Example: AgentResearchOS — Custom Runtime Design

Prompt template instantiation for: Agent-Native Research Artifact (ARA) system
Paradigm: ARA-first, paper-as-compiled-view, failure-trace as first-class memory
Key constraints: Docker-sandboxed execution · User-supplied model · Zero documentation burden

Template Variable Decisions

Before the design: the template variables as resolved, with explicit justifications for any adaptation or drop.

Variable	Value	Notes
`AGENT_NAME`	`ResearchOS`	Orchestrator + sandbox pool collective
`PRIMARY_PURPOSE`	Distill research sessions into executable ARA artifacts	Narrowed from "do research" to the Live Research Manager pattern
`DOMAIN`	Computational research & ML experimentation	Bounded to code-producing research; avoids wet-lab or clinical scope
`TARGET_USERS`	Researchers + coding agents that consume ARAs as baselines	Dual audience: human authors + downstream agent readers
`TASK_EXAMPLES`	See §11
`RISK_LEVEL`	medium	Results influence downstream agents; hallucinated claims propagate
`AUTONOMY_LEVEL`	semi-autonomous	Human gates on world-model commits and claim promotion
`TOOLS_ALLOWED`	Python/shell in sandbox, git, file I/O, citations API	Sandboxed; no network from worker containers
`TOOLS_FORBIDDEN`	Direct internet from workers, external DB writes, model self-modification
`DATA_SOURCES`	Local repos, ARA artifacts, PDF corpus (via ARA Compiler)
`SUCCESS_METRICS`	Reproduction rate, claim coverage, trace completeness, peer seal pass rate
`DEPLOYMENT_ENV`	Local laptop → cloud-portable	Single `docker compose up` launch
`BUDGET_PRIORITY`	balanced	Routing defined by user; no model hardcoded
`PRIVACY_REQUIREMENTS`	Researcher controls what leaves the container	All execution airgapped inside sandbox
`HUMAN_REVIEW_POINTS`	Claim promotion, world-model merge, ARA Seal submission
`MULTI_AGENT_REQUIRED`	yes	Orchestrator + LRM distiller + sandbox workers
`LONG_TERM_MEMORY`	yes	Git as longitudinal memory; ARA artifact store
`LEARNING_ALLOWED`	constrained	Skills + failure traces; no open-ended world-model generation
`OUTPUT_STYLE`	Executable ARA artifact + compiled human-readable view

Template sections adapted or dropped:

§4 Custom Agent Memory Model — user preferences dropped (irrelevant); cases renamed to failure traces (the core ARA insight); world model scoped to a research landscape (known results, retracted claims, active hypotheses per domain).
§7 Model Routing — all model names replaced with env-var references; no default hardcoded.
§8 Security — expanded with sandbox network policy table; container-level isolation is the primary security primitive, not prompt-level rules.

1. Executive Summary

ResearchOS recasts research session outputs from narrative PDFs into Agent-Native Research Artifacts (ARAs): structured, executable knowledge packages with four interlocking layers — claims, code, failure traces, and raw evidence. A Live Research Manager (LRM) agent sits on top of any coding session and distills the conversation into the ARA in the background. A Compiler agent ingests legacy PDFs/repos into the same format. A Seal agent runs automated verification before human review.

The architecture is not a replacement for scientific judgment. It is a substrate that makes judgment auditable, reproducible, and composable — science that compounds like software.

Why this architecture fits:

Failure traces are the primary differentiator. The filesystem keeps dead ends as ranked, attributed evidence — not narrative prose.
Every code execution runs in an isolated Docker sandbox. Reproducibility is structural, not aspirational.
The model is a user-supplied variable. ResearchOS routes by task difficulty, not by product preference.
Git is the longitudinal memory. Every claim promotion, trace update, and world-model merge is a semantic commit with a machine-readable header.

2. Runtime Shape

Choice: Orchestrator + worker pool (multi-container)

┌─────────────────────────────────────────────────────┐
│  docker compose                                      │
│                                                      │
│  ┌─────────────┐   ┌──────────┐   ┌──────────────┐  │
│  │ orchestrator│   │   lrm    │   │    compiler  │  │
│  │  (main.py)  │◄──│ distiller│   │  (pdf→ARA)   │  │
│  └──────┬──────┘   └──────────┘   └──────────────┘  │
│         │  spawns                                    │
│  ┌──────▼──────────────────────────────────────┐     │
│  │  sandbox worker pool  (ephemeral containers) │     │
│  │  sandbox-1  sandbox-2  sandbox-3  ...        │     │
│  │  each: python + git + no network             │     │
│  └──────────────────────────────────────────────┘    │
│                                                      │
│  shared volume: /data  (ARA store + git repo)        │
└─────────────────────────────────────────────────────┘

Why multi-container:

Code execution must be isolated. Each sandbox is ephemeral and network-gapped; a crashed or infinite experiment cannot affect the orchestrator or the ARA store.
The LRM and Compiler are independent concerns. They read from the session queue and write to the ARA store; they should be independently restartable.
Horizontal scaling is trivial: increase SANDBOX_POOL_SIZE in .env.

3. Purpose-Built Filesystem Layout

Only files that earn their place in a research system are included. Generic agent directories (customers/, inbox/outbox/) are dropped.

/data/
├── .git/                          # longitudinal memory — ALL learning events
│
├── ara/                           # primary artifact store
│   ├── index.md                   # registry of all ARAs with status + claim counts
│   ├── <paper-slug>/
│   │   ├── ara.yaml               # ARA manifest (version, authors, claim list, seal status)
│   │   ├── claims/
│   │   │   ├── <claim-id>.yaml    # structured claim: text, evidence refs, confidence
│   │   │   └── ...
│   │   ├── code/
│   │   │   ├── reproduce.sh       # single-command reproduction entry point
│   │   │   ├── environment.yaml   # conda/pip exact pin
│   │   │   └── src/               # research code (may be symlink to repo)
│   │   ├── traces/
│   │   │   ├── failures/          # dead ends, ranked by hours spent + reason failed
│   │   │   ├── decisions/         # judgment calls with rationale
│   │   │   └── checkpoints/       # intermediate results (not in paper)
│   │   ├── evidence/
│   │   │   ├── raw/               # raw outputs: logs, CSVs, model checkpoints
│   │   │   └── figures/           # generated figures with provenance
│   │   └── views/
│   │       ├── paper.md           # compiled human-readable narrative (generated)
│   │       └── review-packet.md   # seal submission view
│
├── agents/
│   ├── orchestrator/
│   │   ├── persona.md             # read-only
│   │   ├── constraints.md         # read-only: budget caps, sandboxing rules
│   │   ├── skills.md              # earned orchestration patterns (max 20)
│   │   ├── goals.md               # recurring gaps
│   │   ├── rewards.md             # rolling log (last 30)
│   │   └── reflections.md         # failure patterns (last 15)
│   ├── lrm/                       # Live Research Manager
│   │   ├── persona.md
│   │   ├── constraints.md
│   │   ├── skills.md              # distillation patterns
│   │   └── session-queue.md       # FIFO of sessions to distill
│   └── compiler/
│       ├── persona.md
│       ├── constraints.md
│       ├── skills.md              # PDF→ARA conversion patterns
│       └── compile-queue.md
│
├── research-landscape/            # scoped world model — NOT open-ended
│   ├── index.md
│   ├── known-results/             # verified claims from imported ARAs
│   ├── retracted/                 # flagged claims with reason
│   └── hypotheses/                # confidence-gated; require tool-backed evidence
│
├── shared/
│   ├── locks/                     # semaphore files for concurrent writes
│   ├── sandbox-results/           # sandbox workers write here; orchestrator reads
│   └── proposals/                 # world-model update proposals awaiting human gate
│
└── system/
    ├── task-queue.md              # incoming research tasks
    ├── routing-policy.yaml        # model routing rules (no model names hardcoded)
    └── seal-policy.yaml           # automated check thresholds

4. Custom Agent Memory Model

Five memory types are kept. Three from the template are adapted; two are dropped.

Kept (adapted)

Skills — earned research patterns, not just code tricks. Examples: "structured ablation before full run", "cache intermediate tensors to evidence/raw/", "always pin random seeds before logging a claim". Budget: 20 entries. Evidence threshold: ≥2 reproduced +1 outcomes.

Verified Facts — promoted claims from the research landscape. Must have: a tool-backed evidence file reference, a confidence score ≥0.85, and no active contradiction in retracted/. These are the epistemic spine of the ARA.

Failure Traces — the core ARA differentiator. Every dead end is a first-class memory object: what was tried, why it failed, how long it took, and a discount_after timestamp (because a failed approach in 2022 may be worth retrying with a 2026 model). The trace is the "ranked menu of what to try and what not to" mentioned in the ARA paper. Traces are never pruned — they are timestamped and discounted, not deleted.

Hypotheses — patterns with weak evidence. Confidence-gated: cannot become a verified fact without tool-backed confirmation. Human review required before world-model merge.

Procedures — reusable experimental protocols (data split strategy, evaluation harness, baseline selection). Separate from skills because they are domain-specific sequences, not heuristics.

Dropped

User Preferences — ResearchOS serves researchers and downstream agents equally. Personalisation at the memory layer adds drift risk with no reproducibility benefit.

Cases — renamed to Failure Traces (above). The original "cases" framing implied positive examples; the ARA insight is that negative cases are what compounds.

Templates — merged into views/ within the ARA artifact. A compiled paper view is a generated output, not a persistent memory object.

5. Verification Architecture

Three levels; the Seal runs all three before human review.

Level 1 — Structural Integrity
  ├── ara.yaml schema validation (jsonschema)
  ├── all claim IDs referenced in code comments
  ├── evidence files exist and are non-empty
  ├── reproduce.sh is executable and environment.yaml is pinned
  └── no missing cross-references between claims and traces

Level 2 — Argumentative Rigor  
  ├── dual-model review: two model calls (routing policy decides tiers)
  │     each produces: claim-support verdict + confidence + contradiction flags
  ├── hypothesis-to-verified-fact gate: confidence ≥ 0.85 + tool evidence
  ├── retraction cross-check: no promoted claim contradicts retracted/
  └── logical consistency scan (claim → evidence → figure chain)

Level 3 — Execution Reproducibility
  ├── sandbox run: `reproduce.sh` executed in isolated container
  ├── output diffed against stored evidence/raw/ (numeric tolerance configurable)
  ├── runtime logged and compared to claimed compute budget
  └── random seed audit: all seeds must be logged before first model call

Human review gates (not automated):

Claim promotion from hypothesis to verified fact in research-landscape
ARA Seal submission
World-model merge (proposals/ → known-results/)
Any trace discount_after date extension

6. Learning Policy

Constrained skill + failure-trace learning. No open-ended world-model generation.

Rules:

learning_policy:
  skills:
    allowed: true
    budget: 20
    evidence_threshold: 2   # minimum +1 outcomes before a skill is written
    pruning: lowest reward_evidence when budget full
    owner: orchestrator + lrm (each have separate budgets)

  failure_traces:
    allowed: true
    budget: unlimited        # traces are never pruned, only discounted
    discount_after: 18months # agent weights recent traces higher
    attribution: required    # who tried it, when, with what model tier

  verified_facts:
    allowed: true
    gate: human_review + tool_evidence
    confidence_floor: 0.85

  hypotheses:
    allowed: true
    max_age_without_evidence: 90days  # auto-expire to retracted/ if no evidence

  world_model_autonomous_generation:
    allowed: false           # agents propose; humans merge
    reason: "LLMs hallucinate entities and overgeneralize from little data"

  self_modification:
    allowed: false
    reason: "persona.md and constraints.md are read-only"

7. Model Routing Policy

No model names hardcoded anywhere in the codebase. All routing is via system/routing-policy.yaml, populated from environment variables at startup.

# system/routing-policy.yaml (generated from env at startup)
routing:
  tiers:
    fast:
      env_var: RESEARCHOS_MODEL_FAST
      use_for:
        - structural integrity checks (Level 1 Seal)
        - session distillation (routine claims)
        - failure trace formatting
        - skill pruning decisions

    balanced:
      env_var: RESEARCHOS_MODEL_BALANCED
      use_for:
        - argumentative rigor review (Level 2 Seal, first pass)
        - hypothesis confidence scoring
        - ARA compiled paper view generation
        - procedure extraction from session logs

    strong:
      env_var: RESEARCHOS_MODEL_STRONG
      use_for:
        - dual-model review second pass
        - novel claim evaluation (novelty score > threshold)
        - contradiction detection across research landscape
        - world-model merge proposals

  novelty_threshold: 0.7      # above this → always use strong tier
  budget_override: false      # never downgrade strong-tier tasks for cost
  fallback: balanced          # if a tier env_var is unset

This means the same ResearchOS image runs on any provider's model or local inference — the user sets three env vars, and routing follows the declared policy.

8. Security Policy

Risk level: medium. Primary threat: a research agent that promotes hallucinated claims into the shared research landscape, which downstream agents treat as ground truth.

Network policy (the sandbox is the primary control)

Container          Outbound network     Notes
─────────────────────────────────────────────────────
orchestrator       LLM API only         via egress proxy; no raw internet
lrm                LLM API only
compiler           LLM API + citations  citations API is the only external data source
sandbox-worker     NONE                 fully airgapped; reads/writes via /data volume only

Worker containers are launched with --network none. All inputs are written to the shared volume before launch; all outputs are read back after exit.

Secret handling

.env (never committed)
  RESEARCHOS_MODEL_FAST=...
  RESEARCHOS_MODEL_BALANCED=...
  RESEARCHOS_MODEL_STRONG=...
  LLM_API_KEY=...
  CITATIONS_API_KEY=...

Secrets injected at runtime via env; never written to /data or .git
Pre-commit hook rejects commits containing key patterns (regex scan)

PII and logging

Researcher names are stored only in ara.yaml author fields, not in logs.
Sandbox stdout/stderr is captured to evidence/raw/<run-id>.log — not streamed to external services.
rewards.md and reflections.md contain no user-identifying information.

Approval gates

Action	Gate
hypothesis → verified fact	human review required
proposals/ → known-results/	human review required
ARA Seal submission	human sign-off on Level 2 + 3 results
sandbox `reproduce.sh` execution	automatic (sandboxed)
failure trace `discount_after` extension	human review

9. Docker Project Files

`Dockerfile`

FROM python:3.12-slim

# System deps
RUN apt-get update && apt-get install -y --no-install-recommends \
    git curl jq \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Python deps
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# App code
COPY src/ ./src/
COPY agents.yaml .
COPY system/ ./system/

# Git config for semantic commits
RUN git config --global user.email "researchos@local" \
 && git config --global user.name "ResearchOS"

# Init /data if not already a git repo (handled at entrypoint)
COPY entrypoint.sh .
RUN chmod +x entrypoint.sh

ENTRYPOINT ["./entrypoint.sh"]

`entrypoint.sh`

#!/bin/bash
set -e

# Initialize /data git repo on first run
if [ ! -d /data/.git ]; then
  git -C /data init
  git -C /data commit --allow-empty -m "init: ResearchOS data store initialized"
fi

# Generate routing-policy.yaml from env
python src/generate_routing_policy.py

# Start main task loop
exec python src/main.py

`docker-compose.yml`

services:
  orchestrator:
    build: .
    image: researchos:latest
    volumes:
      - researchos-data:/data
      - /var/run/docker.sock:/var/run/docker.sock  # spawn sandbox containers
    env_file: .env
    environment:
      - AGENT_ROLE=orchestrator
      - SANDBOX_IMAGE=researchos-sandbox:latest
      - SANDBOX_POOL_SIZE=3

  lrm:
    image: researchos:latest
    volumes:
      - researchos-data:/data
    env_file: .env
    environment:
      - AGENT_ROLE=lrm
    depends_on:
      - orchestrator

  compiler:
    image: researchos:latest
    volumes:
      - researchos-data:/data
    env_file: .env
    environment:
      - AGENT_ROLE=compiler
    depends_on:
      - orchestrator

volumes:
  researchos-data:
    driver: local

`Dockerfile.sandbox`

# Sandbox worker — airgapped, ephemeral, no LLM calls
FROM python:3.12-slim

RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /workspace

# Sandbox gets a copy of the ARA's environment.yaml at runtime
COPY sandbox_runner.py .

ENTRYPOINT ["python", "sandbox_runner.py"]
# Launched by orchestrator with:
#   docker run --rm --network none \
#     -v researchos-data:/data:ro \
#     -v <run-dir>:/workspace \
#     researchos-sandbox:latest

`agents.yaml`

agents:
  orchestrator:
    persona: agents/orchestrator/persona.md
    constraints: agents/orchestrator/constraints.md
    skills_budget: 20
    reward_window: 30
    reflection_window: 15
    task_queue: system/task-queue.md

  lrm:
    persona: agents/lrm/persona.md
    constraints: agents/lrm/constraints.md
    skills_budget: 20
    session_queue: agents/lrm/session-queue.md
    distillation_mode: background  # zero burden on researcher

  compiler:
    persona: agents/compiler/persona.md
    constraints: agents/compiler/constraints.md
    skills_budget: 20
    compile_queue: agents/compiler/compile-queue.md
    output_format: ara_v1

sandbox:
  image: researchos-sandbox:latest
  network: none
  mem_limit: 8g
  cpu_limit: 4.0
  timeout_seconds: 3600
  results_path: shared/sandbox-results/

`env.example`

# Model routing — fill in your own model identifiers; no defaults enforced
RESEARCHOS_MODEL_FAST=
RESEARCHOS_MODEL_BALANCED=
RESEARCHOS_MODEL_STRONG=

# API keys
LLM_API_KEY=
CITATIONS_API_KEY=

# Sandbox pool
SANDBOX_POOL_SIZE=3
SANDBOX_MEM_LIMIT=8g

# ARA store
ARA_STORE_PATH=/data/ara
RESEARCH_LANDSCAPE_PATH=/data/research-landscape

# Seal thresholds
SEAL_L1_REQUIRED=true
SEAL_L2_CONFIDENCE_FLOOR=0.85
SEAL_L3_NUMERIC_TOLERANCE=1e-4

# Git identity for semantic commits
GIT_AUTHOR_EMAIL=researchos@local
GIT_AUTHOR_NAME=ResearchOS

Startup

# Build both images
docker build -t researchos:latest .
docker build -f Dockerfile.sandbox -t researchos-sandbox:latest .

# Copy and fill env
cp env.example .env
# ... edit .env with your model identifiers and API keys ...

# Launch
docker compose up

# Queue a task (from host)
echo "- id: task-001\n  type: compile\n  source: /data/pdfs/attention-is-all-you-need.pdf" \
  >> /path/to/researchos-data/system/task-queue.md

10. Observability

Metrics are research-specific. Generic "task success rate" is replaced with signal that reveals whether the agent is actually improving science throughput.

Metric	Source	Target
Claim coverage rate	claims/ count vs. paper section count	> 85%
Trace completeness	failure entries per ARA	≥ 3 per major experiment
Reproduction pass rate	Seal Level 3 auto-run	> 90%
Claim promotion lag	hypothesis created → verified fact	< 7 days median
Hallucination incidents	Level 2 dual-model disagreement rate	< 5%
Seal rejection rate	L1/L2/L3 auto-fail before human review	Track trend; rising = distiller regression
Skill accumulation curve	skills.md entries over time	Plateau = healthy; churn = policy problem
Failure trace discount rate	traces past `discount_after` date	Flag for human review
Sandbox crash rate	non-zero exit from sandbox workers	< 2%
Model tier usage ratio	fast : balanced : strong call counts	Track cost vs. claim quality correlation

All metrics are derived from files already in /data — no external observability stack required in v1. A simple python src/metrics.py command generates a system/metrics-report.md on demand.

11. Example Tasks (10 Realistic)

Distill a 4-hour coding session into a new ARA for a gradient checkpointing experiment — LRM extracts 3 claims, 2 dead ends, 1 confirmed performance gain.
Compile a legacy PDF ("Attention Is All You Need") into an ARA: extract structured claims, map figures to evidence files, flag missing reproduction code.
Run a Seal check on an existing ARA — Level 3 catches that reproduce.sh produces output that diverges from evidence/raw/ by 2.3% (outside tolerance).
Rank failure traces for a new researcher starting on transformer quantization — retrieve all traces tagged quantization, sorted by recency and discount_after status.
Propose a world-model update: after three independent +1 reproductions of a new result, draft a proposals/ entry for human gate review.
Extend an existing ARA with a new ablation result — add claim, attach sandbox output as evidence, update reproduce.sh, commit with semantic header.
Detect a contradiction: a new claim conflicts with a known-results/ entry — flag both, write a hypotheses/ entry for the disagreement, queue for dual-model review.
Generate a compiled paper view from an ARA — produce views/paper.md as a narrative document from structured claims, evidence, and trace summaries.
Expire a stale hypothesis: a hypotheses/ entry with no new evidence in 90 days is auto-moved to retracted/ with reason evidence_timeout.
Horizontal scale test: spin 5 sandbox workers simultaneously, each reproducing a different ARA; results written back to shared/sandbox-results/ without collision (lock files enforced).

12. Risks & Failure Modes

Risk	Likelihood	Impact	Mitigation
LRM hallucinates a claim not in the session	Medium	High — propagates to research landscape	Dual-model Level 2 check; human gate on promotion
Sandbox reproduce.sh hangs indefinitely	Medium	Low — orchestrator unblocked	`timeout_seconds` in agents.yaml; container killed on expiry
Git history grows unbounded	Low	Medium — slow clones	`git gc` cron in orchestrator; large binary artifacts stored via Git LFS
Failure trace `discount_after` not updated	Medium	Medium — agent avoids valid approaches	Human review queue for expiring traces
Strong-tier model unavailable (API outage)	Low	High — Seal Level 2 blocked	Fallback to balanced + flag for delayed human review
Retracted claim re-promoted by compiler	Low	High	Cross-check against `retracted/` before any promotion
Sandbox worker escapes network isolation	Very low	Critical	`--network none` is Docker-enforced, not policy-enforced
Two agents write to same ARA simultaneously	Medium	Medium	Lock files in `shared/locks/` before any ARA write
Skill budget full with low-quality skills	Low	Low	Pruning on lowest `reward_evidence`; periodic human audit
Trace `discount_after` becomes a ceiling for strong agents	Medium	Low in v1	Add provenance tags to traces; successors can selectively discount

13. Recommended v1 Scope (2 weeks)

Ship exactly these four things, nothing else:

Week 1

Docker images build and docker compose up succeeds with a test ARA.
LRM distiller: takes a session transcript (plain text), produces a valid ara.yaml + at least one claim file + at least one failure trace. No world-model writes yet.
Sandbox worker: runs reproduce.sh from an existing ARA, writes output to shared/sandbox-results/, exits cleanly.

Week 2

Seal Level 1 (structural integrity) runs automatically after every LRM distillation.
Semantic git commits: every claim addition and trace write produces a correctly formatted commit.
metrics.py generates a system/metrics-report.md covering claim count, trace count, and sandbox pass rate.
env.example fully documented; first-run experience requires only filling in API keys and model names.

Explicitly out of scope for v1:

ARA Compiler (PDF ingestion) — add in v2
Seal Level 2 and 3 — add in v2
Research landscape / world model — add in v2
Horizontal sandbox scaling — add in v2

14. v2 Expansion Plan

Ordered by value delivered per effort, based on the ARA paper's empirical findings.

High value, low risk

Seal Level 3 (sandbox reproduction as gating check before any human review).
ARA Compiler: PDF → ARA for legacy literature. Start with arXiv HTML format; PDF is harder.
Failure trace discount scoring: weight traces by age × model-tier-at-time-of-failure. This is the mechanism that prevents the trace from becoming a ceiling for strong agents.

Medium value, medium risk

Research landscape (known-results + hypotheses): start read-only (populated by compiler), add write path only after Level 2 Seal is stable.
Horizontal sandbox scaling: SANDBOX_POOL_SIZE > 1 with lock-file coordination.
Seal Level 2 dual-model review: add once you have enough ARAs to calibrate the confidence floor.

Longer term

ARA-native peer review protocol: reviewers attach signed review packets to artifacts, not to compiled paper views.
Forking: git fork an ARA as a starting point for a new experiment, with provenance preserved.
Provenance tags on failure traces: let downstream agents query "which traces were generated under model-tier X and base compute Y" and selectively discount.
(Human+AI)² Research Network: multiple ResearchOS instances sharing a common research landscape via a git remote, with merge-request style human gates on world-model updates.

Architecture follows the AgentOS blueprint: filesystem as state, rewards as signal, Git as institutional memory, Docker as reproducible runtime. The ARA is not a document format — it is the primary research object. The paper is a view.

MuhammadYossry/AgentResearchOS_example.md

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