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npxcnency-uxnpxcnency-ux/da-agent/skills/execute-auto/SKILL.md

execute-auto

Skill0 starsChanged 8 months agoMITView on GitHub

Automatically execute simple aggregation tasks end-to-end. Use for complexity score < 3.

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    1. VladTheProgrammer/claude-asimov★ 0same name

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  • In this repositoryOne other file it gives its agents.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use CodeGenerator to create Python code from template:

    line 24
  2. Distinctive:

    Before execution, show the generated code:

    line 46
  3. Distinctive:

    Only simple aggregations (top N, groupby)

    line 138

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Execute Auto Skill

Fully automated execution for simple tasks

Purpose

Execute simple aggregation tasks end-to-end without human intervention.

Trigger: Complexity score < 3

Supported Tasks:

  • Top N queries ("show me top 10 users by revenue")
  • Group-by aggregations ("total sales by region")

Workflow

Phase 1: Generate Code

Use CodeGenerator to create Python code from template:

from lib.code_generator import CodeGenerator
from pathlib import Path

templates_dir = Path(__file__).parent.parent / "assets" / "sql_templates"
generator = CodeGenerator(templates_dir)

task = {
    "analysis_type": "aggregation",
    "operation": "top_n",
    "data_source": structured_req["data_sources"][0],
    "sort_by": structured_req["sort_by"],
    "limit": structured_req.get("limit", 10)
}

code = generator.generate(task)

Phase 2: Show Code to User

Before execution, show the generated code:

Generated code:

[paste code here]

This will:
1. Read data from [file]
2. Sort by [column]
3. Return top [N] results
4. Save to outputs/result.csv

Execute? (y/n)

Phase 3: Execute Code

If user approves:

from lib.executor import Executor
import os

executor = Executor(Path(os.getcwd()))

try:
    output = executor.execute(code, timeout=30)
    print(output)
except ExecutionError as e:
    print(f"Execution failed: {e}")
    print("Please check your data file and try again.")

Phase 4: Present Results

✓ Analysis complete!

Results saved to: outputs/result.csv

[Show preview of results]

Would you like me to:
A) Generate a summary report
B) Save this to knowledge base
C) Done

Phase 5: Save to Knowledge Base

from lib.knowledge_store import KnowledgeStore
from pathlib import Path

kb_dir = Path.home() / ".da-agent" / "knowledge"
kb_dir.mkdir(parents=True, exist_ok=True)
kb = KnowledgeStore(str(kb_dir / "kb.db"))

task_id = kb.save_task(
    description=original_request,
    structured_req=structured_req,
    complexity_score=score,
    execution_mode="auto"
)

Error Handling

File not found:

Error: Cannot find data file 'users.csv'
Please check:
- File exists in current directory
- Filename is correct
- You have read permissions

Invalid column:

Error: Column 'revenue' not found in data
Available columns: [list columns]

Security error:

Error: Cannot access file outside workspace
Please copy the file to current directory

Phase 1 Limitations

  • Only simple aggregations (top N, groupby)
  • Single file only (no joins)
  • No charts/visualizations
  • Basic text output only

Phase 2+ will add:

  • Multi-file joins
  • Charts and visualizations
  • Rich HTML reports
  • More analysis types

Download now (ZIP)

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