Creator · learningmatter-mit
Last updated · Sep 6, 2026
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
Creator · learningmatter-mit
Last updated · Sep 6, 2026
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
Creator · learningmatter-mit
Last updated · Sep 6, 2026
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
Creator · learningmatter-mit
Last updated · Sep 6, 2026
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
Sandbox only
Install targets
Codex install prompt
Install the "chem-irc-verification" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-irc-verification. 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: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 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":"learningmatter-mit-chem-irc-verification","task":"Install chem-irc-verification","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
Maintenance
fresh
5d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
161
69/100 Quality · 75/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
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
161 GitHub stars
Repo activity
161 stars, 24 forks
Maintenance
5d since push
License
MIT
Install
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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 learningmatter-mit/AtomisticSkills --skill chem-irc-verificationDo not use when
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-bond-dissociation
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-md
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
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%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/learningmatter-mit-chem-irc-verification/install
Agent should check
Copy prompt
Task: Use chem-irc-verification in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install
Install command: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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/learningmatter-mit-chem-irc-verification/install
LLM text format
/api/skills/learningmatter-mit-chem-irc-verification/install?format=text
Find alternatives
/api/skills/search?q=chem-irc-verification&limit=3
Agent prompt
Use chem-irc-verification for this task. Review https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install, then install with: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verificationRegistry 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/learningmatter-mit-chem-irc-verification
LLM text
/api/registry/manifest/learningmatter-mit-chem-irc-verification?format=text
Install alias
/api/registry/install/learningmatter-mit-chem-irc-verification
Recommend
/api/registry/recommend?task=Use%20chem-irc-verification%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
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
GitHub automation
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
INFO161 GitHub stars
Stars/forks activity
CHECK161 stars, 24 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
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
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
--- name: chem-irc-verification description: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. category: [chemistry] ---
# IRC Verification with Sella
Verify that a saddle-point-optimized TS connects the intended reactant and product.
## Scope
- Domain: molecular chemistry only (non-periodic systems). - Trigger: user has optimized reactant/product + optimized TS and needs IRC endpoint verification. - Exclusions: periodic systems and barrier-only workflows.
## Tool
### `verify_irc_sella.py`
Runs forward/reverse IRC from the TS, optionally relaxes endpoints, then checks mapping quality.
### Use with MACE
```bash # Env: mace-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type mace \ --model_name MACE-OFF23-small \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
### Use with FAIRChem (UMA)
```bash # Env: fairchem-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type fairchem \ --model_name uma-s-1p1 \ --task_name omol \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
## Arguments
- `--reactant`: required optimized reactant geometry. - `--product`: required optimized product geometry. - `--ts`: required saddle-point-optimized TS geometry. - `--model_type`: required backend (`mace` or `fairchem`). - `--model_name`: optional model identifier/checkpoint. - `--task_name`: optional model head/task (for UMA molecular runs use `omol`). - `--device`: `auto|cpu|cuda` (default `auto`). - `--fmax`: IRC convergence threshold in eV/A (default `0.02`). - `--steps`: maximum IRC steps per direction (default `1000`). - `--rmsd_threshold`: endpoint RMSD threshold in A (default `0.20`). - `--relax_endpoints`: `true|false`, relax IRC endpoints before matching (default `true`). - `--endpoint_relax_fmax`: force threshold for optional endpoint relaxation (default `0.02`). - `--output_dir`: required output directory.
## Outputs
- `irc_forward.traj`, `irc_reverse.traj`: IRC trajectories. - `irc_forward.log`, `irc_reverse.log`: IRC logs. - `irc_forward_endpoint.xyz`, `irc_reverse_endpoint.xyz`: terminal endpoint geometries. - `irc_verification_results.json`: endpoint assignment and pass/fail summary.
`irc_verification_results.json` fields include: - selected endpoint assignment (`endpoint_mapping`) - per-pair metrics (`connectivity_match`, `rmsd_angstrom`, thresholds) - all candidate assignments with total RMSD - final decision (`verification_passed`)
## Verification Criterion
Verification passes only if both mapped endpoint-target pairs satisfy: - same formula and atom order - connectivity graph match - Kabsch-aligned RMSD <= `rmsd_threshold`
Default criterion: both pairs must pass with `rmsd_threshold = 0.20 A`.
## Model Guidance
- Recommended for molecules: - `MACE-OFF23-small` / `MACE-OFF23-medium` - `uma-s-1p1` with `--task_name omol` - Use the same backend/model/head as TS optimization to avoid model inconsistency.
## Prerequisites And Constraints
- Activate `mace-agent` or `fairchem-agent` depending on backend. - Script enforces `pbc=False` for all inputs. - Reactant/product/TS must have identical composition and consistent atom ordering.
## Examples
See `examples/` directory for sample inputs and outputs. ---
**Author:** Juno Nam **Contact:** [GitHub @recisic](https://github.com/recisic)
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 chem-irc-verification, ready for a manual X post.
A practical pick for a real agent workflow: chem-irc-verification: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 161 stars https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x
Listing + install path for chem-irc-verification: https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x Install: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to learningmatter-mit but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification/audit)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)learningmatter-mit
@learningmatter-mit
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-solution-md
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
161 StarsSandbox only
Install targets
Codex install prompt
Install the "chem-irc-verification" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-irc-verification. 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: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 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":"learningmatter-mit-chem-irc-verification","task":"Install chem-irc-verification","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
Maintenance
fresh
5d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
161
69/100 Quality · 75/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
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
161 GitHub stars
Repo activity
161 stars, 24 forks
Maintenance
5d since push
License
MIT
Install
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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 learningmatter-mit/AtomisticSkills --skill chem-irc-verificationDo not use when
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-bond-dissociation
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-md
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
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%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/learningmatter-mit-chem-irc-verification/install
Agent should check
Copy prompt
Task: Use chem-irc-verification in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install
Install command: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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/learningmatter-mit-chem-irc-verification/install
LLM text format
/api/skills/learningmatter-mit-chem-irc-verification/install?format=text
Find alternatives
/api/skills/search?q=chem-irc-verification&limit=3
Agent prompt
Use chem-irc-verification for this task. Review https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install, then install with: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verificationRegistry 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/learningmatter-mit-chem-irc-verification
LLM text
/api/registry/manifest/learningmatter-mit-chem-irc-verification?format=text
Install alias
/api/registry/install/learningmatter-mit-chem-irc-verification
Recommend
/api/registry/recommend?task=Use%20chem-irc-verification%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
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
GitHub automation
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
INFO161 GitHub stars
Stars/forks activity
CHECK161 stars, 24 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
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
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
--- name: chem-irc-verification description: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. category: [chemistry] ---
# IRC Verification with Sella
Verify that a saddle-point-optimized TS connects the intended reactant and product.
## Scope
- Domain: molecular chemistry only (non-periodic systems). - Trigger: user has optimized reactant/product + optimized TS and needs IRC endpoint verification. - Exclusions: periodic systems and barrier-only workflows.
## Tool
### `verify_irc_sella.py`
Runs forward/reverse IRC from the TS, optionally relaxes endpoints, then checks mapping quality.
### Use with MACE
```bash # Env: mace-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type mace \ --model_name MACE-OFF23-small \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
### Use with FAIRChem (UMA)
```bash # Env: fairchem-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type fairchem \ --model_name uma-s-1p1 \ --task_name omol \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
## Arguments
- `--reactant`: required optimized reactant geometry. - `--product`: required optimized product geometry. - `--ts`: required saddle-point-optimized TS geometry. - `--model_type`: required backend (`mace` or `fairchem`). - `--model_name`: optional model identifier/checkpoint. - `--task_name`: optional model head/task (for UMA molecular runs use `omol`). - `--device`: `auto|cpu|cuda` (default `auto`). - `--fmax`: IRC convergence threshold in eV/A (default `0.02`). - `--steps`: maximum IRC steps per direction (default `1000`). - `--rmsd_threshold`: endpoint RMSD threshold in A (default `0.20`). - `--relax_endpoints`: `true|false`, relax IRC endpoints before matching (default `true`). - `--endpoint_relax_fmax`: force threshold for optional endpoint relaxation (default `0.02`). - `--output_dir`: required output directory.
## Outputs
- `irc_forward.traj`, `irc_reverse.traj`: IRC trajectories. - `irc_forward.log`, `irc_reverse.log`: IRC logs. - `irc_forward_endpoint.xyz`, `irc_reverse_endpoint.xyz`: terminal endpoint geometries. - `irc_verification_results.json`: endpoint assignment and pass/fail summary.
`irc_verification_results.json` fields include: - selected endpoint assignment (`endpoint_mapping`) - per-pair metrics (`connectivity_match`, `rmsd_angstrom`, thresholds) - all candidate assignments with total RMSD - final decision (`verification_passed`)
## Verification Criterion
Verification passes only if both mapped endpoint-target pairs satisfy: - same formula and atom order - connectivity graph match - Kabsch-aligned RMSD <= `rmsd_threshold`
Default criterion: both pairs must pass with `rmsd_threshold = 0.20 A`.
## Model Guidance
- Recommended for molecules: - `MACE-OFF23-small` / `MACE-OFF23-medium` - `uma-s-1p1` with `--task_name omol` - Use the same backend/model/head as TS optimization to avoid model inconsistency.
## Prerequisites And Constraints
- Activate `mace-agent` or `fairchem-agent` depending on backend. - Script enforces `pbc=False` for all inputs. - Reactant/product/TS must have identical composition and consistent atom ordering.
## Examples
See `examples/` directory for sample inputs and outputs. ---
**Author:** Juno Nam **Contact:** [GitHub @recisic](https://github.com/recisic)
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 chem-irc-verification, ready for a manual X post.
A practical pick for a real agent workflow: chem-irc-verification: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 161 stars https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x
Listing + install path for chem-irc-verification: https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x Install: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to learningmatter-mit but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification/audit)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)learningmatter-mit
@learningmatter-mit
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-solution-md
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
161 StarsSandbox only
Install targets
Codex install prompt
Install the "chem-irc-verification" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-irc-verification. 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: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 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":"learningmatter-mit-chem-irc-verification","task":"Install chem-irc-verification","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
Maintenance
fresh
5d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
161
69/100 Quality · 75/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
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
161 GitHub stars
Repo activity
161 stars, 24 forks
Maintenance
5d since push
License
MIT
Install
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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 learningmatter-mit/AtomisticSkills --skill chem-irc-verificationDo not use when
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-bond-dissociation
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-md
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
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%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/learningmatter-mit-chem-irc-verification/install
Agent should check
Copy prompt
Task: Use chem-irc-verification in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install
Install command: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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/learningmatter-mit-chem-irc-verification/install
LLM text format
/api/skills/learningmatter-mit-chem-irc-verification/install?format=text
Find alternatives
/api/skills/search?q=chem-irc-verification&limit=3
Agent prompt
Use chem-irc-verification for this task. Review https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install, then install with: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verificationRegistry 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/learningmatter-mit-chem-irc-verification
LLM text
/api/registry/manifest/learningmatter-mit-chem-irc-verification?format=text
Install alias
/api/registry/install/learningmatter-mit-chem-irc-verification
Recommend
/api/registry/recommend?task=Use%20chem-irc-verification%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
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
GitHub automation
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
INFO161 GitHub stars
Stars/forks activity
CHECK161 stars, 24 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
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
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
--- name: chem-irc-verification description: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. category: [chemistry] ---
# IRC Verification with Sella
Verify that a saddle-point-optimized TS connects the intended reactant and product.
## Scope
- Domain: molecular chemistry only (non-periodic systems). - Trigger: user has optimized reactant/product + optimized TS and needs IRC endpoint verification. - Exclusions: periodic systems and barrier-only workflows.
## Tool
### `verify_irc_sella.py`
Runs forward/reverse IRC from the TS, optionally relaxes endpoints, then checks mapping quality.
### Use with MACE
```bash # Env: mace-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type mace \ --model_name MACE-OFF23-small \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
### Use with FAIRChem (UMA)
```bash # Env: fairchem-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type fairchem \ --model_name uma-s-1p1 \ --task_name omol \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
## Arguments
- `--reactant`: required optimized reactant geometry. - `--product`: required optimized product geometry. - `--ts`: required saddle-point-optimized TS geometry. - `--model_type`: required backend (`mace` or `fairchem`). - `--model_name`: optional model identifier/checkpoint. - `--task_name`: optional model head/task (for UMA molecular runs use `omol`). - `--device`: `auto|cpu|cuda` (default `auto`). - `--fmax`: IRC convergence threshold in eV/A (default `0.02`). - `--steps`: maximum IRC steps per direction (default `1000`). - `--rmsd_threshold`: endpoint RMSD threshold in A (default `0.20`). - `--relax_endpoints`: `true|false`, relax IRC endpoints before matching (default `true`). - `--endpoint_relax_fmax`: force threshold for optional endpoint relaxation (default `0.02`). - `--output_dir`: required output directory.
## Outputs
- `irc_forward.traj`, `irc_reverse.traj`: IRC trajectories. - `irc_forward.log`, `irc_reverse.log`: IRC logs. - `irc_forward_endpoint.xyz`, `irc_reverse_endpoint.xyz`: terminal endpoint geometries. - `irc_verification_results.json`: endpoint assignment and pass/fail summary.
`irc_verification_results.json` fields include: - selected endpoint assignment (`endpoint_mapping`) - per-pair metrics (`connectivity_match`, `rmsd_angstrom`, thresholds) - all candidate assignments with total RMSD - final decision (`verification_passed`)
## Verification Criterion
Verification passes only if both mapped endpoint-target pairs satisfy: - same formula and atom order - connectivity graph match - Kabsch-aligned RMSD <= `rmsd_threshold`
Default criterion: both pairs must pass with `rmsd_threshold = 0.20 A`.
## Model Guidance
- Recommended for molecules: - `MACE-OFF23-small` / `MACE-OFF23-medium` - `uma-s-1p1` with `--task_name omol` - Use the same backend/model/head as TS optimization to avoid model inconsistency.
## Prerequisites And Constraints
- Activate `mace-agent` or `fairchem-agent` depending on backend. - Script enforces `pbc=False` for all inputs. - Reactant/product/TS must have identical composition and consistent atom ordering.
## Examples
See `examples/` directory for sample inputs and outputs. ---
**Author:** Juno Nam **Contact:** [GitHub @recisic](https://github.com/recisic)
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 chem-irc-verification, ready for a manual X post.
A practical pick for a real agent workflow: chem-irc-verification: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 161 stars https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x
Listing + install path for chem-irc-verification: https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x Install: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to learningmatter-mit but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification/audit)
[](https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)learningmatter-mit
@learningmatter-mit
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-solution-md
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
161 StarsSandbox only
Install targets
Codex install prompt
Install the "chem-irc-verification" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-irc-verification. 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: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 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":"learningmatter-mit-chem-irc-verification","task":"Install chem-irc-verification","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
Maintenance
fresh
5d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
161
69/100 Quality · 75/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
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
161 GitHub stars
Repo activity
161 stars, 24 forks
Maintenance
5d since push
License
MIT
Install
npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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 learningmatter-mit/AtomisticSkills --skill chem-irc-verificationDo not use when
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-bond-dissociation
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-md
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search
Alternative
161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
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%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/learningmatter-mit-chem-irc-verification/install
Agent should check
Copy prompt
Task: Use chem-irc-verification in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-irc-verification%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install
Install command: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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/learningmatter-mit-chem-irc-verification/install
LLM text format
/api/skills/learningmatter-mit-chem-irc-verification/install?format=text
Find alternatives
/api/skills/search?q=chem-irc-verification&limit=3
Agent prompt
Use chem-irc-verification for this task. Review https://www.openagentskill.com/api/skills/learningmatter-mit-chem-irc-verification/install, then install with: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verificationRegistry 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/learningmatter-mit-chem-irc-verification
LLM text
/api/registry/manifest/learningmatter-mit-chem-irc-verification?format=text
Install alias
/api/registry/install/learningmatter-mit-chem-irc-verification
Recommend
/api/registry/recommend?task=Use%20chem-irc-verification%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
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
GitHub automation
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
INFO161 GitHub stars
Stars/forks activity
CHECK161 stars, 24 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
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
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
--- name: chem-irc-verification description: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. category: [chemistry] ---
# IRC Verification with Sella
Verify that a saddle-point-optimized TS connects the intended reactant and product.
## Scope
- Domain: molecular chemistry only (non-periodic systems). - Trigger: user has optimized reactant/product + optimized TS and needs IRC endpoint verification. - Exclusions: periodic systems and barrier-only workflows.
## Tool
### `verify_irc_sella.py`
Runs forward/reverse IRC from the TS, optionally relaxes endpoints, then checks mapping quality.
### Use with MACE
```bash # Env: mace-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type mace \ --model_name MACE-OFF23-small \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
### Use with FAIRChem (UMA)
```bash # Env: fairchem-agent python .agents/skills/chem-irc-verification/scripts/verify_irc_sella.py \ --reactant reactant_optimized.xyz \ --product product_optimized.xyz \ --ts ts_optimized.xyz \ --model_type fairchem \ --model_name uma-s-1p1 \ --task_name omol \ --fmax 0.02 \ --steps 1000 \ --rmsd_threshold 0.20 \ --relax_endpoints true \ --endpoint_relax_fmax 0.02 \ --output_dir results/irc ```
## Arguments
- `--reactant`: required optimized reactant geometry. - `--product`: required optimized product geometry. - `--ts`: required saddle-point-optimized TS geometry. - `--model_type`: required backend (`mace` or `fairchem`). - `--model_name`: optional model identifier/checkpoint. - `--task_name`: optional model head/task (for UMA molecular runs use `omol`). - `--device`: `auto|cpu|cuda` (default `auto`). - `--fmax`: IRC convergence threshold in eV/A (default `0.02`). - `--steps`: maximum IRC steps per direction (default `1000`). - `--rmsd_threshold`: endpoint RMSD threshold in A (default `0.20`). - `--relax_endpoints`: `true|false`, relax IRC endpoints before matching (default `true`). - `--endpoint_relax_fmax`: force threshold for optional endpoint relaxation (default `0.02`). - `--output_dir`: required output directory.
## Outputs
- `irc_forward.traj`, `irc_reverse.traj`: IRC trajectories. - `irc_forward.log`, `irc_reverse.log`: IRC logs. - `irc_forward_endpoint.xyz`, `irc_reverse_endpoint.xyz`: terminal endpoint geometries. - `irc_verification_results.json`: endpoint assignment and pass/fail summary.
`irc_verification_results.json` fields include: - selected endpoint assignment (`endpoint_mapping`) - per-pair metrics (`connectivity_match`, `rmsd_angstrom`, thresholds) - all candidate assignments with total RMSD - final decision (`verification_passed`)
## Verification Criterion
Verification passes only if both mapped endpoint-target pairs satisfy: - same formula and atom order - connectivity graph match - Kabsch-aligned RMSD <= `rmsd_threshold`
Default criterion: both pairs must pass with `rmsd_threshold = 0.20 A`.
## Model Guidance
- Recommended for molecules: - `MACE-OFF23-small` / `MACE-OFF23-medium` - `uma-s-1p1` with `--task_name omol` - Use the same backend/model/head as TS optimization to avoid model inconsistency.
## Prerequisites And Constraints
- Activate `mace-agent` or `fairchem-agent` depending on backend. - Script enforces `pbc=False` for all inputs. - Reactant/product/TS must have identical composition and consistent atom ordering.
## Examples
See `examples/` directory for sample inputs and outputs. ---
**Author:** Juno Nam **Contact:** [GitHub @recisic](https://github.com/recisic)
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 chem-irc-verification, ready for a manual X post.
A practical pick for a real agent workflow: chem-irc-verification: Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks. 161 stars https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x
Listing + install path for chem-irc-verification: https://www.openagentskill.com/skills/learningmatter-mit-chem-irc-verification?ref=x Install: npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-solution-md
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
161 StarsPermission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness