Handbook
Forge — major processes & flow maps
Mermaid diagrams below render on GitHub and in many Markdown viewers.
Forge — major processes & flow maps
Mermaid diagrams below render on GitHub and in many Markdown viewers.
1. Forge iteration lifecycle (high level)
2. Work unit hierarchy
Product backlog
└── Ore (raw, unrefined)
└── Ingot (refined, plannable)
└── Spark (executable, testable)
Daily execution
└── Charge (today's selected Sparks)
Time horizon
└── Forge iteration (one delivery cycle containing Sparks across phases A–F)
| Level | Forge term | Comparable concept | Typical lifespan |
|---|---|---|---|
| Coarsest | Ore | Epic or feature request (pre-refinement) | Days to weeks |
| Mid | Ingot | User story or feature (refined, ready) | One iteration |
| Finest | Spark | Task or sub-task (executable) | One focused session (~1–4 hours) |
| Daily set | Charge | Daily commitment | One day |
| Cycle | Forge iteration | Sprint or release cycle | 1–2 weeks |
Spark = Task (WBS mapping)
In projects with an existing milestone → epic → story → task hierarchy, Forge terms map directly — no parallel namespace:
| Existing level | Forge equivalent | ID example |
|---|---|---|
| Epic / feature request | Ore | M1E3 (pre-refinement) |
| Story (refined) | Ingot | M1E3S2 |
| Task (implementation slice) | Spark | M1E3S2T4 |
Spark IDs inherit from the WBS scheme. Spark-specific data (state, DoD, journal) lives in forge-logs/, not in the requirements tree. If existing Stories already carry clear acceptance criteria, they are effectively Ingots — no extra ceremony needed.
3. State model
| State | Meaning |
|---|---|
| Ore | Raw, unrefined input |
| Ingot | Refined, plannable work |
| Spark | Decomposed, executable work |
| In Charge | Active in today's daily set |
| Blocked | External impediment prevents progress |
| Banked | Intentionally paused with preserved context (strategic) |
| Done | Evidence gathered, ready for Assay Gate |
| Released | Passed Assay Gate, shipped |
| Rejected | Not viable; closed with reason |
4. Refinement flow (Ore → Ingot)
5. Planning flow (Ingot → Sparks → iteration scope)
6. Daily execution loop
7. Assay Gate flow
8. Versona challenge flow
9. Cross-phase mapping (A–F) in one iteration
| Phase | Where it happens in Forge |
|---|---|
| A Discover | Continuous Ore intake + discover: Sparks |
| B Specify | Refinement (Ore → Ingot) + specify: Sparks |
| C Design | Planning decomposition + design: Sparks |
| D Build | Charge execution + build: Sparks |
| E Verify | Assay Gate + verify: Sparks |
| F Release | Assay Gate pass + release: Sparks |
10. Flow details (walkthrough)
Iteration lifecycle — Ore intake is continuous; Refinement shapes selected Ore into Ingots with enough clarity for Planning. Inside the iteration, daily syncs confirm the Charge and surface blockers. Review assesses evidence quality; the Assay Gate makes the strict release decision. Retro captures learning that feeds back as new Ore.
Work unit hierarchy — Forge terms layer on existing WBS conventions. Ore maps to pre-refinement epics/features; Ingots to refined stories; Sparks to tasks. The Charge is a daily view, not a separate backlog. Forge iterations align with milestones in the project's existing structure.
State model — The key distinctions from a simple Kanban board: (1) Banked vs Blocked separates strategic pause from external impediment, (2) Done is not Released until the Assay Gate passes, and (3) Rejected items are closed with a reason rather than silently deleted.
Versona challenge — Versonas are invoked at decision points (refinement, pre-build, pre-release), not on every action. Each Versona references its discipline's bridge document to calibrate challenge intensity to the current SDLC phase.
11. Links
Canonical source
Edit https://github.com/autowww/blueprints/blob/main/sdlc/methodologies/forge/process-and-flows.md first; regenerate with docs/build-handbook.py.