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Agent skill

Schema Optimization Orchestrator

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Multi-phase schema optimization workflow orchestrator. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check: notesAgent Workflows

Install Schema Optimization Orchestrator

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill schema-optimization-orchestrator -a claude-code

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace schema-optimization-orchestrator --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/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/workspace/lab/schema-optimization .claude/skills/schema-optimization-orchestrator && 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
schema-optimization-orchestrator
GitHub stars
2.8k
Token cost
~1.7k tokens
SKILL.md length
278 words
Files
9 (incl. scripts, references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Multi-phase schema optimization workflow orchestrator. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 6 steps: Create Session Directory → Run Phase 1: Initial Schema Analysis → Run Phase 2: Field Utilization Analysis → …
  • Agent Workflows work in your project
  • SKILL.md covers Workflow Pattern, Inputs (JSON), Orchestration Steps and Output (JSON Only), plus 5 more sections
  • Runs Shell scripts from its folder

What it does

Schema Optimization Orchestrator is an agent skill from jeremylongshore/tons-of-skills-marketplace. Multi-phase schema optimization workflow orchestrator. Creates session directories, spawns phase agents sequentially, validates outputs, aggregates results. Trigger: "run schema optimization", "optimize schema workflow", "execute schema phases"

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts and reference files (for example `agents/phase_1.md`, `agents/phase_2.md` and `agents/phase_3.md`).

It sits in Agent Workflows. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “run schema optimization”
  • “optimize schema workflow”
  • “execute schema phases”
  • “/schema-optimization-orchestrator”

Requirements

  • A Bash shell
  • Pre-approved tools (allowed-tools): Read, Write, Bash, Task

Workflow steps

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

  1. Create Session Directory
  2. Run Phase 1: Initial Schema Analysis
  3. Run Phase 2: Field Utilization Analysis
  4. Run Phase 3: Impact Assessment
  5. Run Phase 4: Verification with Script
  6. Run Phase 5: Final Recommendations

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Bash
    • Task

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    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

Schema Optimization Orchestrator loads about 1.7k tokens when it runs, and up to ~6.8k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 278 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.8k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Bash, Task

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); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 278 words, ~1,705 tokens.

Download SKILL.mdSave it as .claude/skills/schema-optimization-orchestrator/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
schema-optimization-orchestrator
description
Multi-phase schema optimization workflow orchestrator. Creates session directories, spawns phase agents sequentially, validates outputs, aggregates results. Trigger: "run schema optimization", "optimize schema workflow", "execute schema phases"
allowed-tools
Read, Write, Bash, Task
version
1.0.0
license
MIT
author
Intent Solutions IO <jeremy@intentsolutions.io>

Schema Optimization Orchestrator

Runs a multi-phase schema optimization workflow with strict validation and evidence collection.

Workflow Pattern

This is a test harness pattern:

  • Creates isolated session directory per run
  • Spawns 5 phase agents sequentially
  • Each phase reads reference docs, runs scripts, writes reports
  • Validates JSON outputs and file artifacts
  • Aggregates final summary

Inputs (JSON)

json
{
  "skill_dir": "/absolute/path/to/.claude/skills/schema-optimization",
  "input_folder": "/path/to/bigquery/export",
  "extraction_type": "bigquery_json",
  "session_dir_base": ".claude/skills/schema-optimization/reports/runs"
}

Required:

  • skill_dir: Absolute path to this skill directory
  • input_folder: Path to data to analyze
  • extraction_type: Type of data extraction (e.g., "bigquery_json")

Optional:

  • session_dir_base: Where to create run directories (default: reports/runs)

Orchestration Steps

1. Create Session Directory
bash
TIMESTAMP=$(date +%Y-%m-%d_%H%M%S)
SESSION_DIR="${session_dir_base}/${TIMESTAMP}"
mkdir -p "${SESSION_DIR}"
2. Run Phase 1: Initial Schema Analysis

Spawn Phase 1 agent with:

json
{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/01-phase-1.md",
  "input_folder": "<input_folder>",
  "extraction_type": "<extraction_type>"
}

Expected output:

json
{
  "status": "complete",
  "report_path": "<SESSION_DIR>/01-initial-schema-analysis.md",
  "schema_summary": {
    "total_tables": 0,
    "total_fields": 0,
    "key_findings": []
  }
}

Validation:

  • JSON parse succeeds
  • status is "complete"
  • report_path file exists
  • schema_summary has required keys
3. Run Phase 2: Field Utilization Analysis

Spawn Phase 2 agent with:

json
{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/02-phase-2.md",
  "phase1_report_path": "<phase1_report_path>",
  "input_folder": "<input_folder>"
}

Expected output:

json
{
  "status": "complete",
  "report_path": "<SESSION_DIR>/02-field-utilization-analysis.md",
  "utilization_summary": {
    "unused_fields": [],
    "low_utilization_fields": [],
    "recommendations": []
  }
}
4. Run Phase 3: Impact Assessment

Spawn Phase 3 agent with:

json
{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/03-phase-3.md",
  "phase1_report_path": "<phase1_report_path>",
  "phase2_report_path": "<phase2_report_path>",
  "input_folder": "<input_folder>"
}

Expected output:

json
{
  "status": "complete",
  "report_path": "<SESSION_DIR>/03-impact-assessment.md",
  "impact_summary": {
    "high_risk_changes": [],
    "medium_risk_changes": [],
    "low_risk_changes": [],
    "estimated_savings": {}
  }
}
5. Run Phase 4: Verification with Script

Spawn Phase 4 agent with:

json
{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/04-phase-4-verify-with-script.md",
  "phase2_report_path": "<phase2_report_path>",
  "phase3_report_path": "<phase3_report_path>",
  "input_folder": "<input_folder>",
  "script_path": "<skill_dir>/scripts/analyze_field_utilization.sh",
  "output_folder_path": "<input_folder>"
}

Expected output:

json
{
  "status": "complete",
  "report_path": "<SESSION_DIR>/04-field-utilization-verification.md",
  "verification_summary": {
    "files_analyzed": 0,
    "conclusions_confirmed": [],
    "conclusions_revised": [],
    "unexpected_findings": [],
    "revised_action_items": []
  }
}
6. Run Phase 5: Final Recommendations

Spawn Phase 5 agent with:

json
{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/05-phase-5.md",
  "phase1_report_path": "<phase1_report_path>",
  "phase2_report_path": "<phase2_report_path>",
  "phase3_report_path": "<phase3_report_path>",
  "phase4_report_path": "<phase4_report_path>"
}

Expected output:

json
{
  "status": "complete",
  "report_path": "<SESSION_DIR>/05-final-recommendations.md",
  "recommendations_summary": {
    "priority_actions": [],
    "implementation_plan": [],
    "success_metrics": []
  }
}

Output (JSON Only)

json
{
  "status": "complete",
  "session_dir": "<SESSION_DIR>",
  "timestamp": "YYYY-MM-DD_HHMMSS",
  "phase_reports": {
    "phase1": "<SESSION_DIR>/01-initial-schema-analysis.md",
    "phase2": "<SESSION_DIR>/02-field-utilization-analysis.md",
    "phase3": "<SESSION_DIR>/03-impact-assessment.md",
    "phase4": "<SESSION_DIR>/04-field-utilization-verification.md",
    "phase5": "<SESSION_DIR>/05-final-recommendations.md"
  },
  "final_summary": {
    "total_tables": 0,
    "total_fields": 0,
    "unused_fields": 0,
    "optimization_opportunities": 0,
    "estimated_savings_pct": 0,
    "verification_status": "confirmed"
  }
}

Error Handling

If any phase fails:

  • Stop execution
  • Return error status with phase details
  • Preserve partial reports for debugging
json
{
  "status": "error",
  "failed_phase": 3,
  "error_message": "Phase 3 agent failed validation",
  "session_dir": "<SESSION_DIR>",
  "completed_phases": ["phase1", "phase2"]
}

Validation Rules

After each phase:

  1. Parse returned JSON (fail if invalid)
  2. Check status is "complete" (fail if not)
  3. Verify report_path exists on disk (fail if not)
  4. Validate phase-specific summary keys (fail if missing)

Implementation Notes

  • Use Task tool to spawn phase agents
  • Pass exact file paths (no wildcards)
  • Session directory must be absolute path
  • All reports must be written before returning
  • No terminal output except final JSON

Example Usage

User: "Run schema optimization on my BigQuery export"

Claude: [Creates session directory]
Claude: [Spawns Phase 1 agent]
Claude: [Validates Phase 1 output]
Claude: [Spawns Phase 2 agent with Phase 1 report]
Claude: [... continues through Phase 5]
Claude: [Returns final JSON summary]

Files Created Per Run

reports/runs/2025-01-15_143022/
├── 01-initial-schema-analysis.md
├── 02-field-utilization-analysis.md
├── 03-impact-assessment.md
├── 04-field-utilization-verification.md
└── 05-final-recommendations.md

Each file is evidence of work completed.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in workspace/lab/schema-optimization of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • agents/phase_1.md
  • agents/phase_2.md
  • agents/phase_3.md
  • agents/phase_4.md
  • agents/phase_5.md
  • references/01-phase-1.md
  • references/04-verify-with-script.md
  • scripts/analyze_field_utilization.sh

Open the folder on GitHubat commit cfae287

Compare with similar skills

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Using Superpowersfarm-fe/farm5.6k36 repos~1.4kAutomated safety check: PassMIT
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Categories

Questions about Schema Optimization Orchestrator

What does Schema Optimization Orchestrator do?

Multi-phase schema optimization workflow orchestrator. An agent skill from jeremylongshore/tons-of-skills-marketplace. Schema Optimization Orchestrator is an agent skill from jeremylongshore/tons-of-skills-marketplace. Multi-phase schema optimization workflow orchestrator.

When should I use Schema Optimization Orchestrator?

Schema Optimization Orchestrator fits situations like: agent Workflows work in your project.

How do I install Schema Optimization Orchestrator in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill schema-optimization-orchestrator -a claude-code`. Or copy the skill folder (workspace/lab/schema-optimization in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/schema-optimization-orchestrator in your project. Claude Code loads it when a task matches its description.

How do I install Schema Optimization Orchestrator in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill schema-optimization-orchestrator -a codex`. Or copy the skill folder (workspace/lab/schema-optimization in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/schema-optimization-orchestrator in your project. Codex loads it when a task matches its description.

Can I use Schema Optimization Orchestrator 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 jeremylongshore/tons-of-skills-marketplace --skill schema-optimization-orchestrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/schema-optimization-orchestrator, .gemini/skills/schema-optimization-orchestrator, .github/skills/schema-optimization-orchestrator and .opencode/skills/schema-optimization-orchestrator in your project.

What does Schema Optimization Orchestrator need to run?

Going by SKILL.md and its folder, Schema Optimization Orchestrator needs a shell for the scripts in its folder. Our summary lists: A Bash shell. Its frontmatter pre-approves these tools: Read, Write, Bash, Task.

Does Schema Optimization Orchestrator 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 Schema Optimization Orchestrator safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Schema Optimization Orchestrator use?

Schema Optimization Orchestrator is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Schema Optimization Orchestrator use?

About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.1k tokens, read only when the agent opens those files.

What are the alternatives to Schema Optimization Orchestrator?

Skills that share tags, products or a category with Schema Optimization Orchestrator: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Schema Optimization Orchestrator?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.