Files
magnus919_agent-skills/forward-deployed-engineering/SKILL.md
T
Magnus Hedemarkandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> a315b77bd8 feat(flatten): rewrite moved-file paths for flat layout
Re-point relative references inside the six non-tailscale/workflow-architect
bundle dirs now that they live one level shallower at the repo root:
- bundle-root SKILL.md/manifest.yaml/README.md: ../../<target> -> ../<target>,
  cross-bundle ../../bundles/<x> -> ../<x>
- forward-deployed-engineering references/: ../../../<target> -> ../../<target>
- product-lifecycle references cross-bundle ../../bundles/neckbeard -> ../../neckbeard
- product-lifecycle references/discovery-brief.md prose headings drop bundles/ prefix
- neckbeard eval shell commands bundles/neckbeard/eval -> neckbeard/eval
- production-excellence AGENTS.md depth note updated
- manifest header comments point at ../schemas/bundle-manifest-v1.schema.json
- regenerate docs/lifecycle-capability-matrix.{md,json}

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
2026-08-14 15:59:56 -04:00

11 KiB

name, description, license, compatibility, metadata
name description license compatibility metadata
forward-deployed-engineering Guide embedded technical engagements from ambiguous stakeholder need through discovery, framing, hypothesis, build, evaluation, deployment, adoption, measurement, and generalization while preserving evidence, decision rights, and field learning. Use when one accountable technical lead must carry continuity across customer or stakeholder discovery, implementation, production fit, adoption, and measurable outcomes. Do not use for a bounded repository change, product investment governance, ongoing reliability or platform ownership, an isolated specialist task, or advisory work that ends before implementation and adoption. MIT Agent harness with file read/write, terminal, and skill loading. No network or runtime dependency required by the bundle itself.
spec-version tags
1.0 forward-deployed-engineering, embedded-delivery, adoption, measurement, generalization

Forward-Deployed Engineering

Use this bundle when the work is an embedded technical engagement whose success depends on continuity, not merely a recommendation or a code change. This is a normative operating model synthesized from the role observations in source-index.md and from routed specialist methods; the nine-stage sequence is not an externally standardized methodology.

Lifecycle

Discover → Frame → Hypothesize → Build → Evaluate → Deploy → Adopt → Measure → Generalize

Stage Required question Minimum output Stop condition
Discover What user workflow and problem are real? Stakeholder/workflow map and unknowns No recognizable problem or access to the relevant workflow
Frame What is in scope, who decides, and what outcome matters? Engagement charter and assumptions/decisions/risks ledger Authority, constraints, or outcome cannot be named
Hypothesize What smallest intervention could change the workflow? Testable hypothesis and decision rule No falsifiable hypothesis or unsafe test
Build What operationally complete slice can be built? Thin-slice implementation plan and owner Dependencies or permissions are infeasible
Evaluate What evidence supports quality, safety, and usefulness? Evaluation and release decision Baseline, representative evidence, or risk constraints missing
Deploy Can it be released, recovered, and verified in the authorized environment? Readiness, rollout, rollback, and verification record No authorized access, rollback, or release decision
Adopt Do intended users activate and use it in the target workflow? Adoption scorecard and intervention record Adoption failure is unexplained or ownership/support is absent
Measure Did the capability change the agreed outcome? Outcome measurement record Instrumentation cannot distinguish expected from observed
Generalize What should happen to the local learning? Productization record and field-learning record No evidence or receiving owner for the proposed next step

At every stage, read the current charter, workflow map, and ledger and add evidence rather than re-deriving prior decisions. Maintain one engagement charter, one stakeholder/workflow map, and one assumptions-decisions-risks ledger. Each stage records entry evidence, the artifact produced, the accountable decision maker, unresolved unknowns, and the next handoff. Never silently turn an observation into a requirement, a prototype into a production claim, or a local success into a reusable product capability. For the expected depth and evidence labeling of these artifacts, see the worked example engagement.

Where to enter the lifecycle

Enter at the stage that matches what already exists. Do not restart earlier stages unless the current charter's stop conditions require it.

What you already have Enter at
A stakeholder request or observed workflow opportunity, no validated problem Discover
Validated problem and stakeholders, no charter Frame (establish the charter first)
Charter, workflow map, and ledger; no approved requirement Hypothesize
Approved requirement; thin slice in progress Build
Built and tested slice; no release decision Evaluate
Released within the authorized boundary Adopt
Adopted and measuring against the decision rule Measure
Post-launch evidence and a generalization question Generalize
A well-specified bounded change with no continuity need Route to neckbeard instead

Before acting at any entry point, review the current charter, workflow map, ledger, and preceding stage-handoff record as entry evidence.

Loading protocol

  1. Establish the charter before solution design: problem, users, workflow, outcome, scope, authority, constraints, success measure, and stop conditions.
  2. Load lifecycle and artifacts and update the shared ledger after every stage.
  3. Treat the stage skills in manifest.yaml as candidates. Apply the route-selection conditions, load one primary specialist, and follow its method rather than copying it into this bundle.
  4. Before action in a constrained or sensitive environment, load authority and escalation and route access, security, privacy, irreversible, cost, and external-commitment decisions to their authorized owner.
  5. Before calling applied AI or any risky capability production-ready, load agent-evals-and-observability and production-readiness, and require baseline, representative and adversarial evidence, constraints, and a release decision.
  6. Treat adoption and measured workflow impact as completion conditions, not postscript communications. Use adoption and measurement.
  7. Apply the classification rules in generalization and productization, then close with the productization record. Classify local work as configuration, reusable pattern, product capability, transfer/replacement, or retirement, with evidence and an owner.
  8. Treat artifacts as private by default and apply the external-sharing gate before they leave the authorized engagement context.

Load only the primary specialist for the active stage. Add a secondary specialist only for a named blocker, risk, or handoff; do not preload every skill in the manifest. If one specialist fully owns the request, stop routing and hand the task to that specialist instead of running the FDE lifecycle.

Epistemic and communication contract

Label each material statement as one of: source fact, engagement observation, inference, recommendation, decision, or commitment. Use the communication reference for concise status and escalation updates. The discovery brief records the overlap audit and source limitations.

Completion and stop rules

The engagement is complete only when the capability is technically verified, deployed within the authorized boundary, adopted by the intended workflow, measured against an agreed outcome, and its learning has a generalization decision. A prototype, demo, or stakeholder approval alone is not completion.

Stop and preserve the ledger when the problem cannot be articulated, authority or access is missing, evidence fails, adoption remains unexplained or below the decision rule, or the next action exceeds the charter. Escalate rather than guess on security, privacy, irreversible changes, material cost, external commitments, or business authority. Route a well-specified repository bug directly to neckbeard and the relevant specialist instead of invoking this lifecycle.

When not to use

Scenario Reach for Why
Well-bounded repository change neckbeard Owns intake through verified PR and authorized release for a bounded change
Product investment, portfolio, or lifecycle governance product-lifecycle Owns investment and lifecycle governance, not delivery continuity
Ongoing reliability ownership (SLOs, alerts, incidents) site-reliability-engineering Standing operational ownership, not an embedded engagement
Internal platform design or operation platform-engineering Platform ownership, not customer delivery
One discipline fully owns the task That specialist directly FDE stops routing when one specialist owns the request
Advisory analysis ending before implementation and adoption The relevant architecture or decision specialist FDE completion requires adoption and outcome continuity

File map

Path Load when
references/discovery-brief.md Reviewing the boundary, overlap audit, or evidence basis
references/source-index.md Checking an externally verifiable role claim or refresh date
references/lifecycle-and-artifacts.md Starting or handing off any lifecycle stage
references/route-selection.md Selecting one stage specialist without violating its entry boundary
references/authority-and-escalation.md Working under access, security, privacy, cost, or authority constraints
references/adoption-and-measurement.md Evaluating activation, workflow adoption, and measurable impact
references/generalization-and-productization.md Deciding what field work becomes or does not become reusable
references/communication.md Writing status, decision, escalation, or handoff communication
references/worked-example-engagement.md Calibrating expected artifact depth or evidence labeling at any stage
templates/ Creating the charter, workflow map, ledger, stage handoff, engagement status, evaluation and release decision, adoption scorecard, outcome measurement record, productization record, or field learning record
manifest.yaml Reading machine-readable stages, routes, outputs, and conflicts