mirror of
https://github.com/magnus919/agent-skills.git
synced 2026-09-11 19:47:12 +03:00
Magnus Hedemarkandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
c0c7690724
Move the 8 directories under bundles/ to the repo root via git mv and remove the now-empty bundles/ directory. Replace the "bundles" entry in pyproject.toml [tool.deptry] extend_exclude with the 8 moved dir names so the moved trees stay excluded from Python dependency analysis. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
88 lines
4.8 KiB
Markdown
88 lines
4.8 KiB
Markdown
# product-lifecycle
|
|
|
|
Route a product through its full lifecycle — from discovery to post-launch
|
|
learning — by composing existing specialist product skills with phase-entry
|
|
evidence, handoff artifacts, and escalation rules.
|
|
|
|
## Why Install This Skill
|
|
|
|
Product work is naturally multi-phase: you discover a problem, validate a
|
|
strategy, design a solution, run experiments, hand off to delivery, drive
|
|
adoption, measure success, and decide whether to continue or retire. But the
|
|
specialist skills that support each phase — product-discovery,
|
|
product-strategy, product-experimentation, product-adoption, and others — don't
|
|
connect to each other. An agent that loads product-discovery has no built-in
|
|
knowledge of what comes next or what evidence to hand off.
|
|
|
|
The product-lifecycle bundle fills this gap. It's a thin orchestration layer
|
|
that sits above the specialist skills and provides three things:
|
|
1. A phase routing table that tells the agent which skill to load at each point
|
|
in the lifecycle.
|
|
2. Phase-entry evidence and handoff contracts so the agent knows what to expect
|
|
at each transition and what to produce for the next phase.
|
|
3. Stop and escalation rules at every phase so the agent knows when to halt —
|
|
and the lifecycle ledger preserves what was learned even when work stops.
|
|
|
|
The bundle is thin by design. It never duplicates a specialist's methodology.
|
|
If you only need one phase, load that specialist skill directly. If you need to
|
|
navigate a product across multiple phases with evidence handoffs between them,
|
|
load this bundle.
|
|
|
|
## What You Get
|
|
|
|
| Path | What it provides |
|
|
|---|---|
|
|
| `SKILL.md` | Thin umbrella with a 9-phase routing table, loading protocol, and "When not to use" boundary |
|
|
| `README.md` | This file — human-facing overview |
|
|
| `AGENTS.md` | Agent-facing loading and operational instructions |
|
|
| `references/phases.md` | Detailed per-phase contracts: entry evidence, output artifacts, escalation behavior, completion criteria, and lifecycle evidence ledger spec |
|
|
| `references/discovery-brief.md` | Bounded discovery brief comparing with existing bundles (neckbeard, workflow-architect, tailscale, research-and-vault) and stating the bundle boundary |
|
|
| `references/capability-map.md` | Capability area → owning skill lookup table for quick reference without traversing the full lifecycle |
|
|
| `evals/evals.json` | Schema-v1 output-quality evaluation cases covering a complete lifecycle, ambiguous requests, failed experiments, non-adoption, and justified retirement |
|
|
| `manifest.yaml` | Machine-readable bundle manifest (schema v1): purpose, audience, stages, included skills, prerequisites, outputs, handoffs, conflicts, and eval suite |
|
|
|
|
## Quick Start
|
|
|
|
Load the umbrella when you need to route a product across multiple lifecycle
|
|
phases:
|
|
|
|
1. Start with the phase routing table in `SKILL.md` to locate your current phase.
|
|
2. Load `references/phases.md` for the detailed phase contract.
|
|
3. Load the specialist skill(s) named in the phase row and follow their method.
|
|
4. Write phase outputs to the lifecycle evidence ledger.
|
|
5. The next phase reads the ledger and continues.
|
|
|
|
For single-phase work, load the specialist skill directly — e.g.,
|
|
`product-discovery` for stakeholder interviews, `product-experimentation` for
|
|
experiment design, or `product-adoption` for adoption planning.
|
|
|
|
## Triggers
|
|
|
|
- Evaluating a new product idea end-to-end.
|
|
- Managing a product through its lifecycle phases.
|
|
- Connecting product phases that currently operate in isolation.
|
|
- Needing phase-entry evidence and handoff contracts between product skills.
|
|
- Deciding whether to continue, pivot, or retire a product.
|
|
- A stakeholder request that needs to be routed through discovery, strategy, and validation before delivery.
|
|
- A product that has launched and needs adoption measurement, success evaluation, and lifecycle review.
|
|
|
|
Do not trigger for:
|
|
- A single product task owned by a specialist skill — load that skill directly.
|
|
- Software delivery lifecycle work — route to `neckbeard`.
|
|
- Standalone strategic analysis, financial modeling, or GTM planning without a lifecycle context.
|
|
|
|
## Requirements
|
|
|
|
- No API keys, services, or network dependencies.
|
|
- No environment variables required.
|
|
- Compatible with any agent harness that supports the Agent Skills format.
|
|
- The specialist skills this bundle routes to must be installed. The bundle
|
|
assumes the following catalog skills are available: `product-discovery`,
|
|
`product-strategy`, `product-roadmapping-and-portfolio`, `product-design-and-ux`,
|
|
`product-experimentation`, `product-analytics-and-measurement`,
|
|
`product-adoption`, `conditional-customer-success`,
|
|
`product-operations-and-governance`, `product-lifecycle-learning`,
|
|
`implementation-planning`, `spec-driven-development`, `production-readiness`,
|
|
`release-engineering`, `product-methodology`, `go-to-market`,
|
|
`financial-modeling`, `data-scientist`.
|