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Agent skill

Neqsim Agent Handoff

by equinor in equinor/neqsim

Agent-to-agent communication schema for NeqSim. An agent skill from equinor/neqsim.

Apache-2.0Auto-check passedAgent Workflows

Install Neqsim Agent Handoff

skills CLI
$ npx skills add equinor/neqsim --skill neqsim-agent-handoff -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install equinor/neqsim neqsim-agent-handoff --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/equinor/neqsim.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/neqsim-agent-handoff .claude/skills/neqsim-agent-handoff && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
neqsim-agent-handoff
GitHub stars
156
Token cost
~2.2k tokens
SKILL.md length
457 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

Agent-to-agent communication schema for NeqSim. An agent skill from equinor/neqsim.

  • Works in 4 steps: Explicit over implicit — include all… → Units always included — every numerical… → Code-ready — the receiving agent can… → …
  • : composing multi-agent pipelines where one agents output feeds another agents input
  • SKILL.md covers Handoff Principles, Schema 1: Fluid Definition…, Schema 2: Process Simulation… and Schema 3: Mechanical Design…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Neqsim Agent Handoff is an agent skill from equinor/neqsim. Agent-to-agent communication schema for NeqSim. USE WHEN: composing multi-agent pipelines where one agent's output feeds another agent's input. Defines structured result formats for fluid definitions, simulation results, and design outputs that agents can pass to each other.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Multi-agent orchestration. The repository describes itself as: NeqSim is a library for calculation of fluid behavior, phase equilibrium and process simulation. The licence is Apache-2.0.

When your agent uses it

  • : composing multi-agent pipelines where one agents output feeds another agents input
  • Tasks that involve Multi-agent orchestration

Example prompts

  • “s output feeds another agent”
  • “/neqsim-agent-handoff”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Explicit over implicit — include all parameters, don't assume the receiving agent can infer
  2. Units always included — every numerical value has a unit
  3. Code-ready — the receiving agent can directly use the values in NeqSim API calls
  4. Traceable — include the source agent and any assumptions made

What it can do on your machine

Read from SKILL.md and the folder at commit 69c5882. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json, java and python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Neqsim Agent Handoff loads about 2.2k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 457 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from equinor/neqsim at commit 69c5882, republished under its Apache-2.0 licence (© equinor). 457 words, ~2,211 tokens.

Download SKILL.mdSave it as .claude/skills/neqsim-agent-handoff/SKILL.md (or your agent's skills folder).
name
neqsim-agent-handoff
description
Agent-to-agent communication schema for NeqSim. USE WHEN: composing multi-agent pipelines where one agent's output feeds another agent's input. Defines structured result formats for fluid definitions, simulation results, and design outputs that agents can pass to each other.
last_verified
2026-07-04

NeqSim Agent Handoff Schema

When agents need to pass results to other agents (e.g., @process-model output feeding @mechanical-design), use these structured formats to ensure no information is lost.

Handoff Principles

  1. Explicit over implicit — include all parameters, don't assume the receiving agent can infer
  2. Units always included — every numerical value has a unit
  3. Code-ready — the receiving agent can directly use the values in NeqSim API calls
  4. Traceable — include the source agent and any assumptions made

Schema 1: Fluid Definition Handoff

Pass from @thermo-fluid to any other agent:

json
{
  "handoff_type": "fluid_definition",
  "source_agent": "thermo.fluid",
  "eos_class": "SystemSrkEos",
  "mixing_rule": "classic",
  "temperature_K": 298.15,
  "pressure_bara": 60.0,
  "components": [
    {"name": "methane", "mole_fraction": 0.85},
    {"name": "ethane", "mole_fraction": 0.10},
    {"name": "propane", "mole_fraction": 0.05}
  ],
  "multi_phase_check": false,
  "characterization": null,
  "java_code": "SystemInterface fluid = new SystemSrkEos(298.15, 60.0);\nfluid.addComponent(\"methane\", 0.85);\nfluid.addComponent(\"ethane\", 0.10);\nfluid.addComponent(\"propane\", 0.05);\nfluid.setMixingRule(\"classic\");",
  "assumptions": ["Lean gas — no water, no C4+ components"]
}

Schema 2: Process Simulation Handoff

Pass from @process-model to @mechanical-design, @safety-depressuring, etc.:

json
{
  "handoff_type": "process_simulation",
  "source_agent": "process.model",
  "fluid_definition": { "...": "Schema 1 above" },
  "equipment": [
    {
      "name": "HP Separator",
      "type": "Separator",
      "inlet_temperature_C": 30.0,
      "inlet_pressure_bara": 60.0,
      "outlet_gas_temperature_C": 30.0,
      "outlet_gas_pressure_bara": 60.0,
      "outlet_liquid_temperature_C": 30.0,
      "outlet_liquid_pressure_bara": 60.0,
      "gas_flow_rate_kg_hr": 42000.0,
      "liquid_flow_rate_kg_hr": 8000.0,
      "gas_density_kg_m3": 45.2,
      "liquid_density_kg_m3": 520.0
    }
  ],
  "mass_balance_error_pct": 0.001,
  "energy_balance_error_pct": 0.01,
  "assumptions": ["Adiabatic separator", "No liquid carryover"]
}

Schema 3: Mechanical Design Handoff

Pass from @mechanical-design to @solve-task for reporting:

json
{
  "handoff_type": "mechanical_design",
  "source_agent": "mechanical.design",
  "equipment_name": "HP Separator",
  "design_pressure_barg": 72.0,
  "design_temperature_C": 100.0,
  "material_grade": "SA-516-70",
  "wall_thickness_mm": 28.5,
  "corrosion_allowance_mm": 3.0,
  "weight_empty_kg": 15200.0,
  "design_standard": "ASME VIII Div.1",
  "company_tr": "Equinor TR2000",
  "cost_estimate_usd": 450000.0,
  "assumptions": ["Joint efficiency 0.85", "No external loads"]
}

Schema 4: Flow Assurance Handoff

Pass from @flow-assurance to @solve-task or @process-model:

json
{
  "handoff_type": "flow_assurance",
  "source_agent": "flow.assurance",
  "hydrate_temperature_C": 18.5,
  "operating_temperature_C": 25.0,
  "subcooling_margin_C": 6.5,
  "hydrate_risk": "LOW",
  "wax_appearance_temperature_C": -5.0,
  "pipeline_pressure_drop_bar": 12.3,
  "arrival_temperature_C": 8.5,
  "assumptions": ["No MEG injection", "Seawater at 4 C"]
}

Schema 5: Safety Analysis Handoff

Pass from @safety-depressuring to reporting:

json
{
  "handoff_type": "safety_analysis",
  "source_agent": "safety.depressuring",
  "scenario": "Fire case blowdown",
  "initial_pressure_bara": 85.0,
  "final_pressure_bara": 6.9,
  "blowdown_time_minutes": 15.0,
  "minimum_temperature_C": -45.0,
  "mdmt_C": -46.0,
  "mdmt_margin_C": 1.0,
  "psv_required_area_cm2": 12.5,
  "assumptions": ["API 521 fire case", "Orifice Cd = 0.85"]
}

How to Use Handoff Schemas

Sending Agent (produces the handoff)

At the end of your work, format results into the appropriate schema:

python
# In a notebook or agent output
handoff = {
    "handoff_type": "process_simulation",
    "source_agent": "process.model",
    "equipment": [...],
    # ... fill all fields
}
# Include in the response to the orchestrating agent
Receiving Agent (consumes the handoff)

When you receive a handoff from another agent:

  1. Validate the handoff — check all required fields are present
  2. Use the values directly — temperatures, pressures, flows are ready to use
  3. Preserve assumptions — carry forward assumptions from the source agent
  4. Add your own assumptions — append to the assumptions list
Router Agent (orchestrates handoffs)

The @neqsim.help router agent manages handoffs when composing multi-agent pipelines:

  1. Runs Agent A, captures handoff output
  2. Passes handoff as context to Agent B
  3. Agent B uses handoff values as inputs
  4. Final results aggregated for user
Show full SKILL.md (201 more words)Show less

Cross-Agent Consistency Checks

When receiving a handoff, verify consistency:

CheckRule
Temperature unitsMust be in C or K (never mixed)
Pressure unitsMust be bara (never barg or psia without conversion)
Flow rate unitsMust include unit string
Mass balanceSum of outlet flows = inlet flow (within 0.1%)
Phase consistencyIf source says 2 phases, receiving agent should see 2 phases

If a consistency check fails, alert the user before proceeding.

Schema 6: Lifecycle State Handoff

Use when passing a complete simulation state between agents — e.g., from a process simulation agent to a mechanical design agent, or between task iterations.

json
{
  "schema": "neqsim-lifecycle-state",
  "version": "1.0",
  "handoff": {
    "source_agent": "process-model",
    "target_agent": "mechanical-design",
    "state_type": "ProcessSystemState | ProcessModelState",
    "state_name": "Gas Processing Base Case",
    "state_version": "1.0.0",
    "state_json": "<serialized JSON from ProcessSystemState.toJson()>",
    "compressed_bytes_base64": "<optional: base64-encoded compressed bytes for large states>",
    "validation": {
      "is_valid": true,
      "checksum": "abc123..."
    },
    "context": {
      "description": "HP/LP separation train for 50 MMSCFD wet gas",
      "key_results": {
        "gas_export_rate_MSm3_day": 1.2,
        "liquid_rate_m3_hr": 45.0
      }
    },
    "assumptions": [
      "SRK EOS with classic mixing rule",
      "Steady-state operation at plateau rate"
    ]
  }
}
Lifecycle State Fields
FieldTypeRequiredDescription
state_typestringYesProcessSystemState (single area) or ProcessModelState (multi-area)
state_namestringYesHuman-readable name for the state
state_versionstringYesSemver version string
state_jsonstringYesSerialized JSON from state.toJson()
compressed_bytes_base64stringNoBase64-encoded compressed bytes for large states
validation.is_validbooleanYesResult of state.validate().isValid()
validation.checksumstringNoIntegrity checksum from the state object
context.descriptionstringYesWhat the process does
context.key_resultsobjectNoSummary of important results
Creating a Lifecycle State Handoff
java
// Source agent creates the state
ProcessSystemState state = ProcessSystemState.fromProcessSystem(process);
state.setName("Gas Processing Base Case");
state.setVersion("1.0.0");
String stateJson = state.toJson();
boolean isValid = state.validate().isValid();

// For large states, use compressed bytes
byte[] compressed = state.toCompressedBytes();
String base64 = java.util.Base64.getEncoder().encodeToString(compressed);
Consuming a Lifecycle State Handoff
java
// Target agent loads the state
ProcessSystemState loaded = ProcessSystemState.fromJson(handoff.state_json);
ProcessSystemState.ValidationResult result = loaded.validate();
assert result.isValid();

// For multi-area states
ProcessModelState modelState = ProcessModelState.fromJson(handoff.state_json);
Version Comparison Across Handoffs

When multiple agents produce states at different design iterations, compare them:

java
ProcessModelState v1 = ProcessModelState.fromJson(handoff1.state_json);
ProcessModelState v2 = ProcessModelState.fromJson(handoff2.state_json);
ProcessModelState.ModelDiff diff = ProcessModelState.compare(v1, v2);
// diff.getModifiedParameters(), diff.getAddedEquipment(), diff.getRemovedEquipment()

© equinor, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/neqsim-agent-handoff of equinor/neqsim.

Open the folder on GitHubat commit 69c5882

Compare with similar skills

Neqsim Agent Handoff next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Neqsim Agent Handoff compared with similar skills
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Neqsim Agent Handoff this skillequinor/neqsim156—~2.2kAutomated safety check: PassApache-2.0
Orca CLIstablyai/orca89k2 repos~593Automated safety check: PassMIT
Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0
Paseo Committeegetpaseo/paseo20k1 repos~496Automated safety check: PassCustom licence
Mission Control Agent APIbuilderz-labs/mission-control6.3k—~2.1kAutomated safety check: PassMIT

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Categories

Questions about Neqsim Agent Handoff

What does Neqsim Agent Handoff do?

Agent-to-agent communication schema for NeqSim. An agent skill from equinor/neqsim. Neqsim Agent Handoff is an agent skill from equinor/neqsim. Agent-to-agent communication schema for NeqSim.

When should I use Neqsim Agent Handoff?

Neqsim Agent Handoff fits situations like: : composing multi-agent pipelines where one agents output feeds another agents input; tasks that involve Multi-agent orchestration.

How do I install Neqsim Agent Handoff in Claude Code?

Run `npx skills add equinor/neqsim --skill neqsim-agent-handoff -a claude-code`. Or copy the skill folder (.github/skills/neqsim-agent-handoff in equinor/neqsim) into .claude/skills/neqsim-agent-handoff in your project. Claude Code loads it when a task matches its description.

How do I install Neqsim Agent Handoff in Codex?

Run `npx skills add equinor/neqsim --skill neqsim-agent-handoff -a codex`. Or copy the skill folder (.github/skills/neqsim-agent-handoff in equinor/neqsim) into .agents/skills/neqsim-agent-handoff in your project. Codex loads it when a task matches its description.

Can I use Neqsim Agent Handoff in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add equinor/neqsim --skill neqsim-agent-handoff -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/neqsim-agent-handoff, .gemini/skills/neqsim-agent-handoff, .github/skills/neqsim-agent-handoff and .opencode/skills/neqsim-agent-handoff in your project.

What does Neqsim Agent Handoff need to run?

SKILL.md names no scripts, command-line tools or credentials: Neqsim Agent Handoff is instructions for the agent only. Our summary lists: Python 3.

Does Neqsim Agent Handoff access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Neqsim Agent Handoff safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Neqsim Agent Handoff use?

Neqsim Agent Handoff is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Neqsim Agent Handoff use?

About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Neqsim Agent Handoff?

Skills that share tags, products or a category with Neqsim Agent Handoff: Orca CLI (stablyai/orca, 89k stars), Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars), O2 Review Loop (openobserve/openobserve, 22k stars) and Paseo Committee (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Neqsim Agent Handoff?

equinor (a GitHub organization) maintains it in equinor/neqsim, which has 156 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 10, 2026.

Source: equinor/neqsim on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.