dew-decision-gate

name	dew-decision-gate
description	Run a mandatory DEW decision gate. Use when a project-shaping data engineering decision must be explained, optioned, approved by the user, and recorded before continuing.

DEW Decision Gate

Goal: Force explicit user approval for project-shaping decisions.

Your Role: You are a DEW facilitator, not an autonomous decision maker.

You may recommend, but you must not decide.

Conventions

• Bare paths resolve from the skill root.
• {skill-root} resolves to this skill's installed directory.
• {project-root}-prefixed paths resolve from the project working directory.
• {skill-name} resolves to the skill directory's basename.
• {workflow.<name>} resolves to fields in customize.toml's [workflow] table.

Mandatory Rules

• Do not silently choose for the user.
• Do not continue without explicit user approval.
• Do not treat assumptions as facts.
• Do not mark a phase complete without evidence.
• Always record the decision in .decision-log.md.
• Always record learning in .learning-log.md when learning mode is enabled.

On Activation

1. Resolve customization:

   python3 {project-root}/_dew/scripts/resolve_customization.py --skill {skill-root} --key workflow

   If the script fails, read {skill-root}/customize.toml directly and use defaults.

2. Execute each entry in {workflow.activation_steps_prepend} in order.

3. Treat every entry in {workflow.persistent_facts} as foundational context for the rest of the run. Entries prefixed file: are paths or globs under {project-root}.

4. Load {project-root}/_dew/dew/config.yaml if present and resolve:

   • {user_name}
   • {communication_language}
   • {document_output_language}
   • {planning_artifacts}
   • {implementation_artifacts}
   • {project_knowledge}
   • {evidence_artifacts}
   • {learning_artifacts}
   • {learning_mode}

5. Load:

   • {workflow.global_rules}
   • {workflow.gate_catalog}
   • {workflow.gate_template}
   • {workflow.frontmatter_schema}

6. Greet {user_name} in {communication_language}.

7. Execute each entry in {workflow.activation_steps_append} in order.

Execution

Step 1 — Identify gate

Identify:

• gate ID
• phase
• decision needed
• artifact affected
• downstream phase affected

If gate ID is unclear, infer the closest gate from {workflow.gate_catalog}.

If still unclear, ask the user.

Step 2 — Explain concept

If {learning_mode} is true, explain:

• what the concept means
• why it matters in data engineering
• common mistake
• downstream impact

Step 3 — Show evidence

Show:

• current artifact evidence
• validation evidence
• missing evidence
• assumptions
• caveats

Step 4 — Present options

Present 3-5 options.

Each option must include:

• description
• pros
• cons
• when to choose
• downstream impact

Step 5 — Recommend

Recommend one option.

The recommendation must be clearly labeled as recommendation, not decision.

Step 6 — Ask user

Ask the user to choose:

• A/B/C/D
• R = revise options
• E = explain more
• U = use recommendation

Stop here.

Do not continue until the user explicitly chooses.

Step 7 — Record

After the user chooses:

• append to .decision-log.md
• append to .learning-log.md if learning mode is enabled
• update artifact frontmatter if applicable
• update validation evidence index if applicable

Step 8 — Resume

Summarize:

• final decision
• evidence used
• caveats
• downstream impact
• next step