Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/yorrick/agent-skills/workflowgit clone --depth 1 https://github.com/yorrick/agent-skillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00018 | $0.06819 |
| Opus 5 | $0.00009 | $0.03410 |
| Sonnet 5 | $0.00004 | $0.01364 |
| Haiku 4.5 | $0.00002 | $0.00682 |
Grade A, and why
workflow scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 593 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Generator
You generate and execute ad-hoc workflow scripts using the StateGraph engine.
The user's request is: $ARGUMENTS
Engine location
The workflow engine is at: ${CLAUDE_PLUGIN_ROOT}/scripts/engine.py
What to do
- Read CLAUDE.md and list quality gates. Before anything else, read
CLAUDE.md(orAGENTS.md,GEMINI.md) in the repo root. If it exists, extract every quality gate command it mentions (e.g.,uv run ruff check .,uv run pyright,npx playwright test,npm run build). Write them down — you will need to add a workflow node for each one. - Understand the request. Read relevant source files to understand the codebase context.
- Design the workflow. Decide which nodes, edges, and routers are needed. Pick the right node type for each step. For every quality gate from step 1, add a dedicated
shell_node(or include the command in an LLM node prompt). If CLAUDE.md says "run ruff check", there must be a node that runs ruff. If it says "run pyright", there must be a node for pyright. No exceptions. Look for parallelization opportunities — independent quality gates can run in parallel viaadd_parallel_edges. - Write the script. Create a Python script at
/tmp/workflow_NNNN.py(use a random 4-digit suffix). Always include--diagramflag handling (see template). - Show the diagram first. Run with
uv run /tmp/workflow_NNNN.py --diagramand show the user the rendered ASCII diagram so they can see the workflow graph before execution. The script template already usesgraph.to_ascii()for this — do NOT change it toto_mermaid(). The ASCII version renders a visual box-and-arrow diagram directly in the terminal. - Run it. Execute with
uv run /tmp/workflow_NNNN.py. - Report the result. Show the user what happened.
Script template
Every generated script follows this structure:
#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.12"
# dependencies = ["mermaid-ascii"]
# ///
import os
import sys
sys.path.insert(0, "${CLAUDE_PLUGIN_ROOT}/scripts")
import asyncio
from engine import (
StateGraph, claude_node, codex_node, gemini_node,
shell_node, python_node, template_node,
detect_available_models, END,
)
def build_graph(models: dict[str, bool] | None = None) -> StateGraph:
"""Build the workflow graph. Accepts optional models dict for testing."""
if models is None:
models = detect_available_models()
HAS_CODEX = models["codex"]
HAS_GEMINI = models["gemini"]
graph = StateGraph(max_iterations=5)
# ... define nodes and edges ...
# Use codex_node/gemini_node when available and appropriate,
# fall back to claude_node otherwise. See model selection guide below.
return graph
if __name__ == "__main__":
graph = build_graph()
if "--diagram" in sys.argv:
print(graph.to_ascii())
sys.exit(0)
initial_state = {"work_dir": os.getcwd()}
asyncio.run(graph.run(initial_state))
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 593 lines · 18 tokens per session scan A 033be14ad543
workflow is a command published in the GitHub repository yorrick/agent-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 6,819 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.