OpenStoryArc

Narrate the story of a project or topic from your session history — the key decisions and turning points, as a deterministic timeline grounded in real sessions and git. Use when the user asks "tell the story of <project>", "how did <X> come to be", "trace the arc of <topic>", "what's the history of this", or wants a narrative built from the record (not a vibe summary).

Prime yourself with prior OpenStory context before starting — pick up exactly where the last session on this project left off: what was done, what's open, the tools used. Use at the START of work, or when the user says "where did we leave off", "resume", "pick up where I left off", "catch up on this project", or "what was I doing here".

See who on your team has an active or recent OpenStory session and what each person is working on — live roster, per-teammate summaries, and recent focus. Requires federated sessions (multiple hosts/users reporting to one store). Use when the user asks "who's working right now", "what is <teammate> on", "team activity", "what did my team do today", or "sync me before our standup".

Show where your time actually goes across coding sessions — by project, by hour, and how focused vs scattered your work is. Distinct from cost (money) and tools (frequency). Use when the user asks "where does my time go", "how much time on X", "when am I most productive", "am I focused or scattered", or "time breakdown".

Show which tools and commands you rely on most across coding sessions — tool histogram, top shell commands, and Read:Write ratio. Frequency, not duration (that's time) or cost (that's cost). Use when the user asks "what tools do I use most", "my top commands", "what do I reach for", "how often do I edit vs read", or "tool breakdown".

Watch the work happening on a branch or project through OpenStory and summarize it live as it streams — a real-time feed of tools and edits plus a rolling summary of what the work is converging on. Use when the user says "watch this branch", "what's happening on <branch> right now", "stream the work", "follow along live", "show me activity as it happens", or "tail the agent on <project>".

Coach yourself — honest feedback on how you work with coding agents: prompt quality, recurring failure patterns, and what your work has been pointing at. From your own OpenStory store. Use when the user asks "how am I doing", "feedback on my prompting", "where do I get stuck", "what am I focused on lately", or "coach me".

Report what your AI coding sessions have cost — total spend, cache savings, and a tokens-per-day timeline — from your own OpenStory store. Use when the user asks "what did this cost", "my agent spend", "how much have I spent", "tokens per day", or anything about token usage or cost of coding sessions.

Use when asked to give a narrated/driven tour of the OpenStory dashboard, tell a "user story" from session data, demo features by driving the UI, tune the design live with voice narration, or record a tour video. Drives the UI through the control seam while narrating aloud.

Query your own coding sessions via the OpenStory MCP tools — find past work, understand what happened, check token usage, search across history, and watch live sessions stream. All read-only observability, never interferes with your execution.

Run OpenStory's automated red-team — dependency scanners, aggressive in-process tests, and testcontainer-based exploit probes — then report which attack vectors were fended off and which broke through. Use when the user asks "audit security", "run a red team", "are we secure", "what attacks would work", or before merging anything that touches auth, the HTTP surface, or dependencies. Dogfoods the project's own tooling — never invent ad-hoc checks when the runner exists.

Tell the story of a team's work on a given day by running the deterministic team_day pipeline, reading the resulting facts sheet, and composing a short narrative on top. Use when the user asks "what did our team do today / yesterday / on date X", "summarize the team's day", "what did Max and Katie work on", or wants a per-author day report grounded in OpenStory data. Dogfoods OpenStory — never guess from the conversation, always run the pipeline first.

Query OpenStory for a structural report on an agent session — at-a-glance metrics, turn arc, file impact, errors, and optional event detail. Use when you have a session ID and need to know what happened in it without re-reading raw transcripts.

Validate the project's docs against the live codebase. Catches drift between claims (in markdown prose) and reality (in Cargo.toml workspace members, source files, the filesystem). Use before committing docs changes, or whenever you're about to write something architectural and want to be sure you're not perpetuating a stale claim. The validator surfaces things internal consistency cannot catch — when every doc agrees with every other doc but none agree with the build.

Tell the story of an OpenStory session by collecting deterministic facts (records, tools, patterns, prompts) via the REST API, then narrating them. Use when the user asks "what happened in session X", "summarize this session", "what was that session about", or wants to understand a past or current session through OpenStory's own data. Dogfoods OpenStory — never grep transcript files when you can ask the API.