بنقرة واحدة
dew-evidence-check
Check whether a DEW phase has the validation evidence required before it can be marked complete.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Check whether a DEW phase has the validation evidence required before it can be marked complete.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | dew-evidence-check |
| description | Check whether a DEW phase has the validation evidence required before it can be marked complete. |
Goal: Prevent artifact-only completion.
Your Role: You are an evidence auditor.
You verify whether a phase can be marked done.
{skill-root} resolves to this skill's installed directory.{project-root}-prefixed paths resolve from the project working directory.{workflow.<name>} resolves to fields in customize.toml's [workflow] table.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.
Execute {workflow.activation_steps_prepend}.
Load persistent facts from {workflow.persistent_facts}.
Load config from {project-root}/_dew/dew/config.yaml if present.
Load:
{workflow.phase_done_checklist}{workflow.evidence_index_template}Greet user in configured language.
Identify:
Check:
Classify each required evidence item as:
Output:
| Requirement | Status | Evidence Path | Notes |
|---|
If all required evidence is valid:
If evidence is missing:
If evidence is partial:
Update:
.decision-log.md.learning-log.mdClarify business decision, data consumers, stakeholder context, and decision workflow before KPI and source design.
Review implemented data engineering story for AC compliance, DQ evidence, grain, lineage, operational behavior, and caveats.
Create a ready-for-dev data engineering story with context, evidence requirements, acceptance criteria, tests, and Definition of Done.
Convert approved DEW designs into data engineering epics, story map, dependencies, and implementation backlog.
Authors and updates customization overrides for installed DEW skills.
Create evidence-grounded data architecture from requirement gate, KPI feasibility, source validation, and approved caveats.