한국어 · Quick start · Analyze prompts · Security boundary

Reverse analyze existing prompts before agents run

prompt-ops-maker

AI agents ship better when the prompt includes scope, deny lists, verification gates, and a result-first report format. Those rules are easy to forget when every project uses a different model or runtime.

prompt-ops-maker generates reusable operating prompts for Claude Fable 5-style long tasks, Claude, Codex, Hermes, MCP agents, Gemini, and generic AI agents.

One command. Project config or preset in. Verification-focused agent prompt out.

One-line result

prompt-ops-maker = prompt generator and local analyzer for agent scope + safety boundaries + verification gates

Security-first reverse analysis

Existing prompts often miss the operational rules that keep AI agents safe during long tasks. The analyze command checks those rules locally before the prompt is reused.

prompt-ops-maker analyze --input existing-prompt.txt --format text
prompt-ops-maker analyze --input existing-prompt.txt --format json --output analysis.json

Example report shape:

# Prompt Ops Analysis — existing-prompt.txt

Score: 14/100

## Summary
- Present checks: 2
- Missing checks: 5
- Secret-like patterns: 1 detected
- Source policy: deterministic local heuristics; no AI call; no secret value echo

## Missing / Risk Items
- [BLOCKER] secret_literal_risk
- [HIGH] deny_list
- [HIGH] verification_gates
- [MEDIUM] evidence_first_report
- [MEDIUM] tool_result_grounding

## Checks
- Execution boundary: present (BLOCKER)
- Unverified item reporting: present (MEDIUM)
- Verification gates: missing (HIGH)

The analyzer uses deterministic local checks only. It does not infer private infrastructure and does not print detected secret values.

Why this exists

Agent tasks fail in repeatable ways:

Problem                                    What breaks
No execution boundary                      Agent edits, deploys, or uploads before approval
No verification gate                       Generated prompt is mistaken for completed work
No target-runtime context                  Claude, Codex, MCP, and CI receive the same vague prompt
No deny list                               Secrets, customer data, and env values can leak into output
No result-first report format              Long logs hide blockers and unverified items
No project preset                          Every audit/fix/deploy prompt is rewritten from scratch

These are not model problems only. They are operating-system problems for AI work. prompt-ops-maker packages the missing rules into prompts that can be reused across projects and agents.

It does not claim Fable 5-equivalent model performance. It reuses Fable 5 prompting-guide patterns as model-agnostic prompt operations: effort level, execution boundaries, verification gates, evidence-first reporting, and explicit unverified-item reporting.

What it produces

Input                    Output
────────────────────────────────────────────────────────────────────────────
Project YAML config      A project-aware agent prompt
Type preset              An ad-hoc agent prompt without a project config
Existing prompt file     Reverse-analysis for missing ops boundaries
Task description         Concrete objective and risk focus
Target AI                Claude / Codex / Hermes / MCP / Gemini / generic guidance
Runtime environment      local / mcp / discord / ci / browser / api instructions
Effort level             low / medium / high / xhigh behavior
Deny list                Actions the agent must not take without approval
Verification gates       Commands, files, URLs, or evidence the agent must check
Report sections          conclusion, evidence, BLOCKER/HIGH/MEDIUM/LOW, unverified items

Usage

Install from GitHub

git clone https://github.com/verisworks-ai/prompt-ops-maker.git
cd prompt-ops-maker
python3 -m pip install -e '.[test]'
prompt-ops-maker list-types

Direct script use

python3 prompt_ops_maker.py list-projects
python3 prompt_ops_maker.py list-types

fable5_prompt_maker.py remains as a compatibility wrapper for older local workflows.

Quick start

Project prompt

prompt-ops-maker make \
  --project mobile-miniapp \
  --mode ad-qa \
  --task "Ad integration QA" \
  --effort high \
  --target-ai codex \
  --environment local \
  --dry-run

Ad-hoc prompt without a project config

prompt-ops-maker make-adhoc \
  --name "Webhook monitor" \
  --type automation-pipeline \
  --task "Operational audit" \
  --risk "duplicate runs, secret output, failed alerts" \
  --effort high \
  --target-ai hermes \
  --environment mcp \
  --dry-run

Analyze an existing prompt

prompt-ops-maker analyze \
  --input existing-prompt.txt \
  --format text

This reverse-analysis mode uses deterministic local checks only. It does not call an AI model, does not infer private infrastructure, and does not echo detected secret values. It reports missing execution boundaries, deny lists, verification gates, unverified-item handling, and evidence-first reporting.

Structured output is available for automation:

prompt-ops-maker analyze --input existing-prompt.txt --format json --output analysis.json
prompt-ops-maker analyze --input existing-prompt.txt --format yaml --output analysis.yaml

Save a prompt file

prompt-ops-maker make \
  --project brand-hub \
  --mode seo-geo \
  --task "Public search and AI citation audit" \
  --effort high \
  --target-ai claude \
  --environment discord \
  --output outputs/brand-hub-seo-geo.md

Example output

# DRY RUN — mobile-miniapp / ad-qa / high / codex / local

You are the Mobile Miniapp ad QA agent.

이번 작업은 high effort로 진행해.
대상 AI/실행자: Codex
실행 환경: Local CLI
수정하지 말고 광고 연동 상태만 평가해.

검증 게이트:
1. Bundle artifact output 확인
2. Full-screen ad load/show 경로 확인
3. 실제 테스트 결과와 미검증 항목 분리

금지:
- 파일 수정
- 콘솔 변경
- live 광고 ID 반복 테스트
- secret 출력

보고 형식:
첫 문장은 사용자가 가장 궁금해할 결과 하나만 말해.
내부 추론은 쓰지 말고, 결론 / 확인한 증거 / 미검증 항목만 보고해.

Built-in modes

Mode       Purpose
────────────────────────────────────────────────────────────────────────────
audit      Launch and operations audit
fix        Approved minimal fixes
deploy     Release or upload readiness checks
ad-qa      Ads integration QA
seo-geo    Search, GEO, and AI citation readiness
appsec     Public/private/security boundary audit
ux         User flow and mobile UX audit

Built-in targets

Target    Use when
────────────────────────────────────────────────────────────────────────────
fable5    Claude Fable 5-style long-task prompt structure
claude    General Claude workflow
codex     Code implementation, tests, and verification
hermes    Hermes Agent skill/tool/gateway environment
mcp       MCP tool/resource/prompt environment
gemini    Research, drafts, and comparison analysis
generic   Generic AI agent

Built-in environments

Environment  Evidence focus
────────────────────────────────────────────────────────────────────────────
local        Local CLI, files, git, tests
mcp          MCP tools, resources, and prompts
discord      Discord status and result reporting
ci           CI/CD logs and artifacts
browser      Browser and UI verification
api          API/server verification
generic      Generic runtime

Type presets

Preset               Use case
────────────────────────────────────────────────────────────────────────────
generic              Generic task
automation-pipeline  Scheduler, batch, alert, webhook operations
web-public           Public web service readiness
mobile-miniapp       WebView, miniapp, SDK, ads bundle readiness

Project configs

Add a private YAML file under configs/<project>.yaml, or publish sanitized examples under configs/examples/<project>.yaml:

project:
  name: "Example Service"
  type: "web-public"
  root: "/path/to/public-web-service"
  domain: "https://example.com"
  agent_role: "Example Service audit agent"
  description: "Public web service example."

core_focus:
  - "Public page UX"
  - "robots.txt, sitemap.xml, JSON-LD"

verification_gates:
  - "Run tests"
  - "Check live HTTP status"

forbidden:
  - "Unapproved deploy"
  - "Secret output"

Then run:

prompt-ops-maker make --project example --mode audit --task "Launch audit" --effort high --dry-run

Practical scenarios

Before a release — force evidence before claims

prompt-ops-maker make \
  --project brand-hub \
  --mode deploy \
  --task "Production release gate" \
  --effort xhigh \
  --target-ai codex \
  --environment ci \
  --dry-run

Use this when a project needs source checks, build output checks, live smoke checks, and explicit approval boundaries.

For MCP agents — separate tool output from final claims

prompt-ops-maker make-adhoc \
  --name "MCP server audit" \
  --type automation-pipeline \
  --task "Check available tools, permissions, and side effects" \
  --risk "credential leakage, tool side effects" \
  --target-ai mcp \
  --environment mcp \
  --dry-run

For Claude or Gemini — keep drafts from becoming fake verification

prompt-ops-maker make-adhoc \
  --name "Market research handoff" \
  --type generic \
  --task "Compare options and list unverified assumptions" \
  --target-ai gemini \
  --environment generic \
  --dry-run

Architecture

prompt-ops-maker/
├── prompt_ops_maker.py          ← public CLI entry
├── fable5_prompt_maker.py       ← compatibility wrapper
├── configs/
│   ├── examples/
│   │   ├── mobile-miniapp.yaml                 ← sanitized example config
│   │   ├── public-real-estate-service.yaml     ← sanitized example config
│   │   └── brand-hub.yaml                      ← sanitized example config
│   └── _types/
│       ├── generic.yaml
│       ├── automation-pipeline.yaml
│       ├── web-public.yaml
│       └── mobile-miniapp.yaml
├── tests/test_prompt_maker.py   ← CLI, prompt, and analyzer regression tests
├── service-ontology.json        ← optional service ontology manifest
├── examples/README.md
└── docs/ontology-notes/README.md

Verification

python3 -m pytest -q
python3 prompt_ops_maker.py list-projects
python3 prompt_ops_maker.py list-types
python3 prompt_ops_maker.py make --project brand-hub --mode audit --task 'smoke test' --effort high --target-ai codex --environment local --dry-run
python3 prompt_ops_maker.py analyze --input README.md --format json

Current release smoke:

pytest                         9 passed
list-projects                  examples/brand-hub, examples/mobile-miniapp, examples/public-real-estate-service
list-types                     automation-pipeline, generic, mobile-miniapp, web-public
clean editable install          prompt-ops-maker list-types OK

Security and privacy boundary

Do not store API keys, tokens, private keys, customer data, or full .env values in configs.
Use placeholder paths in examples.
Treat generated prompts as instructions, not proof that the target project was verified.
Keep deploy, upload, database, and account-setting changes behind explicit approval.
Secret values should never appear in generated prompts or reports.

Requirements

Python 3.10+

License

MIT — veris · [email protected]