graph-plan

name	graph-plan
description	Execute the PLAN phase of the lifecycle via the `agf` CLI — smart decompose, sprint planning, DORA-based estimation, cross-project learning
triggers	["graph-plan"]
version	2.0.0
author	Diego Nogueira
date	"2026-04-04T00:00:00.000Z"

graph-plan

PLAN phase via the agf CLI (zero MCP). Decomposes epics into atomic tasks (auto or manual), plans sprints, maps dependencies, and estimates with DORA metrics.

When to Use

• After DESIGN is complete (ADRs, architecture defined)
• Breaking epics into implementable tasks
• Planning sprint scope and priorities
• agf phase reports PLAN

Mandatory Flow

agf context <id> → agf decompose / agf node add → agf edge add → agf forecast → agf insights → agf phase implement

Workflow

Step 1: Load Context

agf context <epic_id>
agf search "<epic title + decisions>"

Review ADRs, requirements, architecture from DESIGN.

Step 2: Cross-Project Estimates (optional)

agf search "estimates patterns"

Use historical velocity to improve estimates.

Step 3: Decompose Epics

Option A — Smart Decompose (recommended):

agf decompose <epic_id>

Auto-creates subtasks: 1 AC = 1 subtask, with test-type inference:

Keywords in AC	Test type
api, endpoint, database, persists, sync, fetch, http	integration
page, click, browser, redirect, ui, form, button	e2e
else	unit

Option B — Manual:

agf node add --type task
agf node add --type subtask

Atomic rules (XP Anti-Vibe): each task ≤2h · clear testable AC · XP size (XS–XL) · prefer many small over few large.

Step 4: Map Dependencies

agf edge add <from> <to> --type <rel>

Edge types: task→task, subtask→task, task→epic, task→decision.

Step 5: Plan Sprint

agf decompose

Assign by priority, dependencies, size, risk (tackle risky items early).

Step 6: Sync Stack Docs

Refresh stack API docs in the knowledge base before IMPLEMENT so the executor has accurate references. Recommended.

Step 7: DORA Metrics

agf forecast

Use velocity (deploy frequency, lead time) to calibrate sprint capacity.

Step 8: Sprint Health

agf insights

Score the plan: balanced load, no oversized tasks, deps resolved.

Step 9: Validate Readiness

agf gate plan

Gate: all epics decomposed · tasks have AC · deps mapped (no cycles) · sprint assignments exist · no oversized tasks (>L without subtasks).

Step 10: Transition

agf phase implement

Follow the next-action hint from the agf CLI.

Output Format

Phase: PLAN → IMPLEMENT
Tasks: N tasks, M subtasks (K via agf decompose)
Sprints: J planned
Dependencies: D edges
DORA: velocity X tasks/day, lead time P85 Yh
Gate: ready — score N/100, grade X
Status: Ready for IMPLEMENT

Loop Link

PLAN → IMPLEMENT: agf phase implement then agf start ($graph-implement) pulls the first task with TDD.

Anti-Patterns

• Don't write code during PLAN — planning only
• Don't create tasks >2h — use agf decompose or decompose manually
• Don't skip AC — they drive TDD in IMPLEMENT
• Don't ignore dependencies — they set execution order
• Don't plan everything at once — 1–2 sprints ahead, refine later
• Don't ignore the next-action hint
• Don't skip refreshing stack docs — prevents executor hallucination

Codex Notes

• In Codex Plan Mode, plan only — do not mutate files.
• During implementation, follow AGENTS.md and use apply_patch for manual edits.

Token Economy

Economia de tokens. Os levers compartilhados por todas as skills — --select, agf retrieve-command, agf exec chain, reuso antes de criação — vivem em _shared.md → Token Economy. Fonte única: um parágrafo repetido em trinta arquivos é o trigésimo primeiro que envelhece sozinho.