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SkillsMP has collected 108 skills from peterbamuhigire/proposal-skills. Open a skill to review its source and details.
Showing 40 of 108 collected skills.
Use when a SaaS bid needs buyer qualification through ICP fit, critical event, pain chain, role-level impact, decision process, and no-bid signals; use AI-on-SaaS discovery when AI data, model, or hallucination questions apply.
Use when a SaaS proposal needs a time-boxed POC or pilot with explicit scope, success measures, exit criteria, and a decision gate to implementation; use the AI-on-SaaS pilot skill when evaluation datasets or hallucination controls apply.
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 a proposal concerns banking, insurance, payments, mobile money, microfinance, digital finance, or financial inclusion; also apply the finance doctrine whenever money, ledger, tax, or accounting claims are in scope.
Use when routing a proposal to the correct procurement-framework and industry-sector skills before drafting; use profiles separately to determine proposer identity and voice.
Use when auditing or improving the proposal engine or any bid, tender, EOI, methodology, technical/financial proposal, or consulting product it produces.
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 requires a theory of change, logframe, results framework, indicators, baselines, targets, learning, or evaluation. Unlike data-management, this skill defines performance questions and evidence use rather than the full data lifecycle.
Use when drafting the technical approach, conceptual model, phases, methods, QA, deliverables, or implementation logic. Unlike 03-understanding-of-assignment, this skill explains how the work will be executed and verified.
Use when a proposal needs a stronger narrative spine, evaluator journey, case-study story, design rationale, executive argument, or presentation/sign-off flow.
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 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.