feasibility-ai-offline-writer

name	feasibility-ai-offline-writer
description	Use when users need offline feasibility-study drafting, review, scheme-building, or outline-selection workflows from a recovered prompt corpus, especially when they want reusable system, task, review, expert, and outline prompts without depending on the original app.

FeasibilityAI Offline Writer

Overview

This skill packages the recovered FeasibilityAI prompt corpus into a reusable offline workflow. Use it when we want to draft chapters, review text, revise content, extract structured facts, run consistency checks, build schemes, or select industry outlines without depending on the original application. Keep it scoped to offline prompt reuse and workflow reconstruction.

When to Use

• We need a reusable offline prompt library for feasibility-study or engineering-consulting writing.
• We want to reuse the recovered system, tasks, review, scheme, experts, or outline data.
• We need to inspect prompts by key instead of re-opening the Electron bundle.
• We want a model-agnostic prompt workflow derived from the FeasibilityAI corpus.

Do not use this skill for private API replay, vendor authentication bypass, or extraction of secrets from protected services.

Quick Workflow

1. Pick the workflow family

   • Chapter drafting -> read references/system-prompts.md and fetch the relevant chapter prompt with scripts/lookup_prompt.py
   • QA / review / revise / extract / consistency -> read references/task-prompts.md or references/review-prompts.md
   • Scheme building -> read references/scheme-prompts.md
   • Industry expert alignment -> read references/expert-index.md
   • Outline selection -> read references/outline-index.md and use scripts/select_outline.py

2. Inspect exact prompt entries

   • python scripts/lookup_prompt.py --list-groups
   • python scripts/lookup_prompt.py --group system
   • python scripts/lookup_prompt.py --key system/government
   • python scripts/lookup_prompt.py --search consistency

3. Inspect outline coverage

   • python scripts/select_outline.py --list-majors
   • python scripts/select_outline.py --major A1 --list-subgroups
   • python scripts/select_outline.py --major A1 --subgroup municipal
   • python scripts/select_outline.py --search sewage

Bundled Data

• assets/prompts.json ? full recovered prompt/config corpus
• assets/outlines.json ? full recovered outline/template corpus

References

• references/prompt-taxonomy.md ? prompt families and usage map
• references/system-prompts.md ? system-level writing roles and output rules
• references/task-prompts.md ? generic task prompts for write/review/revise/extract/polish
• references/review-prompts.md ? stricter review-agent prompts
• references/scheme-prompts.md ? interviewer / architect / reviewer trio
• references/expert-index.md ? expert-category map and lookup guidance
• references/outline-index.md ? major/subgroup outline catalog
• references/project-structure.md ? recovered project file structure and local data model

Practical Guidance

• Treat the bundled prompts as prompt resources, not as claims that a specific hosted model is required.
• Prefer reading only the reference file relevant to the current job; use the scripts for exact entry lookup.
• When drafting content, combine:
  1. one system role,
  2. one chapter/task prompt,
  3. optional expert prompt,
  4. optional outline/template constraints.
• When auditing content, prefer review/* over tasks/* if we want stricter audit framing.

Common Patterns

Draft a chapter

• Read references/system-prompts.md
• Fetch chapter config like government_2023/chapters/4_site
• Optionally fetch experts/<category>
• Build the final prompt in your working context

Review and revise a chapter

• Start with review/review or tasks/review
• Then use review/revise or tasks/revise
• Use tasks/extract / review/extract when we need structured facts first

Build a scheme interview flow

• Read references/scheme-prompts.md
• Use scheme/interviewer to ask for missing data
• Use scheme/architect to transform facts into structured patches
• Use scheme/reviewer to check missing technical fields and risks

Notes

• This skill is derived from the locally recovered FeasibilityAI desktop bundle.
• The recovered client labels the upstream provider as deepseek and the default model as deepseek-chat, but this skill is intentionally offline and model-agnostic.