Openprompt Specification
| Entity Passport | |
| Registry ID | gh-tool--guided-engineering--openprompt-specification |
| Provider | github |
Cite this tool
Academic & Research Attribution
@misc{gh_tool__guided_engineering__openprompt_specification,
author = {Guided Engineering},
title = {Openprompt Specification Tool},
year = {2026},
howpublished = {\url{https://github.com/guided-engineering/OpenPrompt-Specification}},
note = {Accessed via Free2AITools Knowledge Fortress}
} π¬Technical Deep Dive
Full Specifications [+]βΎ
Quick Commands
git clone https://github.com/guided-engineering/OpenPrompt-Specification pip install openprompt-specification βοΈ Free2AITools Nexus Index V2.0
π¬ Index Insight
FNI V2.0 for Openprompt Specification: Semantic (S:50), Authority (A:0), Popularity (P:47), Recency (R:71), Quality (Q:50).
Verification Authority
π Specs
- Language
- Python
- License
- Open Source
- Version
- 1.0.0
Usage documentation not yet indexed for this tool.
π View Source Code βTechnical Documentation
Guided Engineering
A Spec-Driven Development (SDD) framework.
OpenPrompt-Specification
Version: 0.5.0 β see ROADMAP.md for the delivery history and Phase 6+ outlook.
Vibe Coding β Guided Engineering is a structured, traceable, and executable framework for Spec-Driven Development: versioned specifications drive every downstream artifact (code, tests, ADRs, worklogs) through modular prompts, personas, and reproducible documentation.
This project uses versioned YAML prompts, schema validation, personas, and categorized outputs to enable humanβmachine collaboration with high observability, reproducibility, and minimal complexity.
Brand: Guided Engineering. Methodology: SDD. See
.guides/sdd-process.mdfor the lifecycle.
β Why Guided Engineering?
Because modern software is too complex to be left to chance.
Teams face:
- Fragmented tools and chaotic handoffs.
- Hidden knowledge and undocumented decisions.
- Low reproducibility and onboarding friction.
- Over-reliance on opaque automation.
Guided Engineering addresses these pain points by:
- Treating engineering as a traceable process, not an ad-hoc craft.
- Shifting from unstructured communication to structured execution.
- Giving engineers control over AI through validated, readable prompts.
- Creating a long-term memory of why, how, and by whom something was done.
It allows teams to move fast without losing context, and automate without losing control.
It's not just about doing things faster. It's about doing them understandably.
π« What Guided Engineering *is not*
To avoid confusion, itβs important to be clear on what this practice does not aim to be:
| β Not This | β But Instead |
|---|---|
| A no-code tool | A human-led system for codifying intent |
| A replacement for engineers | A framework for engineers to lead AI |
| An automation-first approach | An explainability-first methodology |
| A documentation generator | A documentation-first execution model |
| A black-box assistant | A white-box process with full visibility |
| A generic AI prompt collection | A validated, version-controlled prompt system |
| DevOps boilerplate factory | A customizable, observable execution flow |
| Magic AI glue | A disciplined orchestration of agents |
Guided Engineering is not about replacing expertise β it's about structuring it.
It's not automation for its own sake, but rather augmentation with purpose.
π¨βπ« What is Guided Engineering?
Guided Engineering is not just a tool, it's a practice.
It defines a new way of working where engineers coordinate agents (e.g., LLMs, tools, automations) to execute small, traceable tasks. The human leads the process; the machine assists with precision and context.
Key aspects:
- Discipline and explainability over chaotic automation.
- Every task is represented by a validated, version-controlled prompt.
- Clear personas define responsibilities for each type of activity.
- All outputs are observable, auditable, and reproducible.
[π€] Human
ββ Plans
ββ Selects Prompts
ββ Orchestrates Execution
β
[π―] Structured Prompt (.yml)
ββ YAML + Schema + Persona
ββ Defines Inputs, Steps, Outputs
β
[π€] Execution Agent (LLM)
ββ GPT, Claude, Copilot, etc.
ββ Executes Prompt with Context
β
[π] Outputs (Markdown)
ββ Docs, Reports, Assessments
ββ Stored in `.guides/` folder
β
[π] Continuous Cycle
ββ Plan
ββ Execute
ββ Document
ββ Learn
βΊ (Feeds back to Prompts)
π§ The Practice (Human-led)
At its core, Guided Engineering is a human-led practice that organizes the SDLC using guided steps and structured decision-making.
Core tenets of the practice:
- Engineers orchestrate agents through prompt-driven workflows.
- Tasks are declarative and traceable - never implicit or opaque.
- The SDLC becomes an auditable process, from discovery to maintenance.
- Personas represent real engineering roles (e.g., QA Analyst, SRE, Code Auditor).
- Documentation is not an afterthought - itβs a first-class citizen.
π¦ The Artefacts (Structured Knowledge)
Guided Engineering relies on structured, versioned artefacts to guide execution and preserve traceability.
Main artefact types:
- YAML prompts (
*.yaml): Define intent, context, persona, and execution steps. - YAML specs (
*.yamlunder.guides/specs/): Versioned source of truth for every feature. - Markdown documentation (
*.md): Capture structured outputs, decisions, playbooks, ADRs. - JSON schemas (
*.json): Enforce structure, consistency, and validation.
Artefacts are stored in a canonical .guides/ folder, categorized by function.
ποΈ The Output (Software Built with Guidance)
Software projects developed using Guided Engineering exhibit the following characteristics:
- Every decision, plan, and test is documented as a prompt and output.
- Projects are self-documenting, self-explanatory, and reproducible.
- No tribal knowledge - everything is explicit and preserved.
- Transitions, audits, and onboarding become seamless due to rich context.
This ensures systems are not only built well - theyβre built understandably.
π οΈ The Tooling (Execution Support)
Guided Engineering is supported by a growing ecosystem of tools that automate, scaffold, and simplify the use of the practice:
- CLIs to run prompts, generate documentation, and validate outputs.
- Portals for browsing
.guides/content visually. - DevTools & extensions to integrate prompts and personas into daily workflows.
- Templates & generators to scaffold projects and personas with validated structure.
These tools serve as assistants - never as replacements for engineering judgment.
π Project Structure (`.guides/`)
| Folder | Purpose |
|---|---|
base/ |
Project-level structure and setup guides |
product/ |
Product requirements, roadmap, user personas |
specs/ |
SDD specs β versioned source of truth for every feature |
traceability/ |
SDD matrices β requirement β spec β test β commit β evidence |
assessment/ |
Full assessments of codebase, stack, risks, entities |
architecture/ |
Architectural layers, stack, rules, plugins |
architecture/adr/ |
Architecture Decision Records (ADRs) |
testing/ |
Test strategies, coverage, risk documentation |
operation/ |
Worklogs, changelogs, validation reports, conformance reports, FAQ |
prompts/ |
Executable YAML prompts by category and persona |
personas/ |
Roles responsible for prompts (e.g., Dev, QA, Architect, Auditor) |
schemas/ |
JSON Schema files to validate prompts, personas, specs, ADRs |
β Core Principles
- Everything is a prompt: Executable, explainable and version-controlled YAML.
- Small steps: Every prompt performs one clear task with observable outputs.
- Personas over roles: Each prompt is assigned to a persona with defined responsibilities.
- Traceable outputs: Every action must generate a file in
.guides/. - Human-led by design: AI and agents assist, but direction, judgment, and intent come from humans.
- Explainability over automation: Prompts must be readable, auditable, and reproducible by a human at any time.
- Minimize cognitive load: All outputs and flows must be simple, observable, and contextual.
π§ How it works
- A prompt (YAML) defines an objective, context, steps, and expected output.
- The prompt is assigned to a persona that represents the executor (human or agent).
- Each step is declarative and uses natural language with imperative clarity.
- Execution of the prompt generates Markdown outputs stored in
.guides/. - All prompts and schemas are versioned and validated locally.
π§© Example Prompts
All prompts live under .guides/prompts/:
prompt.discovery.yamlβ full technical assessment of a project (first prompt).prompt.onboarding.yamlβ generate a complete onboarding file for engineers.prompt.commit.yamlβ analyse pending changes and apply a Conventional Commit.prompt.web.generate-page.yamlβ generate a localized Next.js page with i18n + tests + worklog.
SDD prompts (prompt.spec.author.yaml, prompt.spec.validate.yaml, prompt.adr.author.yaml, prompt.requirement.traceability.yaml, prompt.acceptance-criteria.author.yaml, prompt.test-cases.from-spec.yaml, prompt.spec.conformance-check.yaml) operationalize the methodology.
π§ͺ Reference Example
The canonical illustration of the full SDD loop lives at .guides/specs/spec.example.user-login.yaml. It exercises every stage end-to-end:
| Stage | Artifact |
|---|---|
| 1. Spec | .guides/specs/spec.example.user-login.yaml |
| 2. Validate | .guides/operation/spec-validation.example.user-login.md |
| 3. Design | .guides/architecture/adr/0001-choose-spec-format.md |
| 4. Test cases | .guides/testing/test-cases.example.user-login.yaml |
| Cross-cutting | .guides/traceability/matrix.example.user-login.yaml |
| 5. Conform | .guides/operation/conformance.example.user-login.md |
| Audit | .guides/operation/worklog.md |
Copy the spec into your own .guides/specs/ folder, adapt the IDs, and follow the prompts top to bottom.
Heads-up. This repository is spec-first, so the example has no backing implementation. Its conformance report records the verdict
DEMO; a consuming project that implements the spec re-runsprompt.spec.conformance-check.yamlagainst its source tree and produces a realPASS | PASS-WITH-GAPS | FAILverdict.
π Contributors
This project is maintained using the Guided Engineering model itself - all changes are proposed, executed, and documented via prompts and personas.
π Resources
- Methodology:
.guides/sdd-process.md - Roadmap to v0.5.0:
ROADMAP.md - Manual validation protocol:
VALIDATION.md - Prompt schema (canonical, SDD-aware):
.guides/schemas/prompt.schema.v2.json - Prompt schema v1 (deprecated):
.guides/schemas/prompt.schema.json - Persona schema:
.guides/schemas/persona.schema.json - SDD artifact schemas:
- Persona list:
.guides/personas/personas.yaml - Templates:
π License
MIT Β© Guided Engineering Team
π§ Designed by minds, executed by machines.
Social Proof
AI Summary: Based on GitHub metadata. Not a recommendation.
π‘οΈ Tool Transparency Report
Technical metadata sourced from upstream repositories.
π Identity & Source
- id
- gh-tool--guided-engineering--openprompt-specification
- slug
- guided-engineering--openprompt-specification
- source
- github
- author
- Guided Engineering
- license
- tags
- agentic, ai, ia, prompt, specification, sdd, spec-driven-development
βοΈ Technical Specs
- architecture
- null
- params billions
- null
- context length
- null
- pipeline tag
- other
π Engagement & Metrics
- downloads
- 0
- stars
- 25
- forks
- 3
- github stars
- 25
Data indexed from public sources. Updated daily.