proposal-skills

Use when reviewing new or changed skills and bundled resources for unsafe installers, credential harvesting, hidden execution, or excessive permissions; use skill-writing for authoring structure.

Use when creating or normalising reusable proposal skills, trigger routes, contracts, references, or authoring automation; use skill-safety-audit instead for a read-only security review.

Use when routing, drafting, reviewing, or assembling a complete consulting proposal, EoI, bid, or tender response; use a numbered pipeline skill when only one known section is required.

Use when choosing whether an AI agent is included, sold as an add-on, or sold standalone; use the pricing skill for rate mechanics and the SLA skill for service commitments.

Use when selecting and adapting agent-specific contract exhibits for a proposal, MSA, or statement of work; use the addendum skill when drafting the full MSA/SLA overlay.

Use when defining intervention credits, abort rights, or refund mechanics for an agent engagement; use the SLA skill for broader credits and the outcome-pricing skill for success fees.

Use when drafting agent-specific MSA and SLA addendums covering action accountability, audit logs, kill switches, intervention, liability, and upstream-provider dependencies.

Use when procurement, legal, or finance challenges agent pricing, failed-task billing, audit rights, refunds, liability, indemnities, or price corridors; use the contract pack for final clauses.

Use when defining renewal, volume true-up, ramp-down protection, autonomy-linked price steps, or indexation for an agent engagement; use the pricing skill for initial rates.

Use when defining an agent SLA and service-credit schedule for availability, task success, intervention rate, resolution time, kill-switch response, or audit-log access.

Use when structuring agent gain-share, success-fee, base-plus-success, or performance-corridor pricing; use the general pricing skill for per-step, per-agent, or usage rates.

Use when building a task-based business case for an AI agent, including intervention-adjusted benefits, the full agent cost stack, downside scenarios, and autonomy-ramp payback.

Use when planning workforce adoption, trust staging, supervisor training, affected-party disclosure, or contestability for agents that act on a buyer's behalf.

Use when presenting verified agent-specific trust and compliance credentials, including action logs, irreversibility gates, kill-switch drills, identity controls, and scope attestations.

Use when qualifying an AI-agent opportunity by testing agent-versus-workflow fit, autonomy level, action reversibility, oversight, accountability, success measures, and regulatory exposure.

Use when drafting the end-to-end delivery methodology for an AI agent or multi-agent system, from discovery and action-catalogue design through staged autonomy and operations.

Use when scoping an AI-agent proof of concept or pilot with shadow, supervised, and agentic stages, measurable gates, abort conditions, and production-exit criteria.

Use when selecting and presenting per-resolution, per-outcome, per-step, per-agent, hybrid, or success-based pricing for an AI agent; use commercial-layer skills for contract exhibits.

Use when answering or pre-empting an AI-agent procurement questionnaire on autonomy, action scope, reversibility, kill switches, audit logs, identity, subprocessors, and governance.

Use when drafting an AI-agent risk register or responsible-AI commitment covering autonomous actions, irreversibility, scope, tool injection, identity, kill switches, and contestability.

Use when defining an AI-agent delivery team, role accountabilities, RACI, mobilisation curve, safety coverage, client counterparts, or evaluator-facing staffing evidence.

Use when adapting an AI-agent proposal to customer support, finance, insurance, public sector, healthcare administration, legal support, or operations with a sector-specific autonomy stance.

Use when an AI-on-SaaS proposal needs a CFO-grade ROI model that includes model usage, evaluation cost, risk-adjusted benefits, and downside scenarios; use the base SaaS ROI skill when no AI cost or quality variables apply.

Use when adoption planning must address trust, human oversight, escalation, retraining communications, and workforce concerns for AI features inside multi-tenant SaaS; use the SaaS rollout skill for non-AI operating change.

Use when one proposal must integrate multi-tenant SaaS delivery with RAG, copilots, agents, or AI analytics across shared phases and gates; use SaaS implementation methodology when the scope contains no AI feature.

Use when a proposal must evidence AI-specific trust and compliance controls inside multi-tenant SaaS, including model providers, evaluation practice, data use, and region routing; use the SaaS trust skill for non-AI credentials.

Use when qualifying AI features inside a multi-tenant SaaS opportunity across workflow fit, data readiness, hallucination tolerance, regulation, and tenant variation; use SaaS discovery when no AI capability is proposed.

Use when an AI feature inside SaaS needs a time-boxed POC or pilot with a golden dataset, measurable evaluation thresholds, abstention rules, and a production decision gate; use the SaaS pilot skill for non-AI trials.

Use when pricing AI features within SaaS through credits, allowances, overages, model tiers, or fair-use controls while protecting margin from usage and provider-cost volatility; use SaaS pricing when AI economics do not apply.

Use when an AI-on-SaaS bid must answer or pre-empt procurement questions about model providers, data training, retention, deletion, sub-processors, region routing, and sovereign options; use the SaaS questionnaire skill for non-AI review.

Use when an AI-on-SaaS proposal needs an owned AI risk register and auditable Responsible-AI commitment for CISO, DPO, ethics, or regulator review; use generic risk management when the engagement contains no AI.

Use when an AI-on-SaaS proposal must justify the combined AI, data, safety, evaluation, platform, security, SRE, and customer-success roles required for delivery; use the standard team skill when no AI specialist roles are needed.

Use when AI-on-SaaS positioning must reflect a named vertical's use cases, regulator stance, evidence expectations, and risk language; use SaaS vertical positioning when AI-specific buyer concerns are absent.

Use when a proposal covers accounting, finance operations, controls, ERP or POS finance, grants, tax, audit readiness, modelling, or financial transformation. Route pure bid pricing to 10-financial-proposal; this skill governs finance doctrine and delivery credibility.

Use when a proposal needs diagnostic, requirements, options-appraisal, process, or decision-support tools. Unlike consulting-frameworks, this skill selects and applies business-analysis techniques to a defined analytical question and deliverable.

Use when a proposal requires training, coaching, mentoring, knowledge transfer, Training of Trainers, or institutional capacity development. Unlike change-management, this skill designs learning outcomes, delivery, assessment, and post-training transfer.

Use when a proposal must address adoption, resistance, readiness, communications, transition, or organisational reform. Unlike capacity-building, this skill governs behaviour and operating-model change rather than teaching competencies alone.

Use when a proposal methodology needs a coherent conceptual framework, problem decomposition, or phase logic. Unlike business-analysis-tools, this skill shapes the overall consulting approach rather than selecting a technique for one analytical task.

Use when a proposal covers data collection, quality, governance, MIS, surveys, protection, retention, or migration. Route outcome measurement to monitoring-and-evaluation; this skill governs the data lifecycle and controls.

Use when designing a donor-funded EAC e-commerce business-development-services programme, company-selection method, needs-assessment-to-technical-assistance pipeline, expert-pool deployment model, or results-based monitoring approach.