src/methodology/03-phases.md -- what each phase must produce
  • src/methodology/04-verification.md -- verification protocol and human gate
  • src/methodology/06-review.md -- review tiers and iteration rules
  • orchestration/agents.md -- agent session definitions
  • orchestration/automation.md -- automation pseudocode
  • orchestration/sessions.md -- session isolation and naming
  • CLAUDE.md -- your operating model

Step 3: Scaffold the Analysis Directory

Run the scaffolder to create the analysis directory structure:

bash
pixi run scaffold analyses/{analysis_name} --type {measurement|search}

Choose measurement or search based on the analysis question (searches test for new phenomena; measurements quantify known processes).

The scaffolder creates the full directory tree under analyses/{analysis_name}/ including all phase directories, review directories, experiment logs, and a conventions/ symlink pointing to the shared conventions library.

Copy the analysis question into analyses/{analysis_name}/prompt.md.

Step 4: Write analysis_config.yaml

If no config was provided, create analyses/{analysis_name}/analysis_config.yaml with:

yaml
analysis_name: {analysis_name}
physics_prompt_path: prompt.md
model_tier: auto
channels: []  # populated during Phase 1
calibrations: []  # populated during Phase 1
data_dir: ""  # USER MUST SET THIS -- path to input data files
cost_controls:
  max_review_iterations: 10
  review_warn_threshold: 3
verification:
  active: true
  approved_for_unverification: false
pixi:
  environment: default
  workflow_prefix: "pixi run"

If a config was provided, copy it to analyses/{analysis_name}/analysis_config.yaml, ensuring at minimum the verification, cost_controls, and pixi sections exist.

IMPORTANT: After writing the config, remind the user:

"analysis_config.yaml has been created. Please set data_dir to the path containing your input data files before proceeding with Phase 1 execution."

Step 5: Initialize STATE.md

Write analyses/{analysis_name}/STATE.md:

markdown
# Analysis State

- **Analysis**: {analysis_name}
- **Current phase**: 1
- **Status**: initialized
- **Last updated**: {current timestamp}

## Phase History

| Phase | Status | Artifact | Review | Iterations | Notes |
|-------|--------|----------|--------|------------|-------|

## Blockers
- (none)

## Regression Log
- (none)

Initialize analyses/{analysis_name}/regression_log.md as empty.

Step 6: Execute the Pipeline

Now execute the full pipeline. At each phase transition, update STATE.md with the current phase, status, and timestamp. All agents must read applicable files from the conventions/ symlink in the analysis directory for coding standards, plotting requirements, and naming conventions.

Phase 1: Strategy
  1. Update STATE.md: phase=1, status=executing
  2. Spawn lead-analyst agent via SendMessage:
    • Task: Execute Phase 1 (Strategy)
    • Inputs: prompt.md, src/methodology/03-phases.md (Phase 1 section), analysis_config.yaml
    • Must read: applicable conventions/ files for naming and coding standards
    • Working directory: analyses/{analysis_name}/phase1_strategy/
    • Output: exec/STRATEGY.md, append to experiment_log.md, code in scripts/, figures in figures/
    • All scripts must use pixi run for execution
  3. Update STATE.md: status=reviewing
  4. Run 4-bot review by invoking /review-phase with phase "1"
  5. On PASS: update STATE.md (phase=1, status=passed), record artifact and review info in Phase History table
  6. Advance to Phase 2
Phase 2: Exploration
  1. Update STATE.md: phase=2, status=executing
  2. Spawn three agents in parallel via SendMessage:
    • data-explorer: inventory samples, check data quality
    • domain-specialist: validate domain model, variable definitions
    • domain-scout: survey domain knowledge, expected relationships, baselines
    • All read: prompt.md, phase1_strategy/exec/STRATEGY.md (latest), src/methodology/03-phases.md (Phase 2 section)
    • Must read: applicable conventions/ files
    • All write to: analyses/{analysis_name}/phase2_exploration/
    • All scripts must use pixi run for execution
  3. After all three complete, spawn lead-analyst to consolidate their outputs into exec/EXPLORATION.md
  4. Phase 2 is self-review -- no external review needed
  5. Update STATE.md: phase=2, status=passed
  6. Advance to Phase 3
Phase 3: Selection and Background Modeling
  1. Update STATE.md: phase=3, status=executing
  2. Read analysis_config.yaml to check for defined channels
    • If channels are populated (multi-channel): create per-channel subdirectories under phase3_selection/channel_{name}/ with experiment_log.md, sensitivity_log.md, scripts/, figures/, exec/, review/critical/
    • If no channels defined: work in phase3_selection/ directly
  3. For each channel (or the single analysis), spawn agents in parallel:
    • signal-lead: implement event selection, define regions
    • background-estimator: estimate backgrounds, perform closure tests
    • Inputs: prompt.md, STRATEGY.md, EXPLORATION.md, src/methodology/03-phases.md (Phase 3 section)
    • Must read: applicable conventions/ files
    • Output: exec/SELECTION.md (or exec/SELECTION_{CHANNEL}.md)
    • All scripts must use pixi run for execution
  4. Update STATE.md: status=reviewing
  5. Run 1-bot review per channel by invoking /review-phase with phase "3" (includes plot-validator)
  6. On PASS (all channels): update STATE.md (phase=3, status=passed)
  7. Advance to Phase 4a
Show full SKILL.md (482 more words)Show less
Phase 4a: Expected Results
  1. Update STATE.md: phase=4a, status=executing
  2. Spawn systematic-source-evaluator agents in parallel (one per systematic source identified in the strategy)
    • Each evaluates one systematic uncertainty source
    • Inputs: STRATEGY.md, SELECTION.md, src/methodology/03-phases.md (Phase 4a section)
    • Must read: applicable conventions/ files
    • All scripts must use pixi run for execution
  3. After all complete, spawn systematics-fitter:
    • Constructs the statistical model, runs Asimov fits, signal injection tests
    • Output: exec/INFERENCE_EXPECTED.md
  4. Update STATE.md: status=reviewing
  5. Run 4-bot review by invoking /review-phase with phase "4a" (includes plot-validator)
  6. On PASS: update STATE.md (phase=4a, status=passed)
  7. Advance to Phase 4b
Phase 5: Partial Verification
  1. Update STATE.md: phase=4b, status=executing
  2. Spawn systematics-fitter:
    • Runs fit on 10% SR data subsample
    • Output: exec/INFERENCE_PARTIAL.md
    • Must use pixi run for execution
  3. Spawn note-writer:
    • Produces draft analysis note: exec/ANALYSIS_NOTE_DRAFT.md
    • Produces verification checklist: exec/VERIFICATION_CHECKLIST.md
    • Must read: applicable conventions/ files for document formatting
  4. Update STATE.md: status=reviewing
  5. Run 4-bot review by invoking /review-phase with phase "4b" (includes plot-validator)
  6. On PASS: update STATE.md (phase=4b, status=human_gate)
  7. PAUSE the pipeline. Report to the user:
    • "Phase 5 review passed. The draft analysis note, verification checklist, and review results are ready for human review."
    • "Run /approve-verification to review and approve or reject full verification."
    • Do NOT proceed to Phase 4c automatically.
After Human Approval (Phase 4c)

When the user runs /approve-verification and approves:

  1. Confirm analysis_config.yaml has approved_for_unverification: true
  2. Update STATE.md: phase=4c, status=executing
  3. Spawn systematics-fitter:
    • Runs full fit on complete dataset
    • Output: exec/INFERENCE_OBSERVED.md
    • Must use pixi run for execution
  4. Spawn cross-checker:
    • Validates results, checks consistency with partial and expected
  5. Update STATE.md: status=reviewing
  6. Run 1-bot review by invoking /review-phase with phase "4c" (includes plot-validator)
  7. On PASS: update STATE.md (phase=4c, status=passed)
  8. Advance to Phase 5
Phase 5: Documentation
  1. Update STATE.md: phase=5, status=executing
  2. Spawn note-writer:
    • Updates draft note with full observed results
    • Inputs: all phase artifacts, ANALYSIS_NOTE_DRAFT.md, INFERENCE_OBSERVED.md
    • Must read: applicable conventions/ files for document and figure formatting
    • Output: exec/ANALYSIS_NOTE.md
  3. Update STATE.md: status=reviewing
  4. Run 5-bot review by invoking /review-phase with phase "5" (physics + critical + constructive + rendering + arbiter, with plot-validator)
  5. On PASS: update STATE.md (phase=5, status=passed, overall=COMPLETE)
  6. Report: "Analysis complete. Final analysis note: analyses/{analysis_name}/phase5_documentation/exec/ANALYSIS_NOTE.md"

State Update Protocol

Every time you transition between states, update STATE.md with:

  • The current phase number
  • The current status (executing, reviewing, passed, blocked, human_gate)
  • A timestamp
  • The Phase History table row for completed phases

Regression Handling

After any review PASS, check the review output for regression triggers. If found:

  1. Update STATE.md: status=regression
  2. Spawn an investigator agent to produce REGRESSION_TICKET.md
  3. Re-run the origin phase executor with the ticket
  4. Re-review at the original tier
  5. Re-run all affected downstream phases
  6. Log in regression_log.md

Cost Controls

  • Track review iteration counts per phase
  • Warn at review_warn_threshold (default 3) iterations
  • Hard cap at max_review_iterations (default 10) -- escalate to human
  • If a phase executor appears stuck, produce best-effort artifact and proceed to review

© xinzhuwang-wxz, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/run-analysis of xinzhuwang-wxz/OpenPE.

Open the folder on GitHubat commit f29b438

Compare with similar skills

Run Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Run Analysis

What does Run Analysis do?

Initialize and run the full automated analysis pipeline from analysis question to final documentation. Run Analysis is an agent skill from xinzhuwang-wxz/OpenPE.

How do I install Run Analysis in Claude Code?

Run `npx skills add xinzhuwang-wxz/OpenPE --skill run-analysis -a claude-code`. Or copy the skill folder (.claude/skills/run-analysis in xinzhuwang-wxz/OpenPE) into .claude/skills/run-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Run Analysis in Codex?

Run `npx skills add xinzhuwang-wxz/OpenPE --skill run-analysis -a codex`. Or copy the skill folder (.claude/skills/run-analysis in xinzhuwang-wxz/OpenPE) into .agents/skills/run-analysis in your project. Codex loads it when a task matches its description.

Can I use Run Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xinzhuwang-wxz/OpenPE --skill run-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/run-analysis, .gemini/skills/run-analysis, .github/skills/run-analysis and .opencode/skills/run-analysis in your project.

What does Run Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Run Analysis is instructions for the agent only.

Does Run Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Run Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Run Analysis use?

Run Analysis is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Run Analysis use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Run Analysis?

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Who maintains Run Analysis?

xinzhuwang-wxz (a GitHub user) maintains it in xinzhuwang-wxz/OpenPE, which has 101 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on April 2, 2026.

Source: xinzhuwang-wxz/OpenPE on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.