Creator · jinzhezenggroup
Last updated · Sep 4, 2026
A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined
Creator · jinzhezenggroup
Last updated · Sep 4, 2026
A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined
Creator · jinzhezenggroup
Last updated · Sep 4, 2026
A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined
Creator · jinzhezenggroup
Last updated · Sep 4, 2026
A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined
Sandbox only
Install targets
Codex install prompt
Install the "packmol-generate-mixture" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/data-processing/packmol-generate-mixture. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jinzhezenggroup-packmol-generate-mixture","task":"Install packmol-generate-mixture","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Maintenance
fresh
1d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
135
68/100 Quality · 77/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
135 GitHub stars
Repo activity
135 stars, 27 forks
Maintenance
1d since push
License
LGPL-3.0-or-later
Install
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixtureDo not use when
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Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jinzhezenggroup-packmol-generate-mixture/install
Agent should check
Copy prompt
Task: Use packmol-generate-mixture in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jinzhezenggroup-packmol-generate-mixture/install
Install command: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/jinzhezenggroup-packmol-generate-mixture/install
LLM text format
/api/skills/jinzhezenggroup-packmol-generate-mixture/install?format=text
Find alternatives
/api/skills/search?q=packmol-generate-mixture&limit=3
Agent prompt
Use packmol-generate-mixture for this task. Review https://www.openagentskill.com/api/skills/jinzhezenggroup-packmol-generate-mixture/install, then install with: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixtureRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/jinzhezenggroup-packmol-generate-mixture
LLM text
/api/registry/manifest/jinzhezenggroup-packmol-generate-mixture?format=text
Install alias
/api/registry/install/jinzhezenggroup-packmol-generate-mixture
Recommend
/api/registry/recommend?task=Use%20packmol-generate-mixture%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO135 GitHub stars
Stars/forks activity
CHECK135 stars, 27 forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d since push
License clarity
PASSLGPL-3.0-or-later
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
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--- name: packmol-generate-mixture description: > A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. compatibility: Requires uv and internet access (uses `uvx packmol ...`). license: LGPL-3.0-or-later metadata: author: hcustc-bot version: '1.0' repository: https://github.com/m3g/packmol repositories: - https://github.com/m3g/packmol - https://pypi.org/project/lammps-md-tools/ openclaw: emoji: 📦 requires: bins: [uv, python3] os: [linux, darwin] ---
# packmol-generate-mixture
Use Packmol to generate an initial **packed** configuration for a molecular mixture.
## Agent responsibilities (do these in order)
1. **Collect inputs** (ask if missing; do not guess):
- component structure files (XYZ), one per species (e.g. `species1.xyz`, `species2.xyz`) - molecule counts for each species (e.g. `species1: 100`, `species2: 650`) - **either** target density (g/cm^3) **or** a fixed cubic box length (Å) - Packmol `tolerance` (Å) - **output location**: output directory + output filename prefix (system name)
1. **Validate inputs**:
- confirm XYZ files exist and are readable - confirm the first line (atom count) matches the number of coordinate lines - if density-based box estimation is requested: confirm each molecule’s elemental composition can be inferred from the XYZ symbols
1. **Decide box size**:
- If user provides `box_length_A`: use it. - Else compute `box_length_A` from density (see formula below).
1. **Create a working folder** at the requested output location:
- copy the component XYZ files into it (or reference them with absolute paths)
1. **Write Packmol input** `${system_name}.inp`:
- one `structure ... end structure` block per component - all components share the same `inside box 0 0 0 L L L`
1. **Run Packmol locally**:
- Prefer: `uvx packmol -i ${system_name}.inp` - If you need to force the source package: `uvx --from packmol packmol -i ${system_name}.inp`
1. **Report results**:
- exact output paths (inp, xyz, log) - final box length (Å) and the parameters used (counts, density or fixed L, tolerance) - basic sanity checks (total molecules, total atoms)
1. **(Optional) Post-process for LAMMPS**
If the user plans to run LAMMPS (especially ReaxFF), they often need a LAMMPS data file with correct box bounds.
- If you convert XYZ -> LAMMPS data with dpdata, dpdata may write default box bounds (e.g., 0..100 Å). - Fix the bounds to match the Packmol cubic box length using `lammps-md-tools` from PyPI:
```bash uvx --from lammps-md-tools lammps-fix-box \ --in input.data \ --out output.boxfix.data \ --L 60.690 \ --wrap ```
This rewrites `xlo/xhi`, `ylo/yhi`, `zlo/zhi` to `0..L`, zeroes tilt factors, and optionally wraps atoms into the box.
## What to ask the user (plain language)
If the user didn’t specify them, ask **at minimum**:
- **Packing counts**: how many molecules of each species? (e.g., `species1=100, species2=650`) - **Box definition**: do you want to estimate a cubic box from a target density (g/cm^3), or do you want to provide a fixed cubic box length L (Å)? - **Tolerance**: what Packmol `tolerance` (Å) should be used? (common starting point: 2.0 Å) - **Output location**: which directory should receive the results, and what system name / filename prefix should be used?
If the user says “use defaults”, propose defaults:
- `tolerance = 2.0 Å` - output dir: a `packed/` subfolder under the folder containing the input XYZ - (density) **do not assume**; ask for it, but you may suggest a starting value the user can confirm.
## Input schema (recommended)
Example (replace with your own species/files):
```yaml system_name: mixture_pack output_dir: /path/to/output/packed # Choose ONE of the following: density_g_cm3: 0.25 # box_length_A: 60.69
tolerance_A: 2.0 components: - name: species1 structure_file: /path/to/species1.xyz number: 100 - name: species2 structure_file: /path/to/species2.xyz number: 650 ```
## Density → cubic box length (Å)
When `density_g_cm3` is provided and `box_length_A` is not, estimate L from total mass:
- infer each molecule’s elemental composition from its XYZ symbols - use standard atomic masses (g/mol) - compute total molar mass of the whole configuration (g/mol) - convert to mass per configuration: `m_cfg = M_total / N_A` (g) - compute volume in cm^3: `V_cm3 = m_cfg / density_g_cm3` - convert to Å^3: `V_A3 = V_cm3 * 1e24` - cubic length: `L_A = V_A3 ** (1/3)`
This is an **initial packing estimate** (geometry construction), not an equilibrated density.
## Output contract
The run should produce (within `output_dir`):
- `${system_name}.inp` (Packmol input) - `${system_name}.xyz` (packed XYZ output; name may include `_packed` suffix) - `packmol.out` (stdout log; capture with `tee`)
## Limitations (be explicit)
- Packed XYZ has coordinates only; **no topology**, **no force-field types**, **no LAMMPS data**. - Packing success ≠ physically valid structure; minimization/equilibration still required.
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for packmol-generate-mixture, ready for a manual X post.
packmol-generate-mixture: A tool for generating initial packed molecular configurations (XYZ format) from single-molecu... 135 stars https://www.openagentskill.com/skills/jinzhezenggroup-packmol-generate-mixture?ref=x
Listing + install path for packmol-generate-mixture: https://www.openagentskill.com/skills/jinzhezenggroup-packmol-generate-mixture?ref=x Install: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packm...
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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@jinzhezenggroup
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Install targets
Codex install prompt
Install the "packmol-generate-mixture" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/data-processing/packmol-generate-mixture. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jinzhezenggroup-packmol-generate-mixture","task":"Install packmol-generate-mixture","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Maintenance
fresh
1d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
135
68/100 Quality · 77/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
135 GitHub stars
Repo activity
135 stars, 27 forks
Maintenance
1d since push
License
LGPL-3.0-or-later
Install
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixtureDo not use when
Alternative
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Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
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The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jinzhezenggroup-packmol-generate-mixture/install
Agent should check
Copy prompt
Task: Use packmol-generate-mixture in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jinzhezenggroup-packmol-generate-mixture/install
Install command: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/jinzhezenggroup-packmol-generate-mixture/install
LLM text format
/api/skills/jinzhezenggroup-packmol-generate-mixture/install?format=text
Find alternatives
/api/skills/search?q=packmol-generate-mixture&limit=3
Agent prompt
Use packmol-generate-mixture for this task. Review https://www.openagentskill.com/api/skills/jinzhezenggroup-packmol-generate-mixture/install, then install with: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixtureRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/jinzhezenggroup-packmol-generate-mixture
LLM text
/api/registry/manifest/jinzhezenggroup-packmol-generate-mixture?format=text
Install alias
/api/registry/install/jinzhezenggroup-packmol-generate-mixture
Recommend
/api/registry/recommend?task=Use%20packmol-generate-mixture%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO135 GitHub stars
Stars/forks activity
CHECK135 stars, 27 forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d since push
License clarity
PASSLGPL-3.0-or-later
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
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Run autonomous deep research over web and local sources
--- name: packmol-generate-mixture description: > A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. compatibility: Requires uv and internet access (uses `uvx packmol ...`). license: LGPL-3.0-or-later metadata: author: hcustc-bot version: '1.0' repository: https://github.com/m3g/packmol repositories: - https://github.com/m3g/packmol - https://pypi.org/project/lammps-md-tools/ openclaw: emoji: 📦 requires: bins: [uv, python3] os: [linux, darwin] ---
# packmol-generate-mixture
Use Packmol to generate an initial **packed** configuration for a molecular mixture.
## Agent responsibilities (do these in order)
1. **Collect inputs** (ask if missing; do not guess):
- component structure files (XYZ), one per species (e.g. `species1.xyz`, `species2.xyz`) - molecule counts for each species (e.g. `species1: 100`, `species2: 650`) - **either** target density (g/cm^3) **or** a fixed cubic box length (Å) - Packmol `tolerance` (Å) - **output location**: output directory + output filename prefix (system name)
1. **Validate inputs**:
- confirm XYZ files exist and are readable - confirm the first line (atom count) matches the number of coordinate lines - if density-based box estimation is requested: confirm each molecule’s elemental composition can be inferred from the XYZ symbols
1. **Decide box size**:
- If user provides `box_length_A`: use it. - Else compute `box_length_A` from density (see formula below).
1. **Create a working folder** at the requested output location:
- copy the component XYZ files into it (or reference them with absolute paths)
1. **Write Packmol input** `${system_name}.inp`:
- one `structure ... end structure` block per component - all components share the same `inside box 0 0 0 L L L`
1. **Run Packmol locally**:
- Prefer: `uvx packmol -i ${system_name}.inp` - If you need to force the source package: `uvx --from packmol packmol -i ${system_name}.inp`
1. **Report results**:
- exact output paths (inp, xyz, log) - final box length (Å) and the parameters used (counts, density or fixed L, tolerance) - basic sanity checks (total molecules, total atoms)
1. **(Optional) Post-process for LAMMPS**
If the user plans to run LAMMPS (especially ReaxFF), they often need a LAMMPS data file with correct box bounds.
- If you convert XYZ -> LAMMPS data with dpdata, dpdata may write default box bounds (e.g., 0..100 Å). - Fix the bounds to match the Packmol cubic box length using `lammps-md-tools` from PyPI:
```bash uvx --from lammps-md-tools lammps-fix-box \ --in input.data \ --out output.boxfix.data \ --L 60.690 \ --wrap ```
This rewrites `xlo/xhi`, `ylo/yhi`, `zlo/zhi` to `0..L`, zeroes tilt factors, and optionally wraps atoms into the box.
## What to ask the user (plain language)
If the user didn’t specify them, ask **at minimum**:
- **Packing counts**: how many molecules of each species? (e.g., `species1=100, species2=650`) - **Box definition**: do you want to estimate a cubic box from a target density (g/cm^3), or do you want to provide a fixed cubic box length L (Å)? - **Tolerance**: what Packmol `tolerance` (Å) should be used? (common starting point: 2.0 Å) - **Output location**: which directory should receive the results, and what system name / filename prefix should be used?
If the user says “use defaults”, propose defaults:
- `tolerance = 2.0 Å` - output dir: a `packed/` subfolder under the folder containing the input XYZ - (density) **do not assume**; ask for it, but you may suggest a starting value the user can confirm.
## Input schema (recommended)
Example (replace with your own species/files):
```yaml system_name: mixture_pack output_dir: /path/to/output/packed # Choose ONE of the following: density_g_cm3: 0.25 # box_length_A: 60.69
tolerance_A: 2.0 components: - name: species1 structure_file: /path/to/species1.xyz number: 100 - name: species2 structure_file: /path/to/species2.xyz number: 650 ```
## Density → cubic box length (Å)
When `density_g_cm3` is provided and `box_length_A` is not, estimate L from total mass:
- infer each molecule’s elemental composition from its XYZ symbols - use standard atomic masses (g/mol) - compute total molar mass of the whole configuration (g/mol) - convert to mass per configuration: `m_cfg = M_total / N_A` (g) - compute volume in cm^3: `V_cm3 = m_cfg / density_g_cm3` - convert to Å^3: `V_A3 = V_cm3 * 1e24` - cubic length: `L_A = V_A3 ** (1/3)`
This is an **initial packing estimate** (geometry construction), not an equilibrated density.
## Output contract
The run should produce (within `output_dir`):
- `${system_name}.inp` (Packmol input) - `${system_name}.xyz` (packed XYZ output; name may include `_packed` suffix) - `packmol.out` (stdout log; capture with `tee`)
## Limitations (be explicit)
- Packed XYZ has coordinates only; **no topology**, **no force-field types**, **no LAMMPS data**. - Packing success ≠ physically valid structure; minimization/equilibration still required.
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packmol-generate-mixture: A tool for generating initial packed molecular configurations (XYZ format) from single-molecu... 135 stars https://www.openagentskill.com/skills/jinzhezenggroup-packmol-generate-mixture?ref=x
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Codex install prompt
Install the "packmol-generate-mixture" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/data-processing/packmol-generate-mixture. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jinzhezenggroup-packmol-generate-mixture","task":"Install packmol-generate-mixture","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
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Task: Use packmol-generate-mixture in this workspace.
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--- name: packmol-generate-mixture description: > A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. compatibility: Requires uv and internet access (uses `uvx packmol ...`). license: LGPL-3.0-or-later metadata: author: hcustc-bot version: '1.0' repository: https://github.com/m3g/packmol repositories: - https://github.com/m3g/packmol - https://pypi.org/project/lammps-md-tools/ openclaw: emoji: 📦 requires: bins: [uv, python3] os: [linux, darwin] ---
# packmol-generate-mixture
Use Packmol to generate an initial **packed** configuration for a molecular mixture.
## Agent responsibilities (do these in order)
1. **Collect inputs** (ask if missing; do not guess):
- component structure files (XYZ), one per species (e.g. `species1.xyz`, `species2.xyz`) - molecule counts for each species (e.g. `species1: 100`, `species2: 650`) - **either** target density (g/cm^3) **or** a fixed cubic box length (Å) - Packmol `tolerance` (Å) - **output location**: output directory + output filename prefix (system name)
1. **Validate inputs**:
- confirm XYZ files exist and are readable - confirm the first line (atom count) matches the number of coordinate lines - if density-based box estimation is requested: confirm each molecule’s elemental composition can be inferred from the XYZ symbols
1. **Decide box size**:
- If user provides `box_length_A`: use it. - Else compute `box_length_A` from density (see formula below).
1. **Create a working folder** at the requested output location:
- copy the component XYZ files into it (or reference them with absolute paths)
1. **Write Packmol input** `${system_name}.inp`:
- one `structure ... end structure` block per component - all components share the same `inside box 0 0 0 L L L`
1. **Run Packmol locally**:
- Prefer: `uvx packmol -i ${system_name}.inp` - If you need to force the source package: `uvx --from packmol packmol -i ${system_name}.inp`
1. **Report results**:
- exact output paths (inp, xyz, log) - final box length (Å) and the parameters used (counts, density or fixed L, tolerance) - basic sanity checks (total molecules, total atoms)
1. **(Optional) Post-process for LAMMPS**
If the user plans to run LAMMPS (especially ReaxFF), they often need a LAMMPS data file with correct box bounds.
- If you convert XYZ -> LAMMPS data with dpdata, dpdata may write default box bounds (e.g., 0..100 Å). - Fix the bounds to match the Packmol cubic box length using `lammps-md-tools` from PyPI:
```bash uvx --from lammps-md-tools lammps-fix-box \ --in input.data \ --out output.boxfix.data \ --L 60.690 \ --wrap ```
This rewrites `xlo/xhi`, `ylo/yhi`, `zlo/zhi` to `0..L`, zeroes tilt factors, and optionally wraps atoms into the box.
## What to ask the user (plain language)
If the user didn’t specify them, ask **at minimum**:
- **Packing counts**: how many molecules of each species? (e.g., `species1=100, species2=650`) - **Box definition**: do you want to estimate a cubic box from a target density (g/cm^3), or do you want to provide a fixed cubic box length L (Å)? - **Tolerance**: what Packmol `tolerance` (Å) should be used? (common starting point: 2.0 Å) - **Output location**: which directory should receive the results, and what system name / filename prefix should be used?
If the user says “use defaults”, propose defaults:
- `tolerance = 2.0 Å` - output dir: a `packed/` subfolder under the folder containing the input XYZ - (density) **do not assume**; ask for it, but you may suggest a starting value the user can confirm.
## Input schema (recommended)
Example (replace with your own species/files):
```yaml system_name: mixture_pack output_dir: /path/to/output/packed # Choose ONE of the following: density_g_cm3: 0.25 # box_length_A: 60.69
tolerance_A: 2.0 components: - name: species1 structure_file: /path/to/species1.xyz number: 100 - name: species2 structure_file: /path/to/species2.xyz number: 650 ```
## Density → cubic box length (Å)
When `density_g_cm3` is provided and `box_length_A` is not, estimate L from total mass:
- infer each molecule’s elemental composition from its XYZ symbols - use standard atomic masses (g/mol) - compute total molar mass of the whole configuration (g/mol) - convert to mass per configuration: `m_cfg = M_total / N_A` (g) - compute volume in cm^3: `V_cm3 = m_cfg / density_g_cm3` - convert to Å^3: `V_A3 = V_cm3 * 1e24` - cubic length: `L_A = V_A3 ** (1/3)`
This is an **initial packing estimate** (geometry construction), not an equilibrated density.
## Output contract
The run should produce (within `output_dir`):
- `${system_name}.inp` (Packmol input) - `${system_name}.xyz` (packed XYZ output; name may include `_packed` suffix) - `packmol.out` (stdout log; capture with `tee`)
## Limitations (be explicit)
- Packed XYZ has coordinates only; **no topology**, **no force-field types**, **no LAMMPS data**. - Packing success ≠ physically valid structure; minimization/equilibration still required.
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for packmol-generate-mixture, ready for a manual X post.
packmol-generate-mixture: A tool for generating initial packed molecular configurations (XYZ format) from single-molecu... 135 stars https://www.openagentskill.com/skills/jinzhezenggroup-packmol-generate-mixture?ref=x
Listing + install path for packmol-generate-mixture: https://www.openagentskill.com/skills/jinzhezenggroup-packmol-generate-mixture?ref=x Install: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packm...
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Sandbox only
mono-color
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1.9K StarsLast30days Skill
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61.0K StarsAcademic Research Skills
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Install targets
Codex install prompt
Install the "packmol-generate-mixture" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/data-processing/packmol-generate-mixture. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jinzhezenggroup-packmol-generate-mixture","task":"Install packmol-generate-mixture","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Maintenance
fresh
1d since push
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Needs review
Permission surface may require sandboxing
GitHub quality
135
68/100 Quality · 77/100 Trust
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Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
135 GitHub stars
Repo activity
135 stars, 27 forks
Maintenance
1d since push
License
LGPL-3.0-or-later
Install
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixtureDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jinzhezenggroup-packmol-generate-mixture/install
Agent should check
Copy prompt
Task: Use packmol-generate-mixture in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20packmol-generate-mixture%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jinzhezenggroup-packmol-generate-mixture/install
Install command: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixture
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/jinzhezenggroup-packmol-generate-mixture/install
LLM text format
/api/skills/jinzhezenggroup-packmol-generate-mixture/install?format=text
Find alternatives
/api/skills/search?q=packmol-generate-mixture&limit=3
Agent prompt
Use packmol-generate-mixture for this task. Review https://www.openagentskill.com/api/skills/jinzhezenggroup-packmol-generate-mixture/install, then install with: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packmol-generate-mixtureRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/jinzhezenggroup-packmol-generate-mixture
LLM text
/api/registry/manifest/jinzhezenggroup-packmol-generate-mixture?format=text
Install alias
/api/registry/install/jinzhezenggroup-packmol-generate-mixture
Recommend
/api/registry/recommend?task=Use%20packmol-generate-mixture%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO135 GitHub stars
Stars/forks activity
CHECK135 stars, 27 forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d since push
License clarity
PASSLGPL-3.0-or-later
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
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--- name: packmol-generate-mixture description: > A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chemistry workflows. compatibility: Requires uv and internet access (uses `uvx packmol ...`). license: LGPL-3.0-or-later metadata: author: hcustc-bot version: '1.0' repository: https://github.com/m3g/packmol repositories: - https://github.com/m3g/packmol - https://pypi.org/project/lammps-md-tools/ openclaw: emoji: 📦 requires: bins: [uv, python3] os: [linux, darwin] ---
# packmol-generate-mixture
Use Packmol to generate an initial **packed** configuration for a molecular mixture.
## Agent responsibilities (do these in order)
1. **Collect inputs** (ask if missing; do not guess):
- component structure files (XYZ), one per species (e.g. `species1.xyz`, `species2.xyz`) - molecule counts for each species (e.g. `species1: 100`, `species2: 650`) - **either** target density (g/cm^3) **or** a fixed cubic box length (Å) - Packmol `tolerance` (Å) - **output location**: output directory + output filename prefix (system name)
1. **Validate inputs**:
- confirm XYZ files exist and are readable - confirm the first line (atom count) matches the number of coordinate lines - if density-based box estimation is requested: confirm each molecule’s elemental composition can be inferred from the XYZ symbols
1. **Decide box size**:
- If user provides `box_length_A`: use it. - Else compute `box_length_A` from density (see formula below).
1. **Create a working folder** at the requested output location:
- copy the component XYZ files into it (or reference them with absolute paths)
1. **Write Packmol input** `${system_name}.inp`:
- one `structure ... end structure` block per component - all components share the same `inside box 0 0 0 L L L`
1. **Run Packmol locally**:
- Prefer: `uvx packmol -i ${system_name}.inp` - If you need to force the source package: `uvx --from packmol packmol -i ${system_name}.inp`
1. **Report results**:
- exact output paths (inp, xyz, log) - final box length (Å) and the parameters used (counts, density or fixed L, tolerance) - basic sanity checks (total molecules, total atoms)
1. **(Optional) Post-process for LAMMPS**
If the user plans to run LAMMPS (especially ReaxFF), they often need a LAMMPS data file with correct box bounds.
- If you convert XYZ -> LAMMPS data with dpdata, dpdata may write default box bounds (e.g., 0..100 Å). - Fix the bounds to match the Packmol cubic box length using `lammps-md-tools` from PyPI:
```bash uvx --from lammps-md-tools lammps-fix-box \ --in input.data \ --out output.boxfix.data \ --L 60.690 \ --wrap ```
This rewrites `xlo/xhi`, `ylo/yhi`, `zlo/zhi` to `0..L`, zeroes tilt factors, and optionally wraps atoms into the box.
## What to ask the user (plain language)
If the user didn’t specify them, ask **at minimum**:
- **Packing counts**: how many molecules of each species? (e.g., `species1=100, species2=650`) - **Box definition**: do you want to estimate a cubic box from a target density (g/cm^3), or do you want to provide a fixed cubic box length L (Å)? - **Tolerance**: what Packmol `tolerance` (Å) should be used? (common starting point: 2.0 Å) - **Output location**: which directory should receive the results, and what system name / filename prefix should be used?
If the user says “use defaults”, propose defaults:
- `tolerance = 2.0 Å` - output dir: a `packed/` subfolder under the folder containing the input XYZ - (density) **do not assume**; ask for it, but you may suggest a starting value the user can confirm.
## Input schema (recommended)
Example (replace with your own species/files):
```yaml system_name: mixture_pack output_dir: /path/to/output/packed # Choose ONE of the following: density_g_cm3: 0.25 # box_length_A: 60.69
tolerance_A: 2.0 components: - name: species1 structure_file: /path/to/species1.xyz number: 100 - name: species2 structure_file: /path/to/species2.xyz number: 650 ```
## Density → cubic box length (Å)
When `density_g_cm3` is provided and `box_length_A` is not, estimate L from total mass:
- infer each molecule’s elemental composition from its XYZ symbols - use standard atomic masses (g/mol) - compute total molar mass of the whole configuration (g/mol) - convert to mass per configuration: `m_cfg = M_total / N_A` (g) - compute volume in cm^3: `V_cm3 = m_cfg / density_g_cm3` - convert to Å^3: `V_A3 = V_cm3 * 1e24` - cubic length: `L_A = V_A3 ** (1/3)`
This is an **initial packing estimate** (geometry construction), not an equilibrated density.
## Output contract
The run should produce (within `output_dir`):
- `${system_name}.inp` (Packmol input) - `${system_name}.xyz` (packed XYZ output; name may include `_packed` suffix) - `packmol.out` (stdout log; capture with `tee`)
## Limitations (be explicit)
- Packed XYZ has coordinates only; **no topology**, **no force-field types**, **no LAMMPS data**. - Packing success ≠ physically valid structure; minimization/equilibration still required.
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for packmol-generate-mixture, ready for a manual X post.
packmol-generate-mixture: A tool for generating initial packed molecular configurations (XYZ format) from single-molecu... 135 stars https://www.openagentskill.com/skills/jinzhezenggroup-packmol-generate-mixture?ref=x
Listing + install path for packmol-generate-mixture: https://www.openagentskill.com/skills/jinzhezenggroup-packmol-generate-mixture?ref=x Install: npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill packm...
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Sandbox only
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
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61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K Starsstandard package or runtime install path
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
standard package or runtime install path
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
standard package or runtime install path
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
standard package or runtime install path
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness