How do I install the Op1 Marketing SEO Experiment Stage1 skill?

Run npx skillmds@latest add tomevault-io/op1-marketing-seo-experiment-stage1 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Which AI agents work with Op1 Marketing SEO Experiment Stage1?

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Is Op1 Marketing SEO Experiment Stage1 free to use?

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Who published Op1 Marketing SEO Experiment Stage1?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Op1 Marketing SEO Experiment Stage1 by tomevault-io

Name: Op1 Marketing SEO Experiment Stage1
Author: tomevault-io

Op1 Marketing SEO Experiment Stage1

Run and audit OperatorOne Stage1 marketing SEO shadow experiments across input sync, context index, keyword graph, experiment synthesis, and Ready/Hold/Drop queueing. Use when asked to run SEO experiments, execute 1/2/3 validation rounds, verify reproducibility, or summarize queue and scoreboard outputs for op1_marketing. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

Stage1 SEO Experiment Runner (Shadow)

Objective

Run the Stage1 SEO pipeline in shadow mode and produce auditable artifacts under research/stage1_marketing_seo/.

Single-cycle run

From workspace root:

./scripts/run_marketing_seo_stage1.sh --mode shadow --force

Check:

research/stage1_marketing_seo/run.latest.json
research/stage1_marketing_seo/experiments.queue.latest.json
research/stage1_marketing_seo/scoreboard.latest.json
research/stage1_marketing_seo/decision_log.latest.md

1/2/3 validation run

Use the bundled helper:

python3 {baseDir}/scripts/run_rounds_123.py

This executes:

Round1 baseline
Round2 controlled handoff perturbation
Round3 baseline restore + rerun

Then it writes snapshot artifacts and validation checks to:

research/stage1_marketing_seo/rounds/<timestamp>/rounds_1_2_3.report.md
research/stage1_marketing_seo/rounds/<timestamp>/rounds_1_2_3.report.json

Expected checks:

R1 == R3 for queue/content reproducibility
R2 != R1 for input-sensitivity

Guardrails

Keep mode as shadow for Stage1 capability validation.
Treat this as experiment generation/scoring/queueing only (not auto publish).
If run status is blocked_input_incomplete, stop and report missing requirements from run.latest.json.input_sync.completeness.

References

references/stage1-output-contract.md
references/analysis-checklist.md

Source: Daihaolin201/OperatorOne — distributed by TomeVault.

---name: op1-marketing-seo-experiment-stage1description: Run and audit OperatorOne Stage1 marketing SEO shadow experiments across input sync, context index, keyword graph, experiment synthesis, and Ready/Hold/Drop queueing. Use when asked to run SEO experiments, execute 1/2/3 validation rounds, verify reproducibility, or summarize queue and scoreboard outputs for op1_marketing. Use when this capability is needed.---# Stage1 SEO Experiment Runner (Shadow)## ObjectiveRun the Stage1 SEO pipeline in shadow mode and produce auditable artifacts under `research/stage1_marketing_seo/`.## Single-cycle runFrom workspace root:```bash./scripts/run_marketing_seo_stage1.sh --mode shadow --force```Check:- `research/stage1_marketing_seo/run.latest.json`- `research/stage1_marketing_seo/experiments.queue.latest.json`- `research/stage1_marketing_seo/scoreboard.latest.json`- `research/stage1_marketing_seo/decision_log.latest.md`## 1/2/3 validation runUse the bundled helper:```bashpython3 {baseDir}/scripts/run_rounds_123.py```This executes:1. Round1 baseline2. Round2 controlled handoff perturbation3. Round3 baseline restore + rerunThen it writes snapshot artifacts and validation checks to:- `research/stage1_marketing_seo/rounds/<timestamp>/rounds_1_2_3.report.md`- `research/stage1_marketing_seo/rounds/<timestamp>/rounds_1_2_3.report.json`Expected checks:- `R1 == R3` for queue/content reproducibility- `R2 != R1` for input-sensitivity## Guardrails- Keep mode as `shadow` for Stage1 capability validation.- Treat this as experiment generation/scoring/queueing only (not auto publish).- If run status is `blocked_input_incomplete`, stop and report missing requirements from `run.latest.json.input_sync.completeness`.## References- `references/stage1-output-contract.md`- `references/analysis-checklist.md`---> Source: [Daihaolin201/OperatorOne](https://github.com/Daihaolin201/OperatorOne) — distributed by [TomeVault](https://tomevault.io).

Frequently asked questions