Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/idoforgod/dissertation-simulator-agenticworkflow/copilot-instructionsgit clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/idoforgod/dissertation-simulator-agenticworkflow/copilot-instructions)<a href="https://agentmods.dev/instructions/idoforgod/dissertation-simulator-agenticworkflow/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/idoforgod/dissertation-simulator-agenticworkflow/copilot-instructions.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01673 | $0.01673 |
| Opus 5 | $0.00837 | $0.00837 |
| Sonnet 5 | $0.00335 | $0.00335 |
| Haiku 4.5 | $0.00167 | $0.00167 |
Grade A, and why
Dissertation-Simulator-AgenticWorkflow copilot-instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgenticWorkflow — GitHub Copilot 지시서
이 프로젝트에서 작업하는 모든 AI는 AgenticWorkflow 방법론을 따라야 한다.
필수 참조
이 프로젝트의 모든 절대 기준, 설계 원칙, 워크플로우 구조는 AGENTS.md에 정의되어 있다.
Copilot CLI는 AGENTS.md를 자동으로 인식하므로, 해당 파일의 모든 규칙이 자동 적용된다.
상세 아키텍처는 AGENTICWORKFLOW-ARCHITECTURE-AND-PHILOSOPHY.md를 참조한다.
설계 결정 이력은 DECISION-LOG.md를 참조한다.
유전 설계 (DNA Inheritance)
이 프로젝트는 자식 agentic workflow system을 생성하는 부모 유기체이다.
모든 자식 시스템은 부모의 전체 게놈(절대 기준, SOT, 4계층 검증, Safety Hook 등)을 구조적으로 내장한다.
상세: soul.md, AGENTS.md §1.
절대 기준 (핵심 요약)
- 품질 최우선 — 속도, 비용, 작업량보다 최종 결과물의 품질이 유일한 기준
- 단일 파일 SOT — 모든 공유 상태는 단일 파일에 집중. 쓰기 권한은 Orchestrator만
- 코드 변경 프로토콜 (MANDATORY) — 의도 파악 → 영향 범위 분석 → 변경 설계 3단계 수행. 이 프로토콜을 건너뛴 코드 변경은 무효다. 분석 깊이는 변경 규모에 비례. Coding Anchor Points (CAP-1~4): Think before coding, Simplicity first, Goal-driven execution, Surgical changes
상세 내용: AGENTS.md §2 참조.
워크플로우 구조
모든 워크플로우는 3단계: Research → Planning → Implementation.
Copilot 구현 매핑
| AgenticWorkflow 개념 | Copilot 대응 |
|---|---|
| 전문 에이전트 | Copilot의 단일 세션 내 역할 지정 |
| 자동 검증 | GitHub Actions 또는 외부 스크립트로 구현 |
| SOT 상태관리 | state.yaml 파일 — 단일 쓰기 지점 원칙 동일 적용. SOT 파일 형식: state.yaml, state.yml, state.json |
| Autopilot Mode | SOT의 autopilot.enabled 필드로 제어. (human) 단계 자동 승인. Anti-Skip Guard: 산출물 파일 존재 + 최소 100 bytes 검증. AGENTS.md §5.1 참조 |
| ULW (Ultrawork) Mode | 프롬프트에 ulw 포함 시 활성화. Autopilot과 직교하는 철저함 강도 오버레이. 3가지 강화 규칙(Intensifiers): Sisyphus Persistence(3회 재시도) + Mandatory Task Decomposition + Bounded Retry Escalation. AGENTS.md §5.1.1 참조 |
| Verification Protocol | 각 단계 산출물의 기능적 목표 100% 달성 검증. Anti-Skip Guard(물리적) 위에 의미론적 Verification Gate 계층. 검증 기준은 Task 앞에 선언, 실패 시 최대 10회 재시도(ULW 활성 시 15회). AGENTS.md §5.3 참조 |
| pACS (자체 신뢰 평가) | Verification Gate 통과 후 에이전트가 F/C/L 3차원 자기 평가. Pre-mortem Protocol 필수. min-score 원칙. GREEN(≥70): 자동 진행, YELLOW(50-69): 플래그 후 진행, RED(<50): 재작업. AGENTS.md §5.4 참조 |
| Adversarial Review (Enhanced L2) | 기존 L2 Calibration을 대체하는 강화된 품질 검증. @reviewer(코드/산출물 비판적 분석) + @fact-checker(외부 사실 검증). P1 검증(validate_review.py R1-R5)으로 리뷰 품질 보장. AGENTS.md §5.5 참조 |
| Translation Protocol | 영어 산출물 → 한국어 번역. @translator 서브에이전트가 glossary.yaml 기반 용어 일관성 유지. P1 검증(validate_translation.py T1-T9, validate_verification.py V1a-V1c). Review PASS가 Translation의 전제. AGENTS.md §5.2 참조 |
| Predictive Debugging (L-1) | 에러 이력 기반 위험 파일 사전 경고. predictive_debug_guard.py(PreToolUse 경고 전용) + aggregate_risk_scores()(SessionStart P1 집계) + validate_risk_scores()(RS1-RS6 검증). risk-scores.json 캐시. _context_lib.py + docs/protocols/context-preservation-detail.md 참조 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 69 lines · 1,673 tokens per session scan A 03ce025f030b
Dissertation-Simulator-AgenticWorkflow copilot-instructions.md is an instructions file published in the GitHub repository idoforgod/Dissertation-Simulator-AgenticWorkflow (107 stars, last pushed 3mo ago), licensed MIT. It adds 1,673 tokens to every session, about $0.0084 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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