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64 lines
3.6 KiB
Markdown
64 lines
3.6 KiB
Markdown
# forward-deployed-engineering
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Carry an embedded technical engagement from an ambiguous need to an adopted,
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measurable capability and a deliberate generalization decision.
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## Why Install This Skill
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This bundle is designed to guard against continuity risks between disciplines:
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discovery that leaves builders without usable context, prototypes mistaken for
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production systems, deployments declared complete without adoption, and field
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patterns transferred without evidence or an accountable receiving owner. It
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gives an accountable lead a durable contract across those boundaries.
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After installation, an agent can frame the engagement and decision rights,
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route specialist work without duplicating it, preserve assumptions and risks,
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diagnose adoption and outcome gaps, and decide whether local work should remain
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configuration, become reusable, transfer to another owner, or be retired.
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## What You Get
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| Path | What it provides |
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| `SKILL.md` | Lifecycle trigger, boundary, loading protocol, stop rules, and file map |
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| `manifest.yaml` | Machine-readable stages, routed skills, outputs, handoffs, and conflicts |
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| `references/discovery-brief.md` | Overlap audit, source decisions, and design-risk rationale |
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| `references/source-index.md` | Recoverable primary sources, supported claims, limitations, and refresh rules |
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| `references/lifecycle-and-artifacts.md` | Stage contracts and continuity artifacts |
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| `references/route-selection.md` | Per-stage specialist entry conditions and direct-routing boundaries |
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| `references/authority-and-escalation.md` | Decision rights, constrained-environment discovery, and escalation rules |
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| `references/adoption-and-measurement.md` | Adoption diagnosis, outcome evidence, and applied-AI release evidence |
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| `references/generalization-and-productization.md` | Classification and productization decision model |
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| `references/communication.md` | Evidence-labeled status, handoff, and escalation patterns |
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| `templates/` | Ten templates matching the declared continuity, decision, handoff, measurement, and learning outputs |
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| `evals/evals.json` | Fifteen output-quality cases covering lifecycle, authority, and adjacent-skill boundaries |
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## Quick Start
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1. Load `SKILL.md` and create `templates/engagement-charter.md`.
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2. Record the stakeholder workflow and unknowns before proposing architecture.
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3. Treat `manifest.yaml` stage skills as candidates and apply
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`references/route-selection.md` before loading one primary specialist.
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4. Carry the ledger through deployment, adoption, measurement, and generalization.
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## Triggers
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- An embedded technical lead must own discovery through production adoption.
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- A stakeholder request is ambiguous and implementation context must be discovered.
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- A prototype must become a verified, deployed, adopted capability.
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- An applied-AI engagement needs eval-driven release and field feedback.
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- A local configuration or workflow may warrant a reusable pattern or product capability.
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- Adoption, workflow impact, and field learning must remain connected to delivery.
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Do not trigger for a bounded repository bug, standalone specialist task, product
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portfolio governance, ongoing SRE ownership, platform operation, or advisory
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work that ends before implementation and adoption.
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## Requirements
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- No API keys, services, or network dependencies.
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- Compatible with Agent Skills harnesses that support file reading, writing, and skill loading.
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- Routed catalog skills must be installed, including discovery, evaluation,
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production readiness, deployment, adoption, measurement, security, privacy,
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and constrained-environment specialists.
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