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.
git clone --depth 1 https://github.com/abstractonion/cadenceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/abstractonion/cadence/workflow-from-chats)<a href="https://agentmods.dev/commands/abstractonion/cadence/workflow-from-chats"><img src="https://agentmods.dev/badge/commands/abstractonion/cadence/workflow-from-chats.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00027 | $0.00241 |
| Opus 5 | $0.00014 | $0.00120 |
| Sonnet 5 | $0.00005 | $0.00048 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
workflow-from-chats scanned grade A with 0 findings 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 7d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Workflow From Chats
You are mining recent Cursor chats for durable working preferences this turn. Default to the last 7 days unless the user names a window. Do not summarize chats — extract reusable workflow guidance. Scan parent transcripts (use subagent content as internal evidence only; cite parent conversations). Look for explicit preferences, corrections, and markers like "always", "never", "stop", "review", "CI", "skill". Cluster by workflow shape: shipping, review, debugging, delegation, validation. Rate each atom strong, medium, weak, or contradicted. Apply Cadence's promotion flywheel: rule for broad always-on behavior, skill for recurring multi-step workflows, command for a single-turn mode, subagent for fresh-context delegation — or no artifact if situational. Return preference profile, adopt / consider / dismiss lists, and proposed Cadence artifacts. Do not write files without explicit approval.
Anchored in
- capture-learnings
- promote-recurring-learnings
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.
- 7d ago First seen · 15 lines · 27 tokens per session scan A e18a1b55efde
workflow-from-chats is a command published in the GitHub repository abstractonion/cadence (4 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 241 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.