ateam-fusion-skill-prompt-architect

## Copyright (c) 2026, Oracle and/or its affiliates. ## Licensed under the Universal Permissive License v 1.0 as shown at http://oss.oracle.com/licenses/upl # Fusion Skill Prompt Architect Use this skill to generate reliable, reusable prompt specifications that help the Data Management and Analytics A-Team create high-quality Oracle Fusion skills across **implementation architecture and data/analytics** workstreams. ## Implementation architecture span This skill must support prompts that cover, at minimum: - Cross-pillar architecture (HCM, ERP, SCM, CX). - Application integration (Fusion-to-Fusion, Fusion-to-PaaS, Fusion-to-third-party). - Governance and operating model. - Data definition, ownership, lifecycle, and contracts. - Security (IAM, SoD, masking, encryption, auditability). - Extensibility (PaaS extensions, event/model driven integration, customization boundaries). - Monitoring and observability (functional, technical, and business telemetry). Reference framing: - A-Team architecture coverage: `https://www.ateam-oracle.com/oracle-fusion-cloud-implementation-architecture-a-7-pillar-framework-for-scalable-and-ai-ready-deployments` ## Use when - You need to author a new `agentskills.io` skill prompt for Fusion implementation architecture, operations, or data/analytics work. - You want repeatable prompt quality across multiple architects and agents. - You need stronger controls against hallucinated Fusion objects/endpoints/query syntax. - You need consistent architecture outputs that remain coherent across HCM/ERP/SCM/CX boundaries. - You need measurable prompt quality (usefulness + performance) before broad rollout. ## Inputs expected - Business objective and target users. - Functional scope and domain boundaries (HCM/ERP/SCM/CX, object list, process boundaries, KPI list). - Fusion architecture references (pillar standards, integration patterns, governance principles, extensibility policies). - Fusion technical references (REST endpoints, BICC PVO names, OTBI subject areas, version constraints) where data topics apply. - Non-functional constraints (freshness SLO, latency, cost, compliance/security masking). - Required output format (`SKILL.md`, scripts, references, templates, or prompt-only artifact). - Acceptance criteria and review owners. ## Output contract Produce all of the following: 1. **Prompt Specification** using `assets/prompt-template.md`. 2. **Evidence Ledger** mapping each Fusion claim to source-of-truth artifacts. 3. **Guardrail Matrix** covering hallucination prevention, ambiguity handling, version safety, and cross-pillar consistency. 4. **Evaluation Scorecard** using `scripts/evaluate_prompt_skill.py` + params JSON. 5. **Refinement Notes** listing unresolved `UNVERIFIED` items and closure actions. ## Method (CRAFT+ stack) 1. **C — Context grounding** - Capture organizational context, domain boundaries, and known constraints. - Explicitly record which Fusion references are authoritative. - Encode cross-pillar dependencies (process handoffs across HCM/ERP/SCM/CX). 2. **R — Role calibration** - Define the agent as senior Oracle data architect + senior software engineer. - State expected technical rigor (e.g., object fidelity, pagination semantics, date format discipline). 3. **A — Action definition** - Define the exact deliverable (new skill, enhancement, implementation blueprint, extraction blueprint, evaluation artifact). - Specify sequencing requirements and mandatory checks. 4. **F — Format contract** - Require deterministic output shape (sections, tables, code blocks, scoring block). - Include mandatory confidence scoring with refine-if-<0.9 gate. 5. **T — Target and tone alignment** - Target DMA A-Team practitioners. - Keep language operational and implementation-ready (no generic advisory-only responses). - Ensure guidance is directly consumable by architects, integration engineers, and operations owners. 6. **Enhance with advanced prompting methods** - **EmotionPrompt / stakes framing:** include business criticality to increase focus. - **OPRO cue:** include “Take a deep breath and work step-by-step”. - **ExpertPrompting:** force explicit expert persona and specialization boundaries. - **Role-Play Prompting:** define actor responsibilities (architect, reviewer, operator). - **Principled instructions (26 principles):** use clear constraints, delimiters, and verification instructions. 7. **Apply anti-hallucination guardrails** - Every Fusion technical claim must include one source reference. - Unknown items must be labeled `UNVERIFIED` (never invented). - Separate “Known facts” vs “Assumptions” vs “Open questions”. - Check that each proposed decision has explicit cross-pillar impact analysis. 8. **Evaluate and iterate** - Run the mandatory evaluation+report wrapper with real run data. - If any critical metric misses threshold, revise prompt and re-score. ## Guardrails (mandatory) 1. **No fabricated Fusion metadata or architecture constraints** - Do not invent REST resources, BICC PVOs, OTBI subject areas, query params, version behavior, or governance/security policies. 2. **Evidence-tagged assertions** - Each key claim must cite one of: - repository artifact path, - official Oracle/A-Team URL, - validated run output. 3. **Version + environment awareness** - State Fusion release/API context if known. - If unknown, mark `UNVERIFIED` and request clarification. 4. **Progressive disclosure** - Include only domain-relevant logic in primary output; move non-essential material to optional notes. 5. **Cross-pillar consistency** - Do not optimize one pillar in a way that violates another pillar's control objectives. - Surface integration and data ownership impact for each major decision. 6. **Ambiguity protocol** - If required inputs are missing, ask targeted follow-up questions before finalizing. ## Edge cases to handle - Conflicting source definitions for object/endpoint naming. - Missing query semantics (`q`, date filters, pagination tokens). - Requirements that cannot satisfy both latency and cost targets simultaneously. - Mixed-scope requests (ERP + HCM + SCM) with uneven source readiness. - Cross-pillar operating model conflicts (e.g., governance model differs by pillar). - Extensibility demand that conflicts with Fusion SaaS upgrade-safe boundaries. - Monitoring requirements with no agreed ownership/escalation model. - Security constraints requiring data minimization/masking not present in initial prompt. ## Evaluation protocol (usefulness + performance) Use the mandatory wrapper `scripts/evaluate_prompt_skill_with_report.py`. Minimum required artifacts per run: 1. JSON scorecard 2. Browser HTML scorecard report Example command: ```bash python3 agent-skills-fusion/fusion-skill-prompt-architect/scripts/evaluate_prompt_skill_with_report.py \ --params <eval_params.json> \ --manifest <prompt_manifest.json> \ --scorecard-output <scorecard.json> \ --html-output <scorecard_report.html> ``` The raw evaluator `scripts/evaluate_prompt_skill.py` is still available, but should be treated as a library-level primitive. Operational runs must use the wrapper so that HTML reporting is always produced. Track these metrics: - **Usefulness** - `task_success_rate` - `first_pass_acceptance_rate` - `coverage_rate` - `fusion_fidelity_rate` - `implementation_coverage_rate` - **Performance** - `p95_latency_ms` - `avg_iteration_count` - `accepted_per_1k_tokens` - `consistency_rate` - **Reliability/Safety** - `hallucination_rate` Default gate guidance: - `task_success_rate >= 0.90` - `fusion_fidelity_rate >= 0.95` - `implementation_coverage_rate >= 0.95` - `hallucination_rate <= 0.02` - `p95_latency_ms <= 3000` - `avg_iteration_count <= 2.0` ## Quality bar - Output includes all mandatory sections in the template. - No untagged technical claims. - All unresolved items clearly marked `UNVERIFIED` with next action. - Evaluation scorecard attached and threshold compliance stated. - Confidence scores (0-1) included for: scalability, effectiveness, reliability, completeness. - If any confidence score < 0.9, refine output before final delivery. - Benchmark can be executed on 24 synthetic prompts generated by `scripts/generate_synthetic_prompts.py`. - Each benchmark/evaluation execution must emit an HTML report for browser navigation/filtering. ## References - Prompt template: `assets/prompt-template.md` - Review checklist: `assets/prompt-review-checklist.md` - Method mapping: `references/method-mapping.md` - Evaluation definitions: `references/evaluation-metrics.md` - Pillar framework guide: `references/implementation-pillar-framework.md` - Synthetic benchmark scripts: - `scripts/generate_synthetic_prompts.py` - `scripts/build_synthetic_eval_params.py` - `scripts/evaluate_prompt_skill_with_report.py` - `scripts/render_scorecard_report.py` ## Companion Enrichment (Inputs/Outputs/Workflow/Prompts) This section standardizes quick-operational usage using the fusion-skill-prompt-architect pattern of explicit input/output contracts, deterministic workflow, and reusable prompt examples. Inputs: - Skill objective, target users, and implementation scope - Authoritative Fusion references and architecture constraints - Expected output artifacts (prompt spec, evidence ledger, scorecard) - Acceptance thresholds for usefulness, fidelity, latency, and safety Outputs: - Production-grade prompt specification with deterministic structure - Guardrail matrix and evidence ledger for all key claims - Evaluation scorecard with HTML/JSON reporting artifacts - Refinement notes for unresolved UNVERIFIED items and next actions Workflow: 1. Ground prompt context and constraints using CRAFT context discipline 2. Define role/action/format/target with explicit guardrails 3. Apply anti-hallucination checks and evidence tagging 4. Evaluate against usefulness/performance/safety thresholds 5. Iterate until acceptance gates are met and confidence is high Prompts: - Create a prompt specification for a new Fusion extraction skill using CRAFT+ methods. - Review this draft prompt and identify anti-hallucination gaps with fixes. - Generate evaluation parameters and scoring guidance for this prompt skill. - Refine the prompt until it meets task success and fidelity thresholds.