* test(evals): scope claims to harness model fixtures and revision Append the neckbeard claims-scoping sentence to one representative expected_output per per-skill manifest so every corpus member states VAL-EVL-032 scope (harness, model, fixtures, revision under test). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(product-lifecycle): upgrade integrated launch trajectory Add an explicit launch-decision assertion to the new-product lifecycle case so the integrated product-launch scenario terminates in a launch decision recorded as a lifecycle evidence-ledger entry (VAL-CRP-010), and scope its expected_output claims per VAL-EVL-032. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(production-excellence): add integrated migration reconciliation failure case Add integrated-migration-reconciliation-failure: the production-excellence gate model returns No-go on a reconciliation mismatch, records the failure evidence, produces a rollback/roll-forward decision with an accountable owner, and does not proceed to launch (VAL-CRP-012). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(agent-production-operations): add privacy boundary escalation case Add integrated-privacy-boundary-escalation (VAL-CRP-015): the runtime control plan halts a cross-boundary EU PII trace export before any data processing, names the privacy boundary, and escalates to jurisdiction- specific legal review and a human operator. Also add a tool-authority- health handoff assertion to the read-only contract case (VAL-CRP-016). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(lifecycle-evals): add lifecycle evaluation corpus Add the #204 corpus home: run tooling (run-corpus.sh, fake adapter only), programmatic coverage validator (validate-corpus-coverage.py), machine- readable coverage index + human-readable coverage matrix, regression- detection and fixture/source notes, the bounded discovery brief, and a one-snapshot committed set of fake-adapter per-trial run artifacts with harness/model/date scoping fields. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> --------- Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
Conditional Customer Success — recurring human-relationship practices
Guide customer-success practices for products with recurring human relationships: success plans, health evidence, renewal and expansion signals, quarterly business reviews (QBRs), handoffs to product/support/engineering, escalation paths, and closed-loop Voice of Customer operations. This skill is conditional — it loads only when the product has accounts, renewals, QBRs, or a customer-success team. When those conditions are absent, it declines and routes the caller to the appropriate alternative.
Why Install This Skill
Many products have human relationships with their customers — account managers, renewal conversations, quarterly business reviews, success plans, and structured escalation paths. When those relationships exist, customer-success practice brings discipline: evidence-based health tracking instead of gut-feel red accounts, structured escalation instead of ad-hoc fire drills, and closed-loop feedback that ensures customer insights reach the product team and the customer hears back what happened.
This skill fills a gap in the catalog: no dedicated customer-success capability existed. Isolated pieces lived across onboarding, renewal, expansion, and stakeholder skills, but nothing connected them into a coherent practice with clear artifacts, evidence standards, and handoff protocols.
After installing, your agent can: assess whether customer-success practice applies to a given product context (and decline when it does not), build success plans anchored to customer outcomes, maintain evidence-based health records that surface conflicting signals rather than hiding them, design escalation paths with explicit human-judgment gates, run structured QBRs, handoff account evidence to product/support/engineering teams with context, and operate a closed-loop Voice of Customer process from insight to product change to customer communication.
What You Get
| Directory Entry | What It Provides |
|---|---|
SKILL.md |
Core methodology: applicability decision, success plans, health/risk records, escalation, handoffs, closed-loop feedback, QBR structure, privacy and human-judgment boundaries, and routing to related skills. |
README.md |
This file — human-facing overview. |
references/discovery-brief.md |
Bounded discovery brief: surveys existing onboarding/renewal/expansion/stakeholder content, defines when customer-success applies and when it routes to analytics/adoption/lifecycle-learning. |
references/privacy-and-human-judgment.md |
Full privacy and human-judgment boundaries: consent framework, surveillance-risk guidance, decision-support vs. automated-decision rules, escalation-gate requirements. |
templates/applicability-decision.md |
Structured applicability assessment — records which preconditions are present/absent and produces a proceed-or-decline verdict. |
templates/success-plan.md |
Customer success plan template: desired outcomes, product-capability alignment, measurable milestones, evidence gates, relationship owner. |
templates/health-risk-record.md |
Evidence-based health/risk record: signal, source, trend, confidence, and conflicting-signal tracking per dimension. |
templates/escalation-and-feedback-closure.md |
Escalation path definition template and closed-loop feedback closure record. |
evals/evals.json |
Schema-valid evaluation manifest with five output-quality cases covering B2B subscription, internal-tool decline, public-service, renewal-risk, and conflicting health evidence. |
Quick Start
No API keys or external dependencies are required. The skill loads when the agent detects a product context with accounts, renewals, QBRs, or a customer-success team.
- When customer-success practice is considered, the agent first produces an applicability decision using the template.
- If the context qualifies (accounts, renewals, QBRs, or CS team present), the agent proceeds through the relevant artifacts.
- If the context does not qualify (no accounts, no renewals, no QBRs, no CS team), the agent declines and routes to product-analytics-and-measurement, product-adoption, or product-lifecycle-learning.
To validate the skill:
ruby scripts/validate-skills.rb
.venv/bin/python scripts/validate-evals.py
.venv/bin/python -m eval_runner conditional-customer-success/evals/evals.json --adapter fake --output-dir /tmp/eval-smoke-cs
Triggers
Load this skill when the user asks about: customer success, success plans, account health, health scoring, renewal risk, churn risk, expansion signals, quarterly business reviews (QBRs), executive business reviews, voice of customer, closed-loop feedback, customer escalation, account management, account handoff, customer communication after product changes.
Do NOT load when the context has no accounts, no renewals, no QBRs, and no customer-success team. Examples: internal tools without recurring human relationships, pure transactional products, public services without account-based engagement, consumer apps without human CS relationships.
Requirements
- No API keys, external services, or runtime dependencies.
- The skill references existing skills in the catalog (product-analytics-and-measurement, product-adoption, product-experimentation, go-to-market) and prose-references skills not yet landed (product-lifecycle-learning).
- Templates are markdown files with no special rendering requirements.