From delivery-graph
Sequences the full DGE delivery pipeline from raw demand to done: design, brief, plan, graph, execute, summary. Stops only at two judgment gates for user approval.
How this skill is triggered — by the user, by Claude, or both
Slash command
/delivery-graph:dge-deliver [raw demand, issue link, feature idea, or an in-progress DEM-### to resume][raw demand, issue link, feature idea, or an in-progress DEM-### to resume]The summary Claude sees in its skill listing — used to decide when to auto-load this skill
The **intent-driven** entry point to Delivery Graph Engineering. The user supplies
The intent-driven entry point to Delivery Graph Engineering. The user supplies
intent and judgment; the conductor owns procedure. After this single command,
the user never types another dge-* skill name to reach done.
This skill does not replace the nine dge-* skills — it sequences them and carries
context across phases. The individual skills remain available for power users.
Every delivery tool — and DGE until now — is a command palette: the user remembers
what to run next, the skill names, and the order. /dge-deliver removes that burden.
The design rule, held throughout:
Automate every decision about which skill runs. Automate zero decisions about whether the outcome is acceptable.
So the conductor auto-advances between phases (procedure) but stops at exactly two judgment gates and surfaces genuine failure/ambiguity (judgment). It never asks "shall I run the next skill?".
/dge-deliver, the user is never asked to invoke another
dge-* skill. The conductor runs design → brief → plan → graph → execute → summary.--mermaid adds the DAG diagram for large graphs in a rendering surface.
Gate 2 is always required — never auto-skip it.dge CLI. Never hand-edit graph.json or a brief's markdown.done requires real evidence, always.done.Run the shared preflight before anything else (design runs before dge init, so skip
the graph check on a brand-new demand):
dge preflight --no-graph || npx --no-install dge preflight --no-graph
If it exits non-zero, stop and tell the user to install the DGE CLI first:
DGE CLI not found. The
dgeCLI is a separate npm package from the/dge-*slash commands — the plugin marketplace ships the skills, not the binary. Install it in this project, then re-run:npm install --save-dev github:rafaelolsr/delivery-graph
If #$ARGUMENTS names an existing DEM-###, or the store already holds an in-progress
demand with an approved graph, resume from the ready queue instead of restarting:
dge next --json # returns the queue head; done nodes stay done
If a graph exists and has ready/incomplete nodes, skip design/plan and both gates (already approved) and jump straight to Phase 4 (execute). Announce that you are resuming, not restarting.
Run the /dge-design discipline: grill the demand one question at a time, survey prior
art and learnings, expose gaps, and write testable requirements through the CLI. This is
the only conversational phase; it ends when the design readiness gate passes with no
blocker gaps.
Render and present the Demand Brief:
dge brief demand DEM-###
Show it to the user as the "here is what we will build and why" summary: the problem, the outcome, non-goals, the requirements (with priorities), and your recommendation. Then stop and ask for approval or edits.
dge CLI mutation, then re-render the brief. Loop until approved.Do not proceed until the user explicitly approves.
On Gate 1 approval, auto-advance into the /dge-plan-graph discipline (no user
command): create tracks, nodes, dependency edges, and validation contracts through the
CLI, carrying the design context forward (a validation ambiguity surfaced in Phase 1
should shape how you split nodes and write contracts).
Then render and present the Graph Brief:
dge brief graph DEM-###
Show it as the "here is exactly what will be built and changed" graph: an indented
dependency tree where each node carries its type, the requirements it serves, and its
validation contract inline, plus the ready-queue order. The tree renders in every surface
(terminal, CLI, harness chat); add --mermaid only for a large multi-edge graph in a
rendering surface where a diagram beats indentation. Then stop and ask for approval or
edits (same conversational edit → CLI → re-render loop as Gate 1; blocker gaps still
block approval; abandon is still clean).
Gate 2 is always required. Never begin execution without explicit approval here.
On Gate 2 approval, auto-advance into the /dge-execute-graph loop in quiet mode.
Drive the ready queue end to end, one node at a time, evidence-gated:
dge status) so the user always sees current
graph.json state (the board is a projection, not a separate log).dge done error and the review
report path (stop-on-failure). Do not retry structural failures./dge-execute-graph
"Failure classification".Never fabricate evidence or weaken a contract to keep the loop flowing. After each builder produces evidence, apply the agentic verification policy before closure: standard-risk nodes prefer another harness but may reuse the builder harness in a fresh run; high-risk nodes require a different harness and stop for escalation when none is available.
When the queue is dry, emit the final summary following the shared output
convention (see skills/README.md):
## Next block: /dge-review and /dge-compound as the
natural next steps, or re-running after a blocker if one remains.dge done.dge preflight [--no-graph] # shared preamble
dge brief demand DEM-### # Gate 1 artifact
dge brief graph DEM-### [--mermaid] # Gate 2 artifact
dge next --json # queue head (resume + execution)
dge status # live board projection during execution
dge evidence run NODE-### --satisfies "..." -- <cmd>
dge evidence add NODE-### --satisfies "..." --summary "..." --result pass|fail|ambiguous
dge done NODE-### # the only path to completion
The conductor sequences these; the user issues none of them by hand.
npx claudepluginhub rafaelolsr/delivery-graph --plugin delivery-graphConverts DGE requirements into a structured delivery graph with tracks, dependency nodes, and validation contracts. Use after /dge-design to plan executable work.
Orchestrates the full SDLC autonomously with 4 human approval gates: scope, spec, plan, and final go-ahead. For greenfield projects.
Orchestrates 9-phase feature DELIVER workflow: baseline/review, roadmap/dual-review, TDD step execution, finalization. Tracks state, skips approved phases, retries reviews, enforces quality gates. Invoke via *deliver.