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/abdelstark/worldforge/agents-mdgit clone --depth 1 https://github.com/AbdelStark/worldforgeWhat 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 | $0.08488 | $0.08488 |
| Opus 5 | $0.04244 | $0.04244 |
| Sonnet 5 | $0.01698 | $0.01698 |
| Haiku 4.5 | $0.00849 | $0.00849 |
Grade A, and why
worldforge AGENTS.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 — 554 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Guide
Project Identity
WorldForge is a harness framework for building world-model-based workflows for physical AI. It is the application builder's counterpart to model-training stacks like Stable World Model: it helps roboticists and physical-AI builders compose, evaluate, and benchmark workflows built on top of world models, not train them. It is a library and CLI, not a hosted service, on-chain contract, or end-user application.
The whole framework is organized around one backbone loop: planning and scoring action candidates
with an action-conditioned predictive world model, in latent space. A policy provider proposes
candidate actions, a predict provider rolls them out as forward dynamics, a score provider ranks
them as a cost oracle, and LatentMPCController owns the CEM/receding-horizon optimizer that ties
them together. Evaluation and benchmarking sit on top so a builder can measure and select the best
provider/configuration. World generation, symbolic-scenario simulation, and media generation are out
of scope.
Intended users are Python developers and roboticists composing provider-backed planning loops, evaluation harnesses, and benchmarks to choose configurations for physical-AI tasks.
Architecture Map
src/worldforge/models.py: public compatibility facade that re-exports model contracts, validation errors, shared helpers, provider contracts, scene models, and capability results.src/worldforge/_model_utils.py: shared JSON-native validation helpers, framework errors, deterministic IDs, and numeric/probability checks re-exported throughmodels.py.src/worldforge/scene_models.py: geometry primitives, actions, scene objects, and scene patches used to build providerworld_statedicts (no symbolic world runtime).src/worldforge/capability_results.py: embedding, action-score, and embodied-policy result payload contracts.src/worldforge/provider_models.py: public compatibility facade for provider-facing contracts.src/worldforge/provider_profiles.py: provider capabilities, provider info, and profile metadata.src/worldforge/provider_request_policy.py: retry/backoff and operation timeout policies for HTTP-backed providers.src/worldforge/provider_events.py: structured provider events and event-field validation.src/worldforge/provider_diagnostics.py: provider health, lifecycle readiness, and doctor report models.src/worldforge/provider_redaction.py: observable-field sanitization shared by provider events, diagnostics, manifests, routing, and reports.src/worldforge/capabilities/__init__.py: runtime-checkable capability protocols for narrowCost,Policy,Predictor,Embedder, andRunnableModelintegrations.src/worldforge/control/: score-driven controller primitives.LatentMPCControllersamples action horizons in pure Python, scores them throughscore_actions, and returns an optimized chunk without importing optional ML runtimes or stepping host environments.src/worldforge/framework_capabilities.py: internal capability-protocol registry, structural dispatch, observable-wrapper ownership, and direct/named capability target resolution.src/worldforge/framework.py:WorldForge, provider registration, capability dispatch (predict/score/policy/embed), diagnostics, provider operations, and top-level evaluation helpers. The backbone planner iscontrol.LatentMPCController, which callsscore_actions.src/worldforge/providers/base.py: provider interfaces,ProviderError, remote-provider base behavior, andPredictionPayload.src/worldforge/providers/observable.py: internal wrapper that addsProviderEvent, health, profile, info, and timing behavior around pure capability protocol implementations.src/worldforge/providers/catalog.py: provider factories and auto-registration policy for the in-repo provider catalog.src/worldforge/providers/mock.py: deterministic local provider used by tests, examples, and contract checks.src/worldforge/providers/cosmos_policy.py: host-owned NVIDIA Cosmos-Policy ALOHA/actserver adapter for selecting embodied action chunks through thepolicycapability.src/worldforge/providers/leworldmodel.py: real optional LeWorldModel JEPA cost-model adapter for scoring action candidates throughstable_worldmodel.policy.AutoCostModel.src/worldforge/providers/gr00t.py: experimental host-owned NVIDIA Isaac GR00T PolicyClient adapter for selecting embodied action chunks through thepolicycapability.src/worldforge/providers/lerobot.py: host-owned Hugging Face LeRobotPreTrainedPolicyadapter for selecting embodied action chunks through thepolicycapability.src/worldforge/providers/jepa_wms.py: candidate contract scaffold forfacebookresearch/jepa-wmsscore-provider work; it supports injected test/runtime scoring and a host-owned torch-hub runtime but is intentionally not exported or registered.src/worldforge/providers/remote.py: credential-gated scaffold providers forjepaandgenie; these intentionally use deterministic mock behavior after credential checks.src/worldforge/evaluation/: built-in physics and planning suites plus report renderers.src/worldforge/benchmark.py: capability-aware provider latency, retry, and throughput harness.src/worldforge/observability.py: composableProviderEventsinks for JSON logging, in-memory recording, and metrics aggregation.src/worldforge/workflow_trace.py: JSON-native composed workflow trace artifacts for planning, evaluation, provider-event conversion, Markdown export, and optional Rerun logging.src/worldforge/rerun.py: optional Rerun SDK bridge for sanitized provider events, world snapshots, plans, workflow traces, benchmark reports, robotics showcase visual layers, and JSON artifacts. Rerun is not a provider capability and stays behind thererunextra or host-owned optional runtimes that already providererun-sdk.src/worldforge/testing/: reusable adapter contract helpers, fixture loaders, fixture snapshot manifest helpers, runtime markers, and deterministic controls for artifact/report tests.src/worldforge/demos/: packaged demo entry points exposed throughuv runconsole scripts.src/worldforge/demos/lerobot_e2e.py: packaged LeRobot policy-plus-score planning demo exposed throughuv run worldforge-demo-lerobot.src/worldforge/demos/rerun_showcase.py: packaged Rerun observability and artifact showcase exposed throughuv run --extra rerun worldforge-demo-rerun.scripts/demo_showcases.py: checkout-safe demo evidence runner for the first-run, diagnostics, robotics replay, remote dry-run, adapter authoring, batch eval, service host, Rerun gallery, failure lab, cookbook, external provider package, custom evaluation suite, and policy+score candidate lab, fixture drift review, capability negotiation preflight, and embodied policy replay comparison, non-developer evidence review, and provider failure gallery workflows.scripts/release_readiness_drill.py: checkout-safe release readiness drill that renders clean-pass and controlled-failure release-evidence artifacts without publishing, tagging, signing, or running host-owned optional runtimes.scripts/generate_release_evidence.py: checkout-safe release evidence generator that records validation gate status, sanitized command output, artifact hashes, live-smoke manifest links, known limitations, and claim boundaries without publishing, tagging, or signing.scripts/generate_dependency_audit_evidence.py: checkout-safe dependency-audit evidence wrapper around the documenteduv exportpluspip-auditflow; writes JSON and Markdown summaries with sanitized raw details and without preserving the temporary requirements file.scripts/generate_quality_dashboard.py: local quality dashboard generator that reads release evidence, dependency-audit evidence, and core-performance output and emits JSON/Markdown status summaries with sanitized raw details and deterministic redacted-key collision handling, without running gates.scripts/generate_release_notes.py: maintainer-editable release notes draft generator that assemblesCHANGELOG.md, optional closed GitHub issue metadata, release evidence JSON, validation summaries, row-level validation gate status, caveats, and host-owned optional runtime evidence through shared text redaction, without publishing.src/worldforge/harness/: robotics showcase flow/report package. Keep flow metadata and runners independent from Textual;tui.pyis the only Textual-dependent module. Current flows cover LeWorldModel score planning, LeRobot policy-plus-score planning, Cosmos-Policy ALOHA replay, GR00T PolicyClient replay, and robotics policy replay comparison.src/worldforge/harness/tui_styles.py: Textual-free CSS constants consumed bytui.py; keep styling declarations here instead of embedding large CSS strings in widget classes.src/worldforge/smoke/: packaged optional-runtime smoke entry points exposed throughuv runconsole scripts.src/worldforge/smoke/lerobot_leworldmodel.py: optional host-owned real robotics showcase that composes a LeRobot policy checkpoint with a LeWorldModel score checkpoint throughforge.select_actions(policy) plusforge.score_actions(score) candidate ranking.src/worldforge/smoke/robotics_showcase.py: one-command PushT real robotics showcase that wires the packaged PushT observation, score, translator, and candidate bridge defaults intolewm-lerobot-real.src/worldforge/smoke/pusht_showcase_inputs.py: packaged PushT showcase hooks for building the LeRobot observation, LeWorldModel score tensors, and checkpoint-native action candidates.src/worldforge/smoke/leworldmodel_checkpoint.py: optional host-owned builder for creating the LeWorldModel*_object.ckptfile expected byAutoCostModelfrom Hugging Face LeWM assets.examples/leworldmodel_e2e_demo.py: checkout-safe end-to-end LeWorldModel provider-surface score-planning compatibility wrapper foruv run worldforge-demo-leworldmodel.examples/lerobot_e2e_demo.py: checkout-safe end-to-end LeRobot policy-plus-score planning compatibility wrapper with an injected deterministic policy.scripts/generate_provider_docs.py: provider catalog documentation generator and drift check.scripts/check_docs_snippets.py: checkout-safe snippet gate for selected Python and JSON docs blocks, with explicit host-owned, credentialed, and illustrative skip markers.scripts/check_optional_import_boundaries.py: checkout-safe static and import-time audit that keeps Textual, Rerun, torch, stable-worldmodel, LeRobot, GR00T, and Cosmos-Policy imports behind their allowed optional-runtime modules.scripts/scaffold_provider.py: safe scaffold generator for new provider adapter files, fixture placeholders, tests, runtime manifest stubs, docs stubs, and workbench checklists.scripts/smoke_leworldmodel.py: compatibility wrapper foruv run --python 3.13 --with "stable-worldmodel @ git+https://github.com/galilai-group/stable-worldmodel.git" --with "datasets>=2.21" worldforge-smoke-leworldmodel.scripts/smoke_gr00t_policy.py: optional live GR00T PolicyClient smoke for host environments with Isaac-GR00T or a reachable policy server; startup command logs must redact forwarded secret-shaped server args and host-local paths.scripts/smoke_cosmos_policy.py: optional live Cosmos-Policy/actsmoke for host environments with a reachable ALOHA policy server.scripts/smoke_lerobot_policy.py: optional live LeRobotPreTrainedPolicysmoke for host environments with LeRobot and robot-specific dependencies.
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 · 554 lines · 8,488 tokens per session scan A afae2ad07a6f
worldforge AGENTS.md is an instructions file published in the GitHub repository AbdelStark/worldforge (108 stars, last pushed 24d ago), licensed MIT. It adds 8,488 tokens to every session, about $0.0424 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 instructions, from other repositories
autonomous-os CLAUDE.md
Instructions for autonomous-ai/autonomous-os, covering claude.md, multi-ide rules (cursor + claude code), subagent usage, device access rules and project overview.
autonomous-os AGENTS.md
Instructions for autonomous-ai/autonomous-os, covering agents.md, multi-ide rules, working style, parallel work / subagents and device access rules.
inspect-robots CLAUDE.md
Instructions for robocurve/inspect-robots, covering inspect robots — agent guide, the one big idea, layout, working here and out of scope (separate repos / plugins).
marimo AGENTS.md
Instructions for marimo-team/marimo, covering marimo development guidelines, your primary responsibility is to the project and its users, quick setup, development commands and python.
streamlit copilot-instructions.md
Copilot instructions for streamlit/streamlit, covering streamlit repo overview, tech stack, folder structure, shell & build policy and make commands.
gradio AGENTS.md
AGENTS.md instructions for gradio-app/gradio, covering agents.md, repository structure, pull request rules, code style and agentic contribution policy.