[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QF7SJNNPP2P52PAT30M2TN":3,"skill-same:01M6QF7SJNNPP2P52PAT30M2TN":84},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6QF7SJNNPP2P52PAT30M2TN","molecular-dynamics",null,"Runs and analyzes molecular dynamics simulations using OpenMM and MDAnalysis. Covers system preparation with PDBFixer and OpenFF\u002FGAFF2, force field choice (AMBER14, CHARMM36m, ff19SB), energy minimization, NVT\u002FNPT equilibration, production MD, and trajectory analysis (RMSD, RMSF, protein-ligand contacts). Use when simulating a protein or protein-ligand system on GPU, assessing how a mutation affects protein dynamics, characterizing ligand binding mode, quantifying per-residue flexibility, or modeling membrane proteins and disordered proteins. Not for GROMACS or NAMD workflows, which the skill lists only as alternatives.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-molecular-dynamics\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"dynamics","molecular dynamics","molecular","computational","energy","openmm","mode","which","contacts","membrane","drug-discovery","workflows","computational chemistry","proteins","mutation","only","choice","how","simulation","analysis","2026-11-10 10:12:30",false,"---\nname: molecular-dynamics\ndescription: Runs and analyzes molecular dynamics simulations using OpenMM and MDAnalysis. Covers system preparation with PDBFixer and OpenFF\u002FGAFF2, force field choice (AMBER14, CHARMM36m, ff19SB), energy minimization, NVT\u002FNPT equilibration, production MD, and trajectory analysis (RMSD, RMSF, protein-ligand contacts). Use when simulating a protein or protein-ligand system on GPU, assessing how a mutation affects protein dynamics, characterizing ligand binding mode, quantifying per-residue flexibility, or modeling membrane proteins and disordered proteins. Not for GROMACS or NAMD workflows, which the skill lists only as alternatives.\nlicense: MIT\nmetadata:\n  version: '1.1'\n  category: chemistry-and-drug-discovery\n  maintainer: Kalaris Labs\n  contributor: Kuan-lin Huang\n  tags: molecular dynamics, OpenMM, MDAnalysis, simulation, computational chemistry\n---\n\n# Molecular Dynamics\n\n## Overview\n\nMolecular dynamics (MD) simulation computationally models the time evolution of molecular systems by integrating Newton's equations of motion. This skill covers two complementary tools:\n\n- **OpenMM** (https:\u002F\u002Fopenmm.org\u002F): High-performance MD simulation engine with GPU support, Python API, and flexible force field support\n- **MDAnalysis** (https:\u002F\u002Fmdanalysis.org\u002F): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages\n\n**Installation:**\n```bash\nconda install -c conda-forge openmm mdanalysis nglview\n# or\nuv pip install openmm mdanalysis\n```\n\n## When to Use This Skill\n\nUse molecular dynamics when:\n\n- **Protein stability analysis**: How does a mutation affect protein dynamics?\n- **Drug binding simulations**: Characterize binding mode and residence time of a ligand\n- **Conformational sampling**: Explore protein flexibility and conformational changes\n- **Protein-protein interaction**: Model interface dynamics and binding energetics\n- **RMSD\u002FRMSF analysis**: Quantify structural fluctuations from a reference structure\n- **Free energy estimation**: Compute binding free energy or conformational free energy\n- **Membrane simulations**: Model proteins in lipid bilayers\n- **Intrinsically disordered proteins**: Study IDR conformational ensembles\n\n## Core Workflow: OpenMM Simulation\n\n### 1. System Preparation\n\n```python\nfrom openmm.app import *\nfrom openmm import *\nfrom openmm.unit import *\nimport sys\n\ndef prepare_system_from_pdb(pdb_file, forcefield_name=\"amber14-all.xml\",\n                              water_model=\"amber14\u002Ftip3pfb.xml\"):\n    \"\"\"\n    Prepare an OpenMM system from a PDB file.\n\n    Args:\n        pdb_file: Path to cleaned PDB file (use PDBFixer for raw PDB files)\n        forcefield_name: Force field XML file\n        water_model: Water model XML file\n\n    Returns:\n        pdb, forcefield, system, topology\n    \"\"\"\n    # Load PDB\n    pdb = PDBFile(pdb_file)\n\n    # Load force field\n    forcefield = ForceField(forcefield_name, water_model)\n\n    # Add hydrogens and solvate\n    modeller = Modeller(pdb.topology, pdb.positions)\n    modeller.addHydrogens(forcefield)\n\n    # Add solvent box (10 Å padding, 150 mM NaCl)\n    modeller.addSolvent(\n        forcefield,\n        model='tip3p',\n        padding=10*angstroms,\n        ionicStrength=0.15*molar\n    )\n\n    print(f\"System: {modeller.topology.getNumAtoms()} atoms, \"\n          f\"{modeller.topology.getNumResidues()} residues\")\n\n    # Create system\n    system = forcefield.createSystem(\n        modeller.topology,\n        nonbondedMethod=PME,         # Particle Mesh Ewald for long-range electrostatics\n        nonbondedCutoff=1.0*nanometer,\n        constraints=HBonds,           # Constrain hydrogen bonds (allows 2 fs timestep)\n        rigidWater=True,\n        ewaldErrorTolerance=0.0005\n    )\n\n    return modeller, system\n```\n\n### 2. Energy Minimization\n\n```python\nfrom openmm.app import *\nfrom openmm import *\nfrom openmm.unit import *\n\ndef minimize_energy(modeller, system, output_pdb=\"minimized.pdb\",\n                     max_iterations=1000, tolerance=10.0):\n    \"\"\"\n    Energy minimize the system to remove steric clashes.\n\n    Args:\n        modeller: Modeller object with topology and positions\n        system: OpenMM System\n        output_pdb: Path to save minimized structure\n        max_iterations: Maximum minimization steps\n        tolerance: Convergence criterion in kJ\u002Fmol\u002Fnm\n\n    Returns:\n        simulation object with minimized positions\n    \"\"\"\n    # Set up integrator (doesn't matter for minimization)\n    integrator = LangevinMiddleIntegrator(300*kelvin, 1\u002Fpicosecond, 0.004*picoseconds)\n\n    # Create simulation\n    # Use GPU if available (CUDA or OpenCL), fall back to CPU\n    try:\n        platform = Platform.getPlatformByName('CUDA')\n        properties = {'DeviceIndex': '0', 'Precision': 'mixed'}\n    except Exception:\n        try:\n            platform = Platform.getPlatformByName('OpenCL')\n            properties = {}\n        except Exception:\n            platform = Platform.getPlatformByName('CPU')\n            properties = {}\n\n    simulation = Simulation(\n        modeller.topology, system, integrator,\n        platform, properties\n    )\n    simulation.context.setPositions(modeller.positions)\n\n    # Check initial energy\n    state = simulation.context.getState(getEnergy=True)\n    print(f\"Initial energy: {state.getPotentialEnergy()}\")\n\n    # Minimize\n    simulation.minimizeEnergy(\n        tolerance=tolerance*kilojoules_per_mole\u002Fnanometer,\n        maxIterations=max_iterations\n    )\n\n    state = simulation.context.getState(getEnergy=True, getPositions=True)\n    print(f\"Minimized energy: {state.getPotentialEnergy()}\")\n\n    # Save minimized structure\n    with open(output_pdb, 'w') as f:\n        PDBFile.writeFile(simulation.topology, state.getPositions(), f)\n\n    return simulation\n```\n\n### 3. NVT Equilibration\n\n```python\nfrom openmm.app import *\nfrom openmm import *\nfrom openmm.unit import *\n\ndef run_nvt_equilibration(simulation, n_steps=50000, temperature=300,\n                            report_interval=1000, output_prefix=\"nvt\"):\n    \"\"\"\n    NVT equilibration: constant N, V, T.\n    Equilibrate velocities to target temperature.\n\n    Args:\n        simulation: OpenMM Simulation (after minimization)\n        n_steps: Number of MD steps (50000 × 2fs = 100 ps)\n        temperature: Temperature in Kelvin\n        report_interval: Steps between data reports\n        output_prefix: File prefix for trajectory and log\n    \"\"\"\n    # Add position restraints for backbone during NVT\n    # (Optional: restraint heavy atoms)\n\n    # Set temperature\n    simulation.context.setVelocitiesToTemperature(temperature*kelvin)\n\n    # Add reporters\n    simulation.reporters = []\n\n    # Log file\n    simulation.reporters.append(\n        StateDataReporter(\n            f\"{output_prefix}_log.txt\",\n            report_interval,\n            step=True,\n            potentialEnergy=True,\n            kineticEnergy=True,\n            temperature=True,\n            volume=True,\n            speed=True\n        )\n    )\n\n    # DCD trajectory (compact binary format)\n    simulation.reporters.append(\n        DCDReporter(f\"{output_prefix}_traj.dcd\", report_interval)\n    )\n\n    print(f\"Running NVT equilibration: {n_steps} steps ({n_steps*2\u002F1000:.1f} ps)\")\n    simulation.step(n_steps)\n    print(\"NVT equilibration complete\")\n\n    return simulation\n```\n\n### 4. NPT Equilibration and Production\n\n```python\ndef run_npt_production(simulation, n_steps=500000, temperature=300, pressure=1.0,\n                        report_interval=5000, output_prefix=\"npt\"):\n    \"\"\"\n    NPT production run: constant N, P, T.\n\n    Args:\n        n_steps: Production steps (500000 × 2fs = 1 ns)\n        temperature: Temperature in Kelvin\n        pressure: Pressure in bar\n        report_interval: Steps between reports\n    \"\"\"\n    # Add Monte Carlo barostat for pressure control\n    system = simulation.context.getSystem()\n    system.addForce(MonteCarloBarostat(pressure*bar, temperature*kelvin, 25))\n    simulation.context.reinitialize(preserveState=True)\n\n    # Update reporters\n    simulation.reporters = []\n    simulation.reporters.append(\n        StateDataReporter(\n            f\"{output_prefix}_log.txt\",\n            report_interval,\n            step=True,\n            potentialEnergy=True,\n            temperature=True,\n            density=True,\n            speed=True\n        )\n    )\n    simulation.reporters.append(\n        DCDReporter(f\"{output_prefix}_traj.dcd\", report_interval)\n    )\n\n    # Save checkpoints\n    simulation.reporters.append(\n        CheckpointReporter(f\"{output_prefix}_checkpoint.chk\", 50000)\n    )\n\n    print(f\"Running NPT production: {n_steps} steps ({n_steps*2\u002F1000000:.2f} ns)\")\n    simulation.step(n_steps)\n    print(\"Production MD complete\")\n    return simulation\n```\n\n## Trajectory Analysis with MDAnalysis\n\n### 1. Load Trajectory\n\n```python\nimport MDAnalysis as mda\nfrom MDAnalysis.analysis import rms, align, contacts\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef load_trajectory(topology_file, trajectory_file):\n    \"\"\"\n    Load an MD trajectory with MDAnalysis.\n\n    Args:\n        topology_file: PDB, PSF, or other topology file\n        trajectory_file: DCD, XTC, TRR, or other trajectory\n    \"\"\"\n    u = mda.Universe(topology_file, trajectory_file)\n    print(f\"Universe: {u.atoms.n_atoms} atoms, {u.trajectory.n_frames} frames\")\n    print(f\"Time range: 0 to {u.trajectory.totaltime:.0f} ps\")\n    return u\n```\n\n### 2. RMSD Analysis\n\n```python\ndef compute_rmsd(u, selection=\"backbone\", reference_frame=0):\n    \"\"\"\n    Compute RMSD of selected atoms relative to reference frame.\n\n    Args:\n        u: MDAnalysis Universe\n        selection: Atom selection string (MDAnalysis syntax)\n        reference_frame: Frame index for reference structure\n\n    Returns:\n        numpy array of (time, rmsd) values\n    \"\"\"\n    # Align trajectory to minimize RMSD\n    aligner = align.AlignTraj(u, u, select=selection, in_memory=True)\n    aligner.run()\n\n    # Compute RMSD\n    R = rms.RMSD(u, select=selection, ref_frame=reference_frame)\n    R.run()\n\n    rmsd_data = R.results.rmsd  # columns: frame, time, RMSD\n    return rmsd_data\n\ndef plot_rmsd(rmsd_data, title=\"RMSD over time\", output_file=\"rmsd.png\"):\n    \"\"\"Plot RMSD over simulation time.\"\"\"\n    fig, ax = plt.subplots(figsize=(10, 4))\n    ax.plot(rmsd_data[:, 1] \u002F 1000, rmsd_data[:, 2], 'b-', linewidth=0.5)\n    ax.set_xlabel(\"Time (ns)\")\n    ax.set_ylabel(\"RMSD (Å)\")\n    ax.set_title(title)\n    ax.axhline(rmsd_data[:, 2].mean(), color='r', linestyle='--',\n               label=f'Mean: {rmsd_data[:, 2].mean():.2f} Å')\n    ax.legend()\n    plt.tight_layout()\n    plt.savefig(output_file, dpi=150)\n    return fig\n```\n\n### 3. RMSF Analysis (Per-Residue Flexibility)\n\n```python\ndef compute_rmsf(u, selection=\"backbone\", start_frame=0):\n    \"\"\"\n    Compute per-residue RMSF (flexibility).\n\n    Returns:\n        resids, rmsf_values arrays\n    \"\"\"\n    # Select atoms\n    atoms = u.select_atoms(selection)\n\n    # Compute RMSF\n    R = rms.RMSF(atoms)\n    R.run(start=start_frame)\n\n    # Average by residue\n    resids = []\n    rmsf_per_res = []\n    for res in u.select_atoms(selection).residues:\n        res_atoms = res.atoms.intersection(atoms)\n        if len(res_atoms) > 0:\n            resids.append(res.resid)\n            rmsf_per_res.append(R.results.rmsf[res_atoms.indices].mean())\n\n    return np.array(resids), np.array(rmsf_per_res)\n```\n\n### 4. Protein-Ligand Contacts\n\n```python\ndef analyze_contacts(u, protein_sel=\"protein\", ligand_sel=\"resname LIG\",\n                      radius=4.5, start_frame=0):\n    \"\"\"\n    Track protein-ligand contacts over trajectory.\n\n    Args:\n        radius: Contact distance cutoff in Angstroms\n    \"\"\"\n    protein = u.select_atoms(protein_sel)\n    ligand = u.select_atoms(ligand_sel)\n\n    contact_frames = []\n    for ts in u.trajectory[start_frame:]:\n        # Find protein atoms within radius of ligand\n        distances = contacts.contact_matrix(\n            protein.positions, ligand.positions, radius\n        )\n        contact_residues = set()\n        for i in range(distances.shape[0]):\n            if distances[i].any():\n                contact_residues.add(protein.atoms[i].resid)\n        contact_frames.append(contact_residues)\n\n    return contact_frames\n```\n\n## Force Field Selection Guide\n\n| System | Recommended Force Field | Water Model |\n|--------|------------------------|-------------|\n| Standard proteins | AMBER14 (`amber14-all.xml`) | TIP3P-FB |\n| Proteins + small molecules | AMBER14 + GAFF2 | TIP3P-FB |\n| Membrane proteins | CHARMM36m | TIP3P |\n| Nucleic acids | AMBER99-bsc1 or AMBER14 | TIP3P |\n| Disordered proteins | ff19SB or CHARMM36m | TIP3P |\n\n## System Preparation Tools\n\n### PDBFixer (for raw PDB files)\n\n```python\nfrom pdbfixer import PDBFixer\nfrom openmm.app import PDBFile\n\ndef fix_pdb(input_pdb, output_pdb, ph=7.0):\n    \"\"\"Fix common PDB issues: missing residues, atoms, add H, standardize.\"\"\"\n    fixer = PDBFixer(filename=input_pdb)\n    fixer.findMissingResidues()\n    fixer.findNonstandardResidues()\n    fixer.replaceNonstandardResidues()\n    fixer.removeHeterogens(True)    # Remove water\u002Fligands\n    fixer.findMissingAtoms()\n    fixer.addMissingAtoms()\n    fixer.addMissingHydrogens(ph)\n\n    with open(output_pdb, 'w') as f:\n        PDBFile.writeFile(fixer.topology, fixer.positions, f)\n\n    return output_pdb\n```\n\n### GAFF2 for Small Molecules (via OpenFF Toolkit)\n\n```python\n# For ligand parameterization, use OpenFF toolkit or ACPYPE\n# uv pip install openff-toolkit\nfrom openff.toolkit import Molecule, ForceField as OFFForceField\nfrom openff.interchange import Interchange\n\ndef parameterize_ligand(smiles, ff_name=\"openff-2.0.0.offxml\"):\n    \"\"\"Generate GAFF2\u002FOpenFF parameters for a small molecule.\"\"\"\n    mol = Molecule.from_smiles(smiles)\n    mol.generate_conformers(n_conformers=1)\n\n    off_ff = OFFForceField(ff_name)\n    interchange = off_ff.create_interchange(mol.to_topology())\n    return interchange\n```\n\n## Best Practices\n\n- **Always minimize before MD**: Raw PDB structures have steric clashes\n- **Equilibrate before production**: NVT (50–100 ps) → NPT (100–500 ps) → Production\n- **Use GPU**: Simulations are 10–100× faster on GPU (CUDA\u002FOpenCL)\n- **2 fs timestep with HBonds constraints**: Standard; use 4 fs with HMR (hydrogen mass repartitioning)\n- **Analyze only equilibrated trajectory**: Discard first 20–50% as equilibration\n- **Save checkpoints**: MD runs can fail; checkpoints allow restart\n- **Periodic boundary conditions**: Required for solvated systems\n- **PME for electrostatics**: More accurate than cutoff methods for charged systems\n\n## Additional Resources\n\n- **OpenMM documentation**: https:\u002F\u002Fopenmm.org\u002Fdocumentation.html\n- **MDAnalysis user guide**: https:\u002F\u002Fdocs.mdanalysis.org\u002F\n- **GROMACS** (alternative MD engine): https:\u002F\u002Fmanual.gromacs.org\u002F\n- **NAMD** (alternative): https:\u002F\u002Fwww.ks.uiuc.edu\u002FResearch\u002Fnamd\u002F\n- **CHARMM-GUI** (web-based system builder): https:\u002F\u002Fcharmm-gui.org\u002F\n- **AmberTools** (free Amber tools): https:\u002F\u002Fambermd.org\u002FAmberTools.php\n- **OpenMM paper**: Eastman P et al. (2017) PLOS Computational Biology. PMID: 28278240\n- **MDAnalysis paper**: Michaud-Agrawal N et al. (2011) J Computational Chemistry. PMID: 21500218\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm toolkit versions and input formats (SMILES, SDF, PDB, CIF), and sanitize molecules before computing.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Process a few molecules or one target first and inspect the outputs.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check for invalid structures, unit consistency, and applicability domain of predictive models.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Molecules fail to parse or sanitize | Report and skip them with reasons; never silently drop or alter structures. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Computational predictions are hypotheses, not experimental results; label them as such.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `rowan`: Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API.\n","# Molecular Dynamics\n\n## Overview\n\nMolecular dynamics (MD) simulation computationally models the time evolution of molecular systems by integrating Newton's equations of motion. This skill covers two complementary tools:\n\n- **OpenMM** (https:\u002F\u002Fopenmm.org\u002F): High-performance MD simulation engine with GPU support, Python API, and flexible force field support\n- **MDAnalysis** (https:\u002F\u002Fmdanalysis.org\u002F): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages\n\n**Installation:**\n```bash\nconda install -c conda-forge openmm mdanalysis nglview\n# or\nuv pip install openmm mdanalysis\n```\n\n## When to Use This Skill\n\nUse molecular dynamics when:\n\n- **Protein stability analysis**: How does a mutation affect protein dynamics?\n- **Drug binding simulations**: Characterize binding mode and residence time of a ligand\n- **Conformational sampling**: Explore protein flexibility and conformational changes\n- **Protein-protein interaction**: Model interface dynamics and binding energetics\n- **RMSD\u002FRMSF analysis**: Quantify structural fluctuations from a reference structure\n- **Free energy estimation**: Compute binding free energy or conformational free energy\n- **Membrane simulations**: Model proteins in lipid bilayers\n- **Intrinsically disordered proteins**: Study IDR conformational ensembles\n\n## Core Workflow: OpenMM Simulation\n\n### 1. System Preparation\n\n```python\nfrom openmm.app import *\nfrom openmm import *\nfrom openmm.unit import *\nimport sys\n\ndef prepare_system_from_pdb(pdb_file, forcefield_name=\"amber14-all.xml\",\n                              water_model=\"amber14\u002Ftip3pfb.xml\"):\n    \"\"\"\n    Prepare an OpenMM system from a PDB file.\n\n    Args:\n        pdb_file: Path to cleaned PDB file (use PDBFixer for raw PDB files)\n        forcefield_name: Force field XML file\n        water_model: Water model XML file\n\n    Returns:\n        pdb, forcefield, system, topology\n    \"\"\"\n    # Load PDB\n    pdb = PDBFile(pdb_file)\n\n    # Load force field\n    forcefield = ForceField(forcefield_name, water_model)\n\n    # Add hydrogens and solvate\n    modeller = Modeller(pdb.topology, pdb.positions)\n    modeller.addHydrogens(forcefield)\n\n    # Add solvent box (10 Å padding, 150 mM NaCl)\n    modeller.addSolvent(\n        forcefield,\n        model='tip3p',\n        padding=10*angstroms,\n        ionicStrength=0.15*molar\n    )\n\n    print(f\"System: {modeller.topology.getNumAtoms()} atoms, \"\n          f\"{modeller.topology.getNumResidues()} residues\")\n\n    # Create system\n    system = forcefield.createSystem(\n        modeller.topology,\n        nonbondedMethod=PME,         # Particle Mesh Ewald for long-range electrostatics\n        nonbondedCutoff=1.0*nanometer,\n        constraints=HBonds,           # Constrain hydrogen bonds (allows 2 fs timestep)\n        rigidWater=True,\n        ewaldErrorTolerance=0.0005\n    )\n\n    return modeller, system\n```\n\n### 2. Energy Minimization\n\n```python\nfrom openmm.app import *\nfrom openmm import *\nfrom openmm.unit import *\n\ndef minimize_energy(modeller, system, output_pdb=\"minimized.pdb\",\n                     max_iterations=1000, tolerance=10.0):\n    \"\"\"\n    Energy minimize the system to remove steric clashes.\n\n    Args:\n        modeller: Modeller object with topology and positions\n        system: OpenMM System\n        output_pdb: Path to save minimized structure\n        max_iterations: Maximum minimization steps\n        tolerance: Convergence criterion in kJ\u002Fmol\u002Fnm\n\n    Returns:\n        simulation object with minimized positions\n    \"\"\"\n    # Set up integrator (doesn't matter for minimization)\n    integrator = LangevinMiddleIntegrator(300*kelvin, 1\u002Fpicosecond, 0.004*picoseconds)\n\n    # Create simulation\n    # Use GPU if available (CUDA or OpenCL), fall back to CPU\n    try:\n        platform = Platform.getPlatformByName('CUDA')\n        properties = {'DeviceIndex': '0', 'Precision': 'mixed'}\n    except Exception:\n        try:\n            platform = Platform.getPlatformByName('OpenCL')\n            properties = {}\n        except Exception:\n            platform = Platform.getPlatformByName('CPU')\n            properties = {}\n\n    simulation = Simulation(\n        modeller.topology, system, integrator,\n        platform, properties\n    )\n    simulation.context.setPositions(modeller.positions)\n\n    # Check initial energy\n    state = simulation.context.getState(getEnergy=True)\n    print(f\"Initial energy: {state.getPotentialEnergy()}\")\n\n    # Minimize\n    simulation.minimizeEnergy(\n        tolerance=tolerance*kilojoules_per_mole\u002Fnanometer,\n        maxIterations=max_iterations\n    )\n\n    state = simulation.context.getState(getEnergy=True, getPositions=True)\n    print(f\"Minimized energy: {state.getPotentialEnergy()}\")\n\n    # Save minimized structure\n    with open(output_pdb, 'w') as f:\n        PDBFile.writeFile(simulation.topology, state.getPositions(), f)\n\n    return simulation\n```\n\n### 3. NVT Equilibration\n\n```python\nfrom openmm.app import *\nfrom openmm import *\nfrom openmm.unit import *\n\ndef run_nvt_equilibration(simulation, n_steps=50000, temperature=300,\n                            report_interval=1000, output_prefix=\"nvt\"):\n    \"\"\"\n    NVT equilibration: constant N, V, T.\n    Equilibrate velocities to target temperature.\n\n    Args:\n        simulation: OpenMM Simulation (after minimization)\n        n_steps: Number of MD steps (50000 × 2fs = 100 ps)\n        temperature: Temperature in Kelvin\n        report_interval: Steps between data reports\n        output_prefix: File prefix for trajectory and log\n    \"\"\"\n    # Add position restraints for backbone during NVT\n    # (Optional: restraint heavy atoms)\n\n    # Set temperature\n    simulation.context.setVelocitiesToTemperature(temperature*kelvin)\n\n    # Add reporters\n    simulation.reporters = []\n\n    # Log file\n    simulation.reporters.append(\n        StateDataReporter(\n            f\"{output_prefix}_log.txt\",\n            report_interval,\n            step=True,\n            potentialEnergy=True,\n            kineticEnergy=True,\n            temperature=True,\n            volume=True,\n            speed=True\n        )\n    )\n\n    # DCD trajectory (compact binary format)\n    simulation.reporters.append(\n        DCDReporter(f\"{output_prefix}_traj.dcd\", report_interval)\n    )\n\n    print(f\"Running NVT equilibration: {n_steps} steps ({n_steps*2\u002F1000:.1f} ps)\")\n    simulation.step(n_steps)\n    print(\"NVT equilibration complete\")\n\n    return simulation\n```\n\n### 4. NPT Equilibration and Production\n\n```python\ndef run_npt_production(simulation, n_steps=500000, temperature=300, pressure=1.0,\n                        report_interval=5000, output_prefix=\"npt\"):\n    \"\"\"\n    NPT production run: constant N, P, T.\n\n    Args:\n        n_steps: Production steps (500000 × 2fs = 1 ns)\n        temperature: Temperature in Kelvin\n        pressure: Pressure in bar\n        report_interval: Steps between reports\n    \"\"\"\n    # Add Monte Carlo barostat for pressure control\n    system = simulation.context.getSystem()\n    system.addForce(MonteCarloBarostat(pressure*bar, temperature*kelvin, 25))\n    simulation.context.reinitialize(preserveState=True)\n\n    # Update reporters\n    simulation.reporters = []\n    simulation.reporters.append(\n        StateDataReporter(\n            f\"{output_prefix}_log.txt\",\n            report_interval,\n            step=True,\n            potentialEnergy=True,\n            temperature=True,\n            density=True,\n            speed=True\n        )\n    )\n    simulation.reporters.append(\n        DCDReporter(f\"{output_prefix}_traj.dcd\", report_interval)\n    )\n\n    # Save checkpoints\n    simulation.reporters.append(\n        CheckpointReporter(f\"{output_prefix}_checkpoint.chk\", 50000)\n    )\n\n    print(f\"Running NPT production: {n_steps} steps ({n_steps*2\u002F1000000:.2f} ns)\")\n    simulation.step(n_steps)\n    print(\"Production MD complete\")\n    return simulation\n```\n\n## Trajectory Analysis with MDAnalysis\n\n### 1. Load Trajectory\n\n```python\nimport MDAnalysis as mda\nfrom MDAnalysis.analysis import rms, align, contacts\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef load_trajectory(topology_file, trajectory_file):\n    \"\"\"\n    Load an MD trajectory with MDAnalysis.\n\n    Args:\n        topology_file: PDB, PSF, or other topology file\n        trajectory_file: DCD, XTC, TRR, or other trajectory\n    \"\"\"\n    u = mda.Universe(topology_file, trajectory_file)\n    print(f\"Universe: {u.atoms.n_atoms} atoms, {u.trajectory.n_frames} frames\")\n    print(f\"Time range: 0 to {u.trajectory.totaltime:.0f} ps\")\n    return u\n```\n\n### 2. RMSD Analysis\n\n```python\ndef compute_rmsd(u, selection=\"backbone\", reference_frame=0):\n    \"\"\"\n    Compute RMSD of selected atoms relative to reference frame.\n\n    Args:\n        u: MDAnalysis Universe\n        selection: Atom selection string (MDAnalysis syntax)\n        reference_frame: Frame index for reference structure\n\n    Returns:\n        numpy array of (time, rmsd) values\n    \"\"\"\n    # Align trajectory to minimize RMSD\n    aligner = align.AlignTraj(u, u, select=selection, in_memory=True)\n    aligner.run()\n\n    # Compute RMSD\n    R = rms.RMSD(u, select=selection, ref_frame=reference_frame)\n    R.run()\n\n    rmsd_data = R.results.rmsd  # columns: frame, time, RMSD\n    return rmsd_data\n\ndef plot_rmsd(rmsd_data, title=\"RMSD over time\", output_file=\"rmsd.png\"):\n    \"\"\"Plot RMSD over simulation time.\"\"\"\n    fig, ax = plt.subplots(figsize=(10, 4))\n    ax.plot(rmsd_data[:, 1] \u002F 1000, rmsd_data[:, 2], 'b-', linewidth=0.5)\n    ax.set_xlabel(\"Time (ns)\")\n    ax.set_ylabel(\"RMSD (Å)\")\n    ax.set_title(title)\n    ax.axhline(rmsd_data[:, 2].mean(), color='r', linestyle='--',\n               label=f'Mean: {rmsd_data[:, 2].mean():.2f} Å')\n    ax.legend()\n    plt.tight_layout()\n    plt.savefig(output_file, dpi=150)\n    return fig\n```\n\n### 3. RMSF Analysis (Per-Residue Flexibility)\n\n```python\ndef compute_rmsf(u, selection=\"backbone\", start_frame=0):\n    \"\"\"\n    Compute per-residue RMSF (flexibility).\n\n    Returns:\n        resids, rmsf_values arrays\n    \"\"\"\n    # Select atoms\n    atoms = u.select_atoms(selection)\n\n    # Compute RMSF\n    R = rms.RMSF(atoms)\n    R.run(start=start_frame)\n\n    # Average by residue\n    resids = []\n    rmsf_per_res = []\n    for res in u.select_atoms(selection).residues:\n        res_atoms = res.atoms.intersection(atoms)\n        if len(res_atoms) > 0:\n            resids.append(res.resid)\n            rmsf_per_res.append(R.results.rmsf[res_atoms.indices].mean())\n\n    return np.array(resids), np.array(rmsf_per_res)\n```\n\n### 4. Protein-Ligand Contacts\n\n```python\ndef analyze_contacts(u, protein_sel=\"protein\", ligand_sel=\"resname LIG\",\n                      radius=4.5, start_frame=0):\n    \"\"\"\n    Track protein-ligand contacts over trajectory.\n\n    Args:\n        radius: Contact distance cutoff in Angstroms\n    \"\"\"\n    protein = u.select_atoms(protein_sel)\n    ligand = u.select_atoms(ligand_sel)\n\n    contact_frames = []\n    for ts in u.trajectory[start_frame:]:\n        # Find protein atoms within radius of ligand\n        distances = contacts.contact_matrix(\n            protein.positions, ligand.positions, radius\n        )\n        contact_residues = set()\n        for i in range(distances.shape[0]):\n            if distances[i].any():\n                contact_residues.add(protein.atoms[i].resid)\n        contact_frames.append(contact_residues)\n\n    return contact_frames\n```\n\n## Force Field Selection Guide\n\n| System | Recommended Force Field | Water Model |\n|--------|------------------------|-------------|\n| Standard proteins | AMBER14 (`amber14-all.xml`) | TIP3P-FB |\n| Proteins + small molecules | AMBER14 + GAFF2 | TIP3P-FB |\n| Membrane proteins | CHARMM36m | TIP3P |\n| Nucleic acids | AMBER99-bsc1 or AMBER14 | TIP3P |\n| Disordered proteins | ff19SB or CHARMM36m | TIP3P |\n\n## System Preparation Tools\n\n### PDBFixer (for raw PDB files)\n\n```python\nfrom pdbfixer import PDBFixer\nfrom openmm.app import PDBFile\n\ndef fix_pdb(input_pdb, output_pdb, ph=7.0):\n    \"\"\"Fix common PDB issues: missing residues, atoms, add H, standardize.\"\"\"\n    fixer = PDBFixer(filename=input_pdb)\n    fixer.findMissingResidues()\n    fixer.findNonstandardResidues()\n    fixer.replaceNonstandardResidues()\n    fixer.removeHeterogens(True)    # Remove water\u002Fligands\n    fixer.findMissingAtoms()\n    fixer.addMissingAtoms()\n    fixer.addMissingHydrogens(ph)\n\n    with open(output_pdb, 'w') as f:\n        PDBFile.writeFile(fixer.topology, fixer.positions, f)\n\n    return output_pdb\n```\n\n### GAFF2 for Small Molecules (via OpenFF Toolkit)\n\n```python\n# For ligand parameterization, use OpenFF toolkit or ACPYPE\n# uv pip install openff-toolkit\nfrom openff.toolkit import Molecule, ForceField as OFFForceField\nfrom openff.interchange import Interchange\n\ndef parameterize_ligand(smiles, ff_name=\"openff-2.0.0.offxml\"):\n    \"\"\"Generate GAFF2\u002FOpenFF parameters for a small molecule.\"\"\"\n    mol = Molecule.from_smiles(smiles)\n    mol.generate_conformers(n_conformers=1)\n\n    off_ff = OFFForceField(ff_name)\n    interchange = off_ff.create_interchange(mol.to_topology())\n    return interchange\n```\n\n## Best Practices\n\n- **Always minimize before MD**: Raw PDB structures have steric clashes\n- **Equilibrate before production**: NVT (50–100 ps) → NPT (100–500 ps) → Production\n- **Use GPU**: Simulations are 10–100× faster on GPU (CUDA\u002FOpenCL)\n- **2 fs timestep with HBonds constraints**: Standard; use 4 fs with HMR (hydrogen mass repartitioning)\n- **Analyze only equilibrated trajectory**: Discard first 20–50% as equilibration\n- **Save checkpoints**: MD runs can fail; checkpoints allow restart\n- **Periodic boundary conditions**: Required for solvated systems\n- **PME for electrostatics**: More accurate than cutoff methods for charged systems\n\n## Additional Resources\n\n- **OpenMM documentation**: https:\u002F\u002Fopenmm.org\u002Fdocumentation.html\n- **MDAnalysis user guide**: https:\u002F\u002Fdocs.mdanalysis.org\u002F\n- **GROMACS** (alternative MD engine): https:\u002F\u002Fmanual.gromacs.org\u002F\n- **NAMD** (alternative): https:\u002F\u002Fwww.ks.uiuc.edu\u002FResearch\u002Fnamd\u002F\n- **CHARMM-GUI** (web-based system builder): https:\u002F\u002Fcharmm-gui.org\u002F\n- **AmberTools** (free Amber tools): https:\u002F\u002Fambermd.org\u002FAmberTools.php\n- **OpenMM paper**: Eastman P et al. (2017) PLOS Computational Biology. PMID: 28278240\n- **MDAnalysis paper**: Michaud-Agrawal N et al. (2011) J Computational Chemistry. PMID: 21500218\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm toolkit versions and input formats (SMILES, SDF, PDB, CIF), and sanitize molecules before computing.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Process a few molecules or one target first and inspect the outputs.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check for invalid structures, unit consistency, and applicability domain of predictive models.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Molecules fail to parse or sanitize | Report and skip them with reasons; never silently drop or alter structures. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Computational predictions are hypotheses, not experimental results; label them as such.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `rowan`: Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API.\n",{"name":5,"license":15,"metadata":49,"description":7},{"tags":50,"version":51,"category":52,"maintainer":53,"contributor":54},"molecular dynamics, OpenMM, MDAnalysis, simulation, computational chemistry","1.1","chemistry-and-drug-discovery","Kalaris Labs","Kuan-lin Huang","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-molecular-dynamics","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill molecular-dynamics",{"tool":70,"command":71},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill molecular-dynamics",{"tool":73,"command":74},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill molecular-dynamics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-molecular-dynamics\u002FSKILL.md",true,[78],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fmdanalysis_analysis.md","REFERENCE","参考文档",5643,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-molecular-dynamics\u002Freferences\u002Fmdanalysis_analysis.md",[85,115,140,165,190,213],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":94,"publishedAt":44},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","for","browsing","request","youtube transcripts",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":44},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[122,123,124,125,99,126,127,128,129,130,131,132,133,134,135,136,137,138,139,111],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","use","platform","grow","posting","competitive",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":44},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[146],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[148,133,149,150,151,152,153,154,135,155,156,55,157,158,159,160,161,162,163,164],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":171,"publishedAt":44},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[172,173,174,175,176,177,178,179,180,135,181,106,182,183,184,185,186,187,188,189],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":194,"categories":195,"keywords":197,"publishedAt":44},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[196],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[198,199,200,201,35,150,177,202,203,204,205,206,133,207,105,208,209,210,211,212],"workflow","workflow builder","builder","design","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":219,"publishedAt":44},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[220,221,222,223,224,225,226,227,228,205,229,230,231,232,233,234,107,235,236,189],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]