# Workflow From Chats > Extract durable working preferences from recent Cursor chats and Use when this capability is needed. - Skill: `tomevault-io/workflow-from-chats` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/workflow-from-chats` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/workflow-from-chats/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/workflow-from-chats --- ## Pi Context-Aware Execution When this skill is invoked in Pi, treat the user's current request and any skill arguments as the task input. Do not treat this file as the task by itself. Before applying the skill, establish only the context needed for the request: 1. Identify the current working directory and relevant project scope. 2. Read local guidance first when present: AGENTS.md, CLAUDE.md, TODO.md, or nearby task/spec files. 3. Inspect the current codebase with targeted searches (prefer rg) and read relevant files before making claims or proposing changes. 4. Ground findings and recommendations in project evidence: cite file paths, commands, tests, docs, or external sources as applicable. 5. Ask a concise clarification only when the arguments and codebase context are insufficient to proceed safely. Operate conservatively: avoid broad scans, large reads, subagents, or parallel fanout unless the user's requested depth clearly requires them. # Workflow From Chats Infer durable working preferences from recent chats. Do not summarize chats; extract reusable workflow guidance. ## Scope - Default to the last 7 days unless the user asks for a different window. - Read parent transcripts and relevant subagent transcripts. Use subagent content as evidence, but cite only parent conversations. - Do not expose local transcript paths, secrets, customer data, private chat content, or credentials. ## Workflow 1. State the target workflow or preference surface in one paragraph. 2. Build an internal transcript inventory: title/topic, parent conversation ID, approximate date, completion state, relevant subagents, and why it may contain preference evidence. 3. Scan for explicit preferences, corrections, and workflow markers such as "I prefer", "always", "never", "not what I asked", "stop", "review", "PR", "CI", "logs", and "skill". 4. Extract preference atoms: trigger, workflow step, decision rule, quality bar, stop condition, evidence, and confidence. 5. Rate confidence as strong, medium, weak, or contradicted. 6. Cluster by workflow shape rather than transcript: shipping, review, simplification, debugging, capture, communication, delegation, or validation. 7. Choose the artifact: new skill, skill edit, rule, workflow doc, or no artifact. 8. Draft only the reusable guidance. Filter anecdotes that will not help future tasks. ## Confidence - Strong: explicit user preference, workflow-changing correction, repeated parent-chat pattern, or direct request to encode behavior. - Medium: accepted workflow, repeated tool/model/validation preference, or subagent consensus that the parent used successfully. - Weak: agent-chosen behavior with no user feedback, one ambiguous transcript, or a likely task-specific correction. - Contradicted: evidence points in incompatible directions; ask the user before writing files. ## Artifact Choice - Skill: recurring multi-step workflow with clear triggers. - Rule: general behavior that should apply broadly. - Workflow doc: useful context that is not reliably triggerable. - No artifact: situational, stale, or low-confidence observation. ## Output Return a concise synthesis first: - Target workflow. - Evidence corpus with parent conversation citations only. - Preference profile. - Adopt, consider, dismissed. - Proposed artifacts. - Open questions only if they block writing. --- > Source: [v1truv1us/ai-eng-system](https://github.com/v1truv1us/ai-eng-system) — distributed by [TomeVault](https://tomevault.io).