MCPInstall

ae-experiment

Use when managing Atlas AB or external experiments, traffic layers, Features, metrics, buckets, and experiment reports through ae-cli

Installs
315
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/thinkingaiagenticengine/ae-cli --skill ae-experiment
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads ae-experiment automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

ae-experiment

AE CLI (ae-cli) exposes Atlas AB Experiment capabilities through the experiment domain.

Global Rules

  • Prefer ae-cli experiment <command> for Atlas AB Experiment work.
  • Use --project-id / -p for project-scoped commands.
  • Use --req JSON for complex save, status, and delete DTOs.
  • Do not invent experiment IDs, traffic layer IDs, bucket IDs, Feature keys, metric IDs, or payload field names.
  • Bind only metric IDs returned by experiment metric list; create and verify a missing metric before saving the experiment.
  • Read commands can run directly after IDs are verified.
  • Write commands require explicit user intent and normally keep the confirmation prompt. Use --dry-run before write calls when composing JSON.

Naming and response boundary:

  • CLI command segments and flags use kebab-case.
  • Outer Capability input and all response keys use snake_case.
  • Nested business DTOs passed through --req keep their native camelCase fields.
  • CRITICAL: save build-guide / save validate responses recursively snake_case example_args.req. Never copy those keys into --req. Use camelCase (expName, metricId, …). Authoritative names: ae-cli capability inspect experiment.experiment.save (or the matching final save id) → input_schema.properties.req. save validate valid: true is not a final-save schema pass — snake_case req can still fail on experiment … save.
  • Audience QP is semantic at the CLI boundary: write targeting.definitionRequest; read targeting.definition_request. Never generate or submit targetConfig.
  • Metric QP is semantic at the CLI boundary: write metricDefinition; read metric_definition. Never generate or submit metricConfig, calcType, or Axxx codes.
  • Event-property comparisons belong in metricDefinition.filters, using {relation,items:[{field,operator,values}]}. Never put comparison fields directly on metricDefinition, and never submit raw filts, calcuSymbol, or ftv fields.
  • For metric aggregations sum / avg / max / distinct_count, metricDefinition.property is required and must be a concrete available event property from Analysis metadata. Never create property aggregations without property (for example metric_avg_no_property_*).
  • Preset event aggregations total_count / user_count / active_days omit property.
  • Experiment metric creation does not support avg_per_user / A105.
  • Resolve event and property names with Analysis metadata before saving semantic definitions.
  • Lists return data.items and data.total; detail commands return data.item.
  • Readiness returns data.readiness; reports return data.report; save guides return data.guide; save dry-run validation returns data.validation; writes return data.result.
  • Query cancellation returns data.success.

Typical Workflow

  1. Discover reusable assets:
    • experiment bucket list
    • experiment traffic-layer list
    • experiment feature list
    • experiment metric list
  2. Create missing assets if needed:
    • experiment save build-guide --operation-mode save_metric when save validation fails or req shape is unclear
    • experiment save validate --operation-mode save_metric --req '{...}' before retrying a failed save
    • experiment traffic-layer save
    • experiment feature save
    • experiment metric save
  3. Create or patch the experiment draft with experiment experiment save. Use experiment experiment update-metrics when replacing metric bindings or assigning guardrail roles.
  4. For an external experiment, use experiment external-experiment save-submit instead. It submits directly and does not use Features, traffic layers, allocation, targeting, or experiment cycles.
  5. Check readiness with experiment experiment ready-check.
  6. For a non-mutex traffic layer, run experiment experiment conflict-check before submit (needs feature_key_list from context or experiment get).
  7. Move status with experiment experiment manage.
  8. Query reports with experiment report summary, experiment report sample-size, and experiment report metric-trend.

If an experiment save returns error_code: METRIC_NOT_FOUND, list metrics for the same project. Create and verify the metric before retrying; never retry with another invented ID. Metric deletion returns error_code: METRIC_IN_USE while an active experiment binding exists.

Parameter Conventions

  • Experiment save payloads distinguish two allocation fields: experiment-level req.allocation (integer only; no decimals) and group-level req.groups[].allocation (integer only; sum must equal 100 exactly).
  • Public experiment metric roles are primary, secondary, and guardrail. The internal observation role is currently unavailable for saves. Guardrail is a binding role; updating metrics replaces the full binding list.
ae-cli experiment experiment get --project-id 1 --exp-id exp_123
ae-cli experiment experiment save --project-id 1 --req '{"expName":"Demo"}' --dry-run
ae-cli experiment metric save --project-id 1 --req '{"metricId":"payment_gold_sum","metricName":"Payment gold sum","createType":"event","goalDirection":"up","metricDesc":"Gold sum for payments above 10","metricDefinition":{"type":"event","event":"payment","aggregation":"sum","property":"gold","filters":{"relation":"and","items":[{"field":"gold","operator":"gt","values":[10]}]}}}' --dry-run
ae-cli experiment report metric-trend --project-id 1 --exp-id exp_123 --metric-id metric_1 --start-time 2026-07-01 --end-time 2026-07-07

Optional global parameters work as in other domains: --host, --mcp-url, --format, --jq, --dry-run, and --yes.

References

Open the matching file in references/ before using a command, especially for write operations and JSON payloads.

Save Helpers

experiment save build-guide, experiment save validate

When a save command returns next_tool: experiment.save.build-guide, call the guide first, then experiment save validate, then retry the final save capability.

Read save_build_guide.md and save_validate.md before using these helpers. Rebuild --req in camelCase from inspect / skill references; do not paste example_args.req.

Experiment

experiment experiment save, experiment experiment update-metrics, experiment external-experiment save-submit, capability run experiment.experiment.save-submit, experiment experiment list, experiment experiment list-archived, experiment experiment get, experiment experiment ready-check, experiment experiment conflict-check, experiment experiment manage, experiment experiment update-group, experiment experiment batch-delete, experiment operation-log query

Read manage_guardrail_metrics.md before assigning or replacing metric roles.

External Experiment

experiment external-experiment save-submit

Read save_submit_external_experiment.md before creating or updating an external experiment.

Traffic Layer and Buckets

experiment traffic-layer save, experiment traffic-layer get, experiment traffic-layer list, experiment traffic-layer batch-delete, experiment bucket list

Reports

experiment report summary, experiment report sample-size, experiment report metric-trend, capability run experiment.query.cancel

Metric and Feature

experiment metric save, experiment metric get, experiment metric list, experiment metric delete, experiment feature save, experiment feature update-status, experiment feature get, experiment feature list, experiment feature version-list, experiment feature operation-log query, experiment feature batch-delete, experiment feature whitelist list, experiment feature whitelist save, experiment feature whitelist update-status, experiment feature whitelist batch-delete

Read manage_feature_whitelist.md before querying or changing Feature whitelist rules.

FAQ

What does the ae-experiment skill do?

Use when managing Atlas AB or external experiments, traffic layers, Features, metrics, buckets, and experiment reports through ae-cli

How do I install ae-experiment?

Run npx skills add https://github.com/thinkingaiagenticengine/ae-cli --skill ae-experiment in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ae-experiment/ (personal) or .claude/skills/ae-experiment/ (project).

Which AI agents can use ae-experiment?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is ae-experiment safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains ae-experiment?

It is published by thinkingaiagenticengine in thinkingaiagenticengine/ae-cli. It has 315 recorded installs.

More from thinkingaiagenticengine/ae-cli

View all →

ae-analysis

thinkingaiagenticengine/ae-cli

Use ae-cli for AE analysis-side data questions, asset operations, and asset governance: reports, analysis boards, BI dashboards, ad-hoc models, drilldown, detail data, alerts, clusters, tags, metrics, metadata discovery, project configuration, tracking plans, local input-file upload, governance asset lists/rules/lineage/impact/dependency, batch asset operations, projects, and resource links. Use when the user asks to query data, explain a change, export evidence, inspect/create/update/govern analysis assets, recommend project assets for certification/authentication, review asset-certification recommendations, review metric recommendations, or build/update/refresh/sync a project semantic knowledge base from a governed asset package.

ae-engage

thinkingaiagenticengine/ae-cli

AE Engage capability gateway: config center, flows, push/config channels, strategies, templates, task management, operation activities, and query lifecycle. Trigger words: config center, scene config, push channel, config channel, operation strategy, operation task, operation activity, query lifecycle, template, config item, Engage, Hermes, engage-scene, engage-setting, engage-flow, engage-task, engage-activity, engage-query.

ae-dataops

thinkingaiagenticengine/ae-cli

Use ae-cli for DataOps warehouse tables/views and fields, recycling, workflow configuration and release, execution troubleshooting, backfill, SQL queries, and data integration.

Data & Databases870 installs

ae-community

thinkingaiagenticengine/ae-cli

AE community analysis and data reporting for posts, comments, topics, livestreams, customer-service chats, WeCom after-sales groups, and in-game chat. Use for community content insight, sentiment, intent, evidence drill-down, community data ingestion/import/submission, and WeCom chat data integration (社区数据上报、导入、提交社区数据、企微聊天数据接入).

Media & Creative865 installs

ae-agent

thinkingaiagenticengine/ae-cli

AE Agent platform CLI for Agent, resource package import and export, notification, approval, archived conversation, model, MCP, Skill, attachment, and user-memory work. Use when managing these resources, browsing Agent markets, handling approval requests and tasks, restoring archived conversations, persisting user memory, or answering from user preferences, background, stable workflows, or historical conventions.

AI & Agents854 installs

ae-kb

thinkingaiagenticengine/ae-cli

AE knowledge base CLI manual for creating, importing read-only compiled snapshots, querying, LLM-powered ask, listing accessible knowledge bases and their sources, deterministic index/grep/read retrieval, checking status, ZIP source upload and directory management, raw child-file reading, revision-checked child updates and deletion, compiling, schema generation, URL sources, source deletion, ordinary text source creation, full reads and revision-checked editing, file/ZIP source draft replacement and restoration, and knowledge base deletion. Use when the user asks to manage AE (Agentic Engine) knowledge bases, import a compiled Markdown ZIP snapshot, upload documents or URLs to a knowledge base, query knowledge, ask knowledge bases with an LLM, list accessible knowledge bases or source metadata, inspect knowledge base indexes, search knowledge base pages, read a specific knowledge base page, check knowledge base status, generate schemas, compile knowledge, remove sources, create or edit

AI & Agents853 installs

Popular other skills

View category →

hyperframes-cli

heygen-com/hyperframes

The HyperFrames CLI by job - scaffold, check, look at frames, edit the timeline, preview, render locally or in the cloud, publish, project history - plus reading its JSON and recovering from build or render failures.

1.3M installs

triage

mattpocock/skills

Move issues and external PRs through a state machine of triage roles, categorise, verify, grill if needed, and write agent-ready briefs.

1M installs

ask-matt

mattpocock/skills

Ask which skill or flow fits your situation. A router over the skills in this repo.

778.4K installs

to-questionnaire

mattpocock/skills

Turn a decision you can't fully answer into a questionnaire for someone else to fill in.

508.9K installs

wait-what

mattpocock/skills

Stop. That last message did not land: re-pitch it.

459.7K installs

loop-me

mattpocock/skills

Grill me about specs for the workflows I want to build, within this workspace.

447.9K installs