DAG Library
code-yeongyu/oh-my-openagent
Stores a DAG definition once and re-runs it by name, with key rotation controlling whether each run is fresh or reuses an earlier result.
One-shot CatGo DAG workflow construction. An agent skill from Hello-QM/catgo-LRG.
$ npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Hello-QM/catgo-LRG catgo-build-workflow --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/server/catgo/workflow/skills/workflow_builder .claude/skills/catgo-build-workflow && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "catgo-build-workflow" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/workflow_builder into .claude/skills/catgo-build-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "catgo-build-workflow", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/workflow_builderType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Hello-QM/catgo-LRG catgo-build-workflow --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .agents/skills && cp -r skills-src/server/catgo/workflow/skills/workflow_builder .agents/skills/catgo-build-workflow && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "catgo-build-workflow" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/workflow_builder into .agents/skills/catgo-build-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "catgo-build-workflow", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Hello-QM/catgo-LRG catgo-build-workflow --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/server/catgo/workflow/skills/workflow_builder .cursor/skills/catgo-build-workflow && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "catgo-build-workflow" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/workflow_builder into .cursor/skills/catgo-build-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "catgo-build-workflow", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Hello-QM/catgo-LRG.git --path server/catgo/workflow/skills/workflow_builder--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Hello-QM/catgo-LRG catgo-build-workflow --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/server/catgo/workflow/skills/workflow_builder .gemini/skills/catgo-build-workflow && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "catgo-build-workflow" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/workflow_builder into .gemini/skills/catgo-build-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "catgo-build-workflow", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Hello-QM/catgo-LRG catgo-build-workflowInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .github/skills && cp -r skills-src/server/catgo/workflow/skills/workflow_builder .github/skills/catgo-build-workflow && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "catgo-build-workflow" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/workflow_builder into .github/skills/catgo-build-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "catgo-build-workflow", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Hello-QM/catgo-LRG catgo-build-workflow --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/server/catgo/workflow/skills/workflow_builder .opencode/skills/catgo-build-workflow && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "catgo-build-workflow" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/workflow_builder into .opencode/skills/catgo-build-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "catgo-build-workflow", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
catgo-build-workflowOne-shot CatGo DAG workflow construction. An agent skill from Hello-QM/catgo-LRG.
Catgo Build Workflow is an agent skill from Hello-QM/catgo-LRG. One-shot CatGo DAG workflow construction. Use whenever the user asks to "create a workflow", "build a pipeline", "set up CO2RR/OER/HER/NEB/DOS/slow-growth", "make a workflow for X reaction", or any catalysis pipeline involving structureinput → calculation → analysis. Skips the exploration phase (avoids listing nodetypes / templates / nodedetails repeatedly) and goes straight to a single catgoworkflow create + batch round-trip with the full graphjson. Triggers in Chinese on 创建工作流, 建立工作流, 工作流, 计算流, 反应路径, 自由能图.
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: AI-driven workbench for computational materials science — interactive 3D structure viewer, natural-language CatBot assistant, visual DAG workflow engine, HPC job submission… The licence is AGPL-3.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit fd6291b. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Catgo Build Workflow loads about 3.1k tokens when it runs. Until then it costs about 136 tokens; SKILL.md has 1,102 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from Hello-QM/catgo-LRG at commit fd6291b, republished under its AGPL-3.0 licence (© Hello-QM). 1,102 words, ~3,124 tokens.
.claude/skills/catgo-build-workflow/SKILL.md (or your agent's skills folder).Tight playbook for assembling a CatGo workflow in one or two MCP round-trips. The default CatBot path explores node_types, node_details, templates, then incrementally adds nodes and edges — that is 8+ MCP calls and the user sees the "Thinking…" indicator for tens of seconds. This skill cuts it to 1–2 calls.
Activities, overpotentials, and barriers reported in catalysis literature are differences in Gibbs free energy at the operating temperature, not DFT electronic energies. So whenever the user asks for a reaction pathway — CO2RR, OER, HER, NRR, ORR, NEB, slow-growth, C–N coupling, anything ending in *RR, anything called "free energy diagram" or "volcano plot" — the workflow must contain a freq node between geo_opt (or md) and free_energy (or the reaction-specific analysis node). Without freq there is no ZPE and no thermal/entropic correction, the resulting numbers cannot be compared to experiment, and the user is silently wrong.
If the user proposes a mechanism workflow without a freq step, add one anyway and tell them one short sentence why ("Inserted a freq step so the ΔG values include ZPE + TS — without it the free-energy diagram is just an electronic-energy diagram"). If they explicitly say "skip freq for now, I just want a quick electronic-energy scan", honour it but flag that the result is not a Gibbs energy.
The freq node must run on the same geometry as the final relaxation it sits after — chaining geo_opt → freq → free_energy keeps the geometries consistent. For adsorbate thermochemistry, use freeze_mode: "adsorbate": freeze the full slab and vibrate only atoms tagged by adsorbate_place.
Every surface-reaction workflow built from a bulk structure must use structure_input → slab_gen → geo_opt(clean slab) → adsorbate_place. Reuse the same relaxed clean-slab output for every adsorbate branch. Never connect slab_gen directly to adsorbate_place: doing so optimizes only slab+adsorbate states and leaves no consistent relaxed clean-slab reference.
If the user wants to modify an existing workflow (add a node to one that already exists), prefer a direct catgo_workflow {action:"add_node"} call rather than reloading this skill.
catgo_quickbuild and stop; its server-side recipes enforce the clean-slab invariant. Use the manual path below only for custom graphs.graph_json payload.catgo_workflow with action="create", name="<descriptive>", template_id only if you genuinely want the backend's stock template (most of the time you do not, because the recipes here are tighter). Otherwise omit template_id — create will auto-add a structure_input node seeded from the viewer's current structure.catgo_workflow with action="batch" and an operations array carrying every add_node + connect step in one round-trip. Do not call add_node one at a time.<name>': N nodes, M edges. Open the Workflow tab to inspect."). Do not list every node — the user can see the graph in the editor.That is the entire happy path. Do not call templates, node_types, node_details, list_presets, or get before creating unless the user explicitly asks "what templates exist?" — those calls only exist for discovery and the recipes below already cover the common cases.
Each recipe gives the operations array you pass to batch. The seed structure_input node is already created for you by create; reference it as "si" in from fields. Use stable short IDs (n1, n2, …) for new nodes — these only need to be unique within the workflow.
[
{"op":"add_node","id":"opt","type":"geo_opt","x":300,"y":200,"params":{"software":"vasp","encut":520,"ediffg":-0.03}},
{"op":"add_node","id":"freq","type":"freq","x":520,"y":200,"params":{"software":"vasp","freeze_mode":"adsorbate"}},
{"op":"add_node","id":"fe","type":"free_energy","x":740,"y":200,"params":{"temperature":298.15,"reference":"CHE"}},
{"op":"connect","from":"si","to":"opt"},
{"op":"connect","from":"opt","to":"freq"},
{"op":"connect","from":"freq","to":"fe"}
]For multi-intermediate CO2RR (CO2* → COOH* → CO* → CHO* …), duplicate geo_opt + freq per intermediate, all wired to the same free_energy node which aggregates ΔG values.
[
{"op":"add_node","id":"opt","type":"geo_opt","x":300,"y":200,"params":{"software":"vasp","encut":520}},
{"op":"add_node","id":"freq","type":"freq","x":520,"y":200,"params":{"software":"vasp"}},
{"op":"add_node","id":"oer","type":"oer_analysis","x":740,"y":200,"params":{"reference":"CHE","pH":0}},
{"op":"connect","from":"si","to":"opt"},
{"op":"connect","from":"opt","to":"freq"},
{"op":"connect","from":"freq","to":"oer"}
]If the OER analysis node type is not registered, fall back to free_energy and tell the user to flip the analysis mode in the node panel.
[
{"op":"add_node","id":"opt","type":"geo_opt","x":300,"y":200,"params":{"software":"vasp","encut":520}},
{"op":"add_node","id":"freq","type":"freq","x":520,"y":200,"params":{"software":"vasp"}},
{"op":"add_node","id":"fe","type":"free_energy","x":740,"y":200,"params":{"reference":"CHE","target":"H"}},
{"op":"connect","from":"si","to":"opt"},
{"op":"connect","from":"opt","to":"freq"},
{"op":"connect","from":"freq","to":"fe"}
][
{"op":"add_node","id":"r_opt","type":"geo_opt","x":300,"y":120,"params":{"software":"vasp","label":"reactant"}},
{"op":"add_node","id":"p_opt","type":"geo_opt","x":300,"y":320,"params":{"software":"vasp","label":"product"}},
{"op":"add_node","id":"neb","type":"neb","x":540,"y":220,"params":{"software":"vasp","n_images":7,"climbing":true}},
{"op":"add_node","id":"freq","type":"freq","x":760,"y":220,"params":{"software":"vasp"}},
{"op":"connect","from":"si","to":"r_opt"},
{"op":"connect","from":"si","to":"p_opt"},
{"op":"connect","from":"r_opt","to":"neb","handle":"reactant"},
{"op":"connect","from":"p_opt","to":"neb","handle":"product"},
{"op":"connect","from":"neb","to":"freq"}
]NEB needs two structure_input nodes if reactant and product are different structures. Ask the user before assuming the seed structure is one endpoint. If they confirm two endpoints, add a second structure_input in the operations array and skip the auto-seeded one (or repurpose it as the reactant).
[
{"op":"add_node","id":"opt","type":"geo_opt","x":300,"y":200,"params":{"software":"vasp","encut":520}},
{"op":"add_node","id":"sp","type":"single_point","x":520,"y":200,"params":{"software":"vasp","encut":520}},
{"op":"add_node","id":"dos","type":"dos_analysis","x":740,"y":200,"params":{"emin":-10,"emax":5,"d_band_center":true}},
{"op":"connect","from":"si","to":"opt"},
{"op":"connect","from":"opt","to":"sp"},
{"op":"connect","from":"sp","to":"dos"}
]Add a second single_point for band structure with a denser k-path if the user asks for both.
[
{"op":"add_node","id":"opt","type":"geo_opt","x":300,"y":200,"params":{"software":"vasp"}},
{"op":"add_node","id":"equil","type":"md","x":520,"y":200,"params":{"software":"vasp","ensemble":"nvt","temperature":300,"nsw":2000,"potim":0.5}},
{"op":"add_node","id":"sg","type":"slow_growth","x":740,"y":200,"params":{"software":"vasp","iconst":"<user-provided>"}},
{"op":"add_node","id":"barrier","type":"md_analysis","x":960,"y":200,"params":{"mode":"barrier"}},
{"op":"connect","from":"si","to":"opt"},
{"op":"connect","from":"opt","to":"equil"},
{"op":"connect","from":"equil","to":"sg"},
{"op":"connect","from":"sg","to":"barrier"}
]The iconst template depends on the reaction coordinate — for C–N coupling use R 1 2 0 (where 1 and 2 are the atom indices and the trailing 0 increments per step). Confirm the indices with the user before submitting.
[
{"op":"add_node","label":"bulk_opt","node_type":"cell_opt","params":{"software":"vasp","encut":520}},
{"op":"add_node","label":"slab","node_type":"slab_gen","params":{"miller":"1,1,1","layers":4,"vacuum":15}},
{"op":"add_node","label":"slab_opt","node_type":"geo_opt","params":{"software":"vasp","frozen_layers":2}},
{"op":"add_node","label":"ads","node_type":"adsorbate_place","params":{"species":"CO","site":"ontop"}},
{"op":"add_node","label":"ads_opt","node_type":"geo_opt","params":{"software":"vasp","frozen_layers":2}},
{"op":"connect","from_id":"si","to_id":"bulk_opt"},
{"op":"connect","from_id":"bulk_opt","to_id":"slab"},
{"op":"connect","from_id":"slab","to_id":"slab_opt"},
{"op":"connect","from_id":"slab_opt","to_id":"ads"},
{"op":"connect","from_id":"ads","to_id":"ads_opt"}
]When the user asks for something not in the recipes above, you can usually compose it from these node types. Do not call node_types to refresh this list unless the user reports a node-type error.
| Type | Purpose | Common params |
|---|---|---|
structure_input | Seed structure (POSCAR/CIF/MP-ID) | mp_id, structure_json |
cell_opt | Cell + ion relaxation (ISIF=3) | software, encut, ediffg |
geo_opt | Ion-only relaxation (ISIF=2) | software, encut, ediffg, frozen_layers |
single_point | Static SCF | software, encut, ismear |
md | Molecular dynamics | ensemble, temperature, nsw, potim |
slow_growth | Constrained AIMD via ICONST | iconst, nsw |
freq | Vibrational frequencies | freeze_mode, freeze_layers |
neb | NEB / CI-NEB TS search | n_images, climbing |
ts_search | Sella / DIMER TS | software, mode |
slab_gen | Cut slab from bulk | miller, layers, vacuum, supercell |
adsorbate_place | Place adsorbate on slab | species, site, height |
dos_analysis | DOS / PDOS / d-band | emin, emax, d_band_center |
free_energy | ΔG with ZPE + TS corrections | temperature, reference, target |
md_analysis | RDF / MSD / barrier from trajectory | mode, pairs |
condition | If/else branching | expression |
loop | Iterate over a list | variable, values |
merge | Barrier / join branches | — |
batch call — just use a loop node with variable=layers and values=[3,4,5,6].catgo_workflow {action:"node_types"} to refresh the catalogue. Don't preemptively check.templates first to look up the template_id and pass it to create.One sentence. State the workflow name, node count, and that the workflow is open in the editor for inspection. Do not dump the operations array, the graph_json, or per-node parameter lists — the editor visualises all of that. Example:
Built "CO2RR on Cu(100)": 4 nodes, 3 edges. Opened in the Workflow tab — review and click ▶ Run when ready.
© Hello-QM, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in server/catgo/workflow/skills/workflow_builder of Hello-QM/catgo-LRG.
Open the folder on GitHubat commit fd6291b
Catgo Build Workflow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Catgo Build Workflow this skillHello-QM/catgo-LRG | 205 | — | ~3.1k | Automated safety check: Pass | AGPL-3.0 | |
| DAG Librarycode-yeongyu/oh-my-openagent | 70k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Nexrad Mosaic Constructionsickn33/agentic-awesome-skills | 47k | 1 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Agent Harness Constructionaffaan-m/ECC | 276k | 2 repos | ~222 | Automated safety check: Pass | MIT | |
| Agent Harness Constructionaffaan-m/ECC | 276k | — | ~297 | Automated safety check: Pass | MIT | |
| Airflow DAG Patternswshobson/agents | 40k | 9 repos | ~784 | Automated safety check: Pass | MIT |
code-yeongyu/oh-my-openagent
Stores a DAG definition once and re-runs it by name, with key rotation controlling whether each run is fresh or reuses an earlier result.
sickn33/agentic-awesome-skills
Construct a quality-aware NEXRAD multi-radar mosaic from aligned single-site products with explicit coverage, beam geometry, quality weighting, overlap resolution, and provenance.
affaan-m/ECC
设计和优化AI代理的动作空间、工具定义和观察格式,以提高完成率。
affaan-m/ECC
AI エージェントのアクション空間、ツール定義、観測フォーマットを設計・最適化して完了率を向上させます. An agent skill from affaan-m/ECC.
wshobson/agents
Patterns for writing production-ready Apache Airflow DAGs: task dependencies, custom operators and sensors, local testing, and rules for what to avoid.
tanweai/pua
Compact version of the PUA persona skill that pushes an agent to act like a high-ownership engineer, with level roles, extra-work markers and corporate-style commentary.
Hello-QM/catgo-LRG
Drive a file-first, agent-in-the-loop computational campaign via a folder + markdown tree (no DB).
Hello-QM/catgo-LRG
Compute adsorption/reaction Gibbs free energies, free-energy diagrams, and electrochemical overpotentials (HER/ORR/OER/CO2RR/NRR) with VASP.
Hello-QM/catgo-LRG
Generate and manage ABINIT DFT calculations. An agent skill from Hello-QM/catgo-LRG.
Hello-QM/catgo-LRG
A skill your agent uses when the user asks to place an adsorbate molecule on a surface, find adsorption sites, or set up a surface+adsorbate model for DFT.
Hello-QM/catgo-LRG
A skill your agent uses when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.
Hello-QM/catgo-LRG
A skill your agent uses when the user asks to analyze computational results: Gibbs free energy, OER/HER/CO2RR overpotentials, adsorption energy, convergence tests, DOS/d-band analysis, or Bader…
One-shot CatGo DAG workflow construction. An agent skill from Hello-QM/catgo-LRG. Catgo Build Workflow is an agent skill from Hello-QM/catgo-LRG. One-shot CatGo DAG workflow construction.
Catgo Build Workflow fits situations like: the user asks to create a workflow; build a pipeline; set up CO2RR/OER/HER/NEB/DOS/slow-growth; make a workflow for X reaction.
Run `npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a claude-code`. Or copy the skill folder (server/catgo/workflow/skills/workflow_builder in Hello-QM/catgo-LRG) into .claude/skills/catgo-build-workflow in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a codex`. Or copy the skill folder (server/catgo/workflow/skills/workflow_builder in Hello-QM/catgo-LRG) into .agents/skills/catgo-build-workflow in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Hello-QM/catgo-LRG --skill catgo-build-workflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/catgo-build-workflow, .gemini/skills/catgo-build-workflow, .github/skills/catgo-build-workflow and .opencode/skills/catgo-build-workflow in your project.
SKILL.md names no scripts, command-line tools or credentials: Catgo Build Workflow is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Catgo Build Workflow is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Catgo Build Workflow: DAG Library (code-yeongyu/oh-my-openagent, 70k stars), Nexrad Mosaic Construction (sickn33/agentic-awesome-skills, 47k stars), Agent Harness Construction (affaan-m/ECC, 276k stars) and Agent Harness Construction (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Hello-QM (a GitHub user) maintains it in Hello-QM/catgo-LRG, which has 205 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on September 22, 2026.
Source: Hello-QM/catgo-LRG on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.