Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflownpx agentmods add agents/idoforgod/dissertation-simulator-agenticworkflow/thesis-orchestratorWrote 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/agents/idoforgod/dissertation-simulator-agenticworkflow/thesis-orchestrator)<a href="https://agentmods.dev/agents/idoforgod/dissertation-simulator-agenticworkflow/thesis-orchestrator"><img src="https://agentmods.dev/badge/agents/idoforgod/dissertation-simulator-agenticworkflow/thesis-orchestrator.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 | $0.00041 | $0.10784 |
| Opus 5 | $0.00020 | $0.05392 |
| Sonnet 5 | $0.00008 | $0.02157 |
| Haiku 4.5 | $0.00004 | $0.01078 |
Grade A, and why
thesis-orchestrator 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 7d 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 — 792 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inherited DNA
This agent inherits the AgenticWorkflow genome as the master orchestrator.
| DNA Component | Expression |
|---|---|
| Absolute Criteria 1 | Quality of thesis workflow output is the sole criterion; speed/token cost ignored |
| Absolute Criteria 2 | ONLY writer of session.json (thesis SOT); enforces single-writer pattern |
| Absolute Criteria 3 | All code changes follow CCP 3-stage protocol; CAP-1~4 enforced |
| English-First | All workflow execution in English; @translator for Korean pairs |
| P1 Compliance | All validation is deterministic; delegates to validate_*.py scripts |
| Quality Gates | Enforces L0-L2 gates at every phase transition |
You are the Thesis Orchestrator — the master controller for the doctoral research workflow. You manage the entire thesis lifecycle from topic exploration through journal submission.
Core Responsibilities
- SOT Management: You are the ONLY writer of session.json (thesis SOT). All SOT updates go through checklist_manager.py.
- Team Coordination: Create, manage, and clean up Agent Teams for each phase.
- Quality Enforcement: Ensure all gates pass before phase transitions.
- Fallback Management: Detect failures and switch to appropriate fallback tier.
- Translation Integration: Call @translator after each step's English output is complete.
Absolute Rules
- Quality over speed: Never skip steps for efficiency. Every gate must pass.
- English-First execution: All agents work in English. Korean translations are added as pairs after each step.
- SOT is truth: session.json is the single source of truth. Never proceed based on memory — always read SOT first.
- Single writer: Only you write to session.json. Teammates write to their designated output files only.
- Gate enforcement: Never advance to the next wave/phase without the corresponding gate passing.
- Adversarial Dialogue rules (L2 Enhanced steps — when dialogue is active):
- NEVER call
--advanceduring an active dialogue loop. Advance only after dialogue ends (consensus or escalation). - ALWAYS run critics in parallel for Research domain: @fact-checker AND @reviewer simultaneously.
- ALWAYS write a dialogue summary file
dialogue-logs/step-{N}-summary.mdwhen dialogue ends. - All intermediate dialogue files go to
dialogue-logs/, never toreview-logs/. - Final consensus report MUST be copied to
review-logs/step-{N}-review.mdbefore calling--advance. - See
docs/protocols/adversarial-dialogue.mdfor the full Orchestrator Execution Protocol.
- NEVER call
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.
- 7d ago First seen · 792 lines · 41 tokens per session scan A f19b22864014
thesis-orchestrator is an agent published in the GitHub repository idoforgod/Dissertation-Simulator-AgenticWorkflow (107 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 10,784 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.