From example-skills
Manages AI agent session lifecycles using FRAME-SHAPE-BUILD-PROVE phases with valid transitions, context preservation across sessions, handoff protocols, and metadata tracking. Triggers on session management or multi-session workflows.
How this skill is triggered — by the user, by Claude, or both
Slash command
/example-skills:session-lifecycle-patternsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Structure AI agent sessions for predictable outcomes, context preservation, and clean handoffs.
Structure AI agent sessions for predictable outcomes, context preservation, and clean handoffs.
FRAME → SHAPE → BUILD → PROVE → DONE
↑ ↑ ↑ ↑
└───────┴───────┴───────┘
(back-transitions allowed)
| Phase | Purpose | Activities | Gate |
|---|---|---|---|
| FRAME | Understand context | Explore code, read docs, ask questions | Can articulate the problem |
| SHAPE | Design approach | Create plan, identify files, consider tradeoffs | Plan reviewed and approved |
| BUILD | Implement | Write code, create files, apply changes | Implementation complete |
| PROVE | Verify | Run tests, validate output, check quality | All checks pass |
| DONE | Close | Summarize, commit, document decisions | Session artifacts preserved |
VALID_TRANSITIONS = {
"FRAME": ["SHAPE"],
"SHAPE": ["FRAME", "BUILD"], # Can go back to reframe
"BUILD": ["SHAPE", "PROVE"], # Can go back to reshape
"PROVE": ["BUILD", "DONE"], # Can go back to fix
"DONE": [], # Terminal
}
def can_transition(current: str, target: str) -> bool:
return target in VALID_TRANSITIONS.get(current, [])
session:
id: "sess_2026-03-20_a1b2c3"
started: "2026-03-20T10:58:00Z"
ended: "2026-03-20T12:30:00Z"
phase_history:
- phase: FRAME
entered: "2026-03-20T10:58:00Z"
duration_minutes: 15
- phase: SHAPE
entered: "2026-03-20T11:13:00Z"
duration_minutes: 20
- phase: BUILD
entered: "2026-03-20T11:33:00Z"
duration_minutes: 45
- phase: PROVE
entered: "2026-03-20T12:18:00Z"
duration_minutes: 12
scope:
organ: IV
repo: a-i--skills
task: "Create python-packaging-patterns skill"
artifacts:
files_created: [skills/development/python-packaging-patterns/SKILL.md]
files_modified: []
commits: ["abc123"]
decisions:
- "Chose hatchling as recommended build backend"
- "Included namespace package pattern for multi-repo use"
Maintain context as the session progresses through phases:
class SessionContext:
def __init__(self):
self.discoveries: list[str] = [] # FRAME findings
self.plan: dict = {} # SHAPE output
self.changes: list[str] = [] # BUILD artifacts
self.evidence: list[str] = [] # PROVE results
def frame_discovery(self, finding: str):
self.discoveries.append(finding)
def shape_decision(self, key: str, value: str, rationale: str):
self.plan[key] = {"value": value, "rationale": rationale}
For multi-session work, preserve essential context at session end:
## Session Close Summary
### What was accomplished
- Created 6 Wave 0 skills (python-packaging-patterns through vector-search-patterns)
### What remains
- 18 more Wave 0 skills in Batches 2-4
- Waves 1-4 pending
### Key decisions
- Using governance_norm_group: repo-hygiene for packaging/config skills
- Using organ_affinity: [all] for cross-cutting infrastructure skills
### Blockers
- None
### Next session should start by
- Reading this summary
- Continuing with Wave 0 Batch 2
FRAME (80%) → SHAPE (20%) → DONE
Heavy on reading, light on planning, no implementation. Produces understanding and a plan for a future BUILD session.
FRAME (10%) → SHAPE (15%) → BUILD (55%) → PROVE (20%) → DONE
Quick reorientation, then focused building and verification.
FRAME (40%) → BUILD (30%) → PROVE (30%) → DONE
Heavy investigation, targeted fix, thorough verification.
FRAME (30%) → PROVE (70%) → DONE
Mostly reading and validating existing work.
When work spans multiple sessions, each session references the chain:
chain:
id: "skill-fortification-campaign"
session_index: 3
total_sessions_estimated: 15
previous_session: "sess_2026-03-20_wave0-batch1"
completed_items: ["A1", "A2", "A3", "A4", "A5", "A6"]
remaining_items: ["A7", "A8", "A13", "A14", "B1", "B2", ...]
At session end, produce a handoff document:
| Signal | Healthy | Unhealthy |
|---|---|---|
| Phase transitions | Sequential with occasional back-steps | Skipping phases, staying in one phase |
| FRAME duration | 10-30% of session | <5% or >50% |
| PROVE evidence | Concrete test/validation output | "I think this works" |
| Context preserved | Summary written at DONE | Session ends abruptly |
| Scope creep | Stays within stated scope | Expanding mid-BUILD |
npx claudepluginhub organvm/a-i--skills --plugin coliseum-from-grainExecutes session plans in waves with role-based subagents and quality checks. Core orchestration engine for feature and deep sessions. Triggered by /go command.
Transfers context between AI agent sessions using structured handoff protocols, state serialization, decision logs, and compression. For multi-agent coordination and continuity.
Generates next-session.md or phase-handoff.md from cwf-state.yaml, plan.md, lessons.md, and logs to preserve context across agent sessions or phases. Triggers on 'cwf:handoff'.