Artifact Management
ClineOfficialPublished by Cline
- Skill
- Guidelines
- Data and AI
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Artifact Management provides a standardized framework for persisting data analysis results beyond a single chat session. It emphasizes creating self-contained, reproducible packages that include the original query, raw data snapshots, visualizations, and contextual metadata. By following these guidelines, users can ensure that their analysis is transparent, critique-ready, and easily accessible for future decision-making or reporting.
What it does
- Defines a consistent directory structure for storing analysis artifacts.
- Provides templates for README files and metadata to capture analysis context.
- Establishes best practices for linking generated outputs to their source scripts.
- Outlines safety and privacy considerations for handling sensitive data exports.
- Standardizes the reporting of saved artifacts to the user.
When to use it
- When analysis results need to be shared, cited, or reproduced later.
- When generating non-trivial SQL queries or complex data visualizations.
- When an analysis is intended to support long-term business decisions or dashboards.
- When you need to provide a clear audit trail of assumptions, filters, and metrics used.
Advantages
- Ensures analysis is reproducible and verifiable by others.
- Reduces knowledge loss by documenting assumptions and caveats alongside data.
- Promotes consistency in how analysis outputs are organized and shared.
- Helps maintain a clear link between source code and final generated reports.
Usage examples
- “Save the results of this user churn analysis so the team can review it later.”
- The agent organizes the SQL, CSV results, and a summary into a dated folder, providing a README with reproduction steps and metadata.
- “I need to share this trend chart with my manager; how should I package it?”
- The agent creates an artifact package containing the chart image, the underlying data, and a markdown summary explaining the metric definitions and caveats.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/cline/artifact-management/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip artifact-management.zip -d .agents/skills
Documentation by Cline
Artifact Management
Use this skill when analysis outputs should persist beyond the current conversation.
When to store artifacts
Store analysis artifacts when:
the user asks to save, export, share, cite, or reproduce an analysis
a chart, report asset, or presentation-ready output is generated
the analysis may be reused for a decision, review, dashboard follow-up, or external-facing answer
the query result is non-trivial and the SQL, assumptions, or caveats matter for interpretation
Do not write files into a repository or long-lived location unless the user requested persistence or approved the destination.
What to store
Prefer storing enough context for someone to reproduce and critique the analysis later:
original user question or decision context
metric definitions, assumptions, population, grain, time window, filters, and exclusions
source models/tables and relevant data-dictionary references
final SQL or query source
result snapshot, such as CSV, JSON, Parquet, or a small markdown table
narrative summary, findings, evidence, confidence, and caveats
generated plot files and the underlying data used to create them
reproduction instructions, including commands or tool steps when helpful
creation timestamp and author/tool context when useful
Recommended local structure
Use a dated, human-readable slug for each analysis package:
analyses/
└── 2026-05-27-active-users-trend/
├── README.md
├── query.sql
├── result.csv
├── summary.md
├── chart.html
├── chart.png
└── metadata.jsonFor lightweight exports, a single CSV or markdown file is fine. For reusable or report-ready work, prefer the package structure above.
Reproducible generated artifacts
When an artifact is generated by code, preserve enough context to reproduce and safely modify it later:
Save the generator script or notebook alongside the generated outputs when practical.
Keep generated files reproducible from the saved script and source data.
If fixing a generated artifact, update the generator and regenerate the output. Avoid only hand-patching generated files, because that causes drift between the source script and artifact.
If a generated file must be manually patched, document that in
README.md,summary.md, ormetadata.jsonand state whether the generator is stale.Save validation steps that were performed, such as SQL row counts, JavaScript syntax checks, local render checks, or browser viewing notes.
For interactive HTML artifacts, include viewing instructions when browser security context may matter. For example:
cd path/to/artifact-directory
python3 -m http.server 8000Then open http://localhost:8000/chart.html instead of relying on file:// behavior.
If a lightweight chart evolves into a reusable or report-ready asset, upgrade the artifact package with a human-readable README.md that includes:
question and short answer
metric definition and definition provenance
source model/table and SQL/query notes
time window, grain, filters, and exclusions
artifact inventory
caveats and sensitivity notes
validation performed
reproduce/refresh instructions
README template
# Analysis title
Question: ...
Answer: ...
How I measured it:
- Metric: ...
- Population: ...
- Grain/window: ...
- Filters/exclusions: ...
- Source model/table: ...
Artifacts:
- `query.sql` — final query
- `result.csv` — result snapshot
- `summary.md` — findings and caveats
- `chart.html` / `chart.png` — visualization, if generated
Caveats:
- ...
Reproduce:
1. ...Metadata fields
When creating metadata.json, include fields like:
{
"title": "Active users trend",
"created_at": "2026-05-27T15:40:00-07:00",
"question": "...",
"time_window": "...",
"grain": "...",
"models": ["..."],
"metrics": ["..."],
"filters": ["..."],
"caveats": ["..."],
"artifacts": ["query.sql", "result.csv", "summary.md", "chart.html"]
}Safety and privacy
Avoid persisting sensitive raw rows unless they are necessary and explicitly requested.
Prefer aggregate, sampled, redacted, or anonymized outputs for shareable artifacts.
Make caveats visible next to exported numbers and charts.
Do not publish or upload artifacts to external services unless the user explicitly asks.
If storing in a shared repository, avoid secrets, credentials, private customer data, and overly broad raw exports.
Output reporting
After saving artifacts, report:
absolute or workspace-relative paths
what each artifact contains
any sensitivity caveats
how to reproduce or refresh the analysis
Boundaries
This skill does not replace a BI tool, governed dashboards, documented metric definitions, or a source-of-truth reporting system. Treat persisted artifacts as snapshots unless they are backed by documented models and an agreed refresh process.
Files
- ATTRIBUTION.mdAdded by AutoGineer864 B
- LICENSELicence file added by AutoGineer10.5 KiB
- SKILL.md5.2 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
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Attribution and licence
- Published by
- Cline
- Publisher
- Cline (Official)
- Source
- cline/skills · skills/data-analyst/skills/artifact-management · commit 2637846
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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