Agent skill

Auto

by Q00 in Q00/ouroboros

Automatically converge from goal to A-grade Seed and execute it

MITAuto-check: warningsAgent Workflows

Install Auto

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add Q00/ouroboros --skill auto -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Q00/ouroboros auto --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Q00/ouroboros.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/auto .claude/skills/auto && rm -rf skills-src

Use ~/.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/

Facts

Skill name
auto
GitHub stars
6.2k
Token cost
~5.3k tokens
SKILL.md length
2,619 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Automatically converge from goal to A-grade Seed and execute it

  • Works in 6 steps: Starts an auto session. → Runs bounded Socratic interview rounds… → Generates a Seed. → …
  • Agent Workflows work in your project
  • SKILL.md covers Dispatch requirement, Usage, CLI flag → MCP arg translation and Behavior, plus 3 more sections
  • Calls cursor

What it does

Auto is an agent skill from Q00/ouroboros. Automatically converge from goal to A-grade Seed and execute it

Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. The repository describes itself as: Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI… The licence is MIT.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/auto”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Starts an auto session.
  2. Runs bounded Socratic interview rounds with source-tagged auto answers.
  3. Generates a Seed.
  4. Reviews and repairs until A-grade or blocked.
  5. Starts execution only after A-grade.
  6. Auto reaches COMPLETE as soon as the run has a durable handle. The run keeps going as a background job, and that job carries its own run →…

What it can do on your machine

Read from SKILL.md and the folder at commit 0df5b98. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • cursor

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Auto loads about 5.3k tokens when it runs. Until then it costs about 17 tokens; SKILL.md has 2,619 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~17
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k

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.

Safety

Auto-check: warnings

The automated check found patterns that need a careful read before installing.

  • WarningContains instruction-override wording (e.g. “without asking the user”)SKILL.md:226
    status), keep waiting. Do not tell the user to call job tools themselves.

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.

SKILL.md

The full file from Q00/ouroboros at commit 0df5b98, republished under its MIT licence (© Q00). 2,619 words, ~5,289 tokens.

Download SKILL.mdSave it as .claude/skills/auto/SKILL.md (or your agent's skills folder).
name
auto
description
Automatically converge from goal to A-grade Seed and execute it
mcp_tool
ouroboros_start_auto
mcp_args.goal
$goal
mcp_args.resume
$resume
mcp_args.cwd
$CWD
mcp_args.max_interview_rounds
$max_interview_rounds
mcp_args.max_repair_rounds
$max_repair_rounds
mcp_args.skip_run
$skip_run
mcp_args.pipeline_timeout_seconds
$pipeline_timeout_seconds
mcp_args.efficiency_mode
$efficiency_mode
mcp_args.frugality_assurance
$frugality_assurance

/ouroboros:auto

Run the full-quality auto pipeline from a single task description.

Dispatch requirement

This skill must be executed by invoking MCP tool ouroboros_start_auto. Do not manually inspect repositories, run shell commands, query GitHub, edit files, or otherwise emulate the auto pipeline as a substitute. Full auto runs routinely exceed interactive MCP tool-call timeouts, so the background starter is the supported default: it returns job_id and auto_session_id quickly. Retain both. When response.meta.job_observer is present, delegate its read-only wait/result contract to exactly one independent child session. The main session keeps only start and explicit on-demand status responsibility.

If ouroboros_start_auto is unavailable, or if any required job polling/result MCP tool is unavailable, stop and report that the required MCP tool is unavailable. A manual fallback is not an ooo auto run.

If a started auto job later returns detached, blocked, failed, or another auto-session status, report that auto-session status and the tool's blocker. detached is non-terminal tracked background work; surface the job/Ralph handles and keep observing them through the same owner. Do not label a blocked or failed outcome as MCP dispatch failure; dispatch failure means the MCP tool could not be invoked.

If the active runtime routes ooo auto through a background starter such as ouroboros_start_auto, do not stop after returning the job_id. Keep ownership of the conversational UX: retain the returned job_id, auto_session_id, and cursor, then delegate monitoring when the host supports child sessions. Only the fallback path monitors with ouroboros_job_wait / ouroboros_job_status in the main session. The user should not have to poll the job manually.

Usage

text
ooo auto "Build a local-first habit tracker CLI"
ooo auto --resume auto_abc123
ooo auto "Build a local-first habit tracker CLI" --skip-run
/ouroboros:auto "Build a local-first habit tracker CLI"

ooo auto does not accept a parent Seed ID/path or infer lineage from goal prose. Mentioning an existing Seed in the goal is ordinary context, not an authorized derivative operation. Use ooo evolve for typed parent evolution, or ooo run to execute an existing immutable Seed. Do not claim preserved lineage or AC edit scope from an ooo auto goal alone.

CLI flag → MCP arg translation

When the user types ooo auto with CLI-style flags inside chat, translate to MCP arguments before invoking ouroboros_start_auto:

CLI flagMCP argType
--skip-runskip_run=trueboolean
--max-interview-rounds Nmax_interview_rounds=Ninteger
--max-repair-rounds Nmax_repair_rounds=Ninteger
--pipeline-timeout-seconds Xpipeline_timeout_seconds=Xnumber
--efficiency-mode adaptive|quality_firstefficiency_mode=<value>string
--frugality-assurance off|observe|strictfrugality_assurance=<value>string
--resume <id>resume=<id>string

--max-generations is not a flag for ooo auto; it belongs to ooo ralph. The chained Ralph started by the run job is bounded by execution.auto_evolve_max_generations.

--complete-product is deprecated and ignored: the run job owns run → evaluate → ralph, so a single Auto invocation no longer drives Ralph itself. Follow the run job's chain with ooo status or the job tools.

--pipeline-timeout-seconds is accepted only when starting a session. Passing it with --resume is rejected because the original deadline is preserved across process restarts.

Before a fresh Auto start, if the user did not already choose an efficiency policy, first check the persistent default: when execution.default_policy in ~/.ouroboros/config.yaml is efficient or quality_first, do not ask — omit both arguments and the server applies the configured default (the handoff still reports the resolved policy). Otherwise ask in outcome language: Efficient execution maps to adaptive/observe; Quality-first execution maps to quality_first/off. strict assurance is a separate explicit opt-in because it may spend extra work on proof. Never infer strict from the efficiency choice. On resume, do not ask or send either argument; Auto restores the persisted contract.

Behavior

  1. Starts an auto session.
  2. Runs bounded Socratic interview rounds with source-tagged auto answers.
  3. Generates a Seed.
  4. Reviews and repairs until A-grade or blocked.
  5. Starts execution only after A-grade.
  6. Auto reaches COMPLETE as soon as the run has a durable handle. The run keeps going as a background job, and that job carries its own run → evaluate → ralph chain governed by execution.auto_evaluate / execution.auto_evolve (both default true, Ralph bounded by execution.auto_evolve_max_generations). Auto does not evaluate the run itself; the chained evaluate job does.
Preflight blockers and recovery

seed_preflight_unexecutable is a start-new-session boundary, not a plain --resume boundary. The persisted Seed artifact is immutable for that session, so answering an open question cannot silently replace the artifact. Use the displayed questions to revise the goal/Seed contract, then start a new ooo auto session; the blocked session remains available for audit.

Transient interview, evaluator, and lateral-tool exhaustion is different: its resume capability is durable and can be retried with the same session after the external dependency or provider is healthy again. Do not treat a seed_preflight_unexecutable status as a retryable provider outage.

Background monitoring UX

When an auto start response includes response.meta.job_id:

  1. Briefly acknowledge that auto started and keep the handles in local state: job_id, auto_session_id / session_id, and cursor from response.meta if present. Show response.meta.dashboard_url when available; otherwise mention ouroboros tui open once as the live view. Tell the user that an observer will post meaningful progress/attention/completion events here and that this conversation remains available for requirement refinement, read-only inspection/review, explicit control, or unrelated isolated work. Include the resolved runtime_backend, llm_backend, efficiency_mode, and frugality_assurance when present. Say that the exact active model and first parallel level will be announced from the first configuration/plan events rather than guessing them.
  2. If MCP metadata is unavailable, recover job_observer from the final <!-- ouroboros-job-observer-v1 base64 ... --> content sentinel. Fail closed unless the bounded payload passes canonical v1 validation and its job/session identity matches the visible start receipt. Visible IDs are identity anchors, not a source for reconstructing tools or arguments. Reject validation failure or any mismatch between structured and inline surfaces.
  3. If the structured or recovered job_observer is present and the host supports independent child sessions, spawn exactly one read-only observer and pass the contract unchanged. Codex uses spawn_agent, OMP uses one native Task child, and Claude Code uses one Task/Agent child. The observer owns the cursor, waits until terminal, fetches the result, and follows downstream IDs named by follow_result_job_keys. It must not edit files, control execution, or spawn implementation workers. The main session must not poll the same job. On Codex, call spawn_agent exactly once with task_name="run_observer"; a wait call is not a spawn, and the handoff may claim an observer only after the spawn result returns a live child ID/path. Once acknowledged, keep the parent turn open with wait_agent calls of at most 60 seconds while the child remains active. A child send_message only queues a mailbox event; it cannot revive an ended parent turn. Relay meaningful updates and wait again until the observer sends its terminal summary. On OMP, submit exactly one native Task child named RunObserver, require its live agent/job ID, and use the host wait/inbox relay until terminal. User input may interrupt the wait; handle it and resume waiting while observation remains active unless the user asks to stop live observation or replaces the active request. Then end only the relay loop, keep the durable job running, and offer next-turn or explicit- status catch-up. If the observer child fails, is cancelled, or exits before a terminal summary, use that same fallback instead of waiting indefinitely. This parent relay loop must not call job wait/result or take cursor ownership. If spawn fails, do not promise live proactive relays. The detached worker continues after the stdio turn; say that durable progress will be caught up on the next parent turn or an explicit status request. Keep the current turn open in the fallback polling loop only when the user asked for live watching.
  4. If child-to-parent progress messages are supported, the observer relays only meaningful phase_changed, progress_advanced, attention_required, and terminal events in at most 1-2 lines. During interview, it may use ouroboros_session_status(session_id=<auto_session_id>) to surface a new pending question or newly answered rounds. Otherwise, keep the main session available and use on-demand status only when the user asks. Surface attention_required immediately when a blocker needs human judgment. Suppress unchanged heartbeats and raw tool output. Interpret execution relays explicitly: run_configuration reports current runtime/harness/model policy; execution_plan reports total ACs, total dependency/parallel levels, and first scheduled ACs; discovery_summary reports bounded targets and purpose; level/routing/harness/verified subtypes report only material changes. Never relay raw commands or reasoning. Before the main session writes to the active auto workspace, check for overlap with worker files or move the unrelated work to an isolated worktree.
  5. When no independent child session exists and the user explicitly asks to keep watching in this turn, enter the fallback low-noise loop. Otherwise end the turn safely and resume from the durable cursor on the next interaction:
    • ouroboros_job_wait(job_id=<job_id>, cursor=<cursor>, timeout_seconds=120, view="summary", stream="linked", wait_for="attention_or_ac_change")
    • update cursor = response.meta.cursor after every wait/status response
    • treat response.meta as the source of truth; use response text only as a human-readable hint
  6. Relay only meaningful changes: status changes, phase changes, new execution/session/lineage handles, progress counters, blocker/error text, or a terminal state. If response.meta.changed is false, continue silently unless the user asked for heartbeat updates. Synapse delivery events are meaningful: distinguish queued/delivering from applied/completed, and surface rejected/delivery_uncertain immediately. Render the relay in the user's current conversation language; preserve raw event codes only when exact diagnostics help. When the user adds implementation intent during an executing auto run, the main session first reloads Synapse schemas with tool discovery query: "+ouroboros session signal", then calls ouroboros_session_signal_targets with the observed execution_id, selects the semantically matching AC from ac_content and current activity, then sends ouroboros_session_signal with that target's exact IDs. Never ask the user for internal IDs. Ask a short clarification only when multiple live ACs remain genuinely tied, and never route shared goal/AC/ constraint changes to one worker. Send additive implementation refinements with exact target guards, contract_effect="additive", source="user", mode="redirect", and explicit fallback_mode="after_turn". Use mode="inform" for a read-only AC question or assurance request, omit fallback_mode entirely in that mode, and relay the bounded reply from the completed event.
  7. During the interview phase, surface the live Q&A — not just the round counter. Whenever the relayed phase is interview (e.g. progress reads interview round N/50), call ouroboros_session_status(session_id=<auto_session_id>) and relay to the user: (a) the current meta.pending_question (the question the interview is asking right now), and (b) the meta.auto_answer_log entries — each is {round, source, question, answer}, i.e. what the auto-answerer answered and why (source: conservative_default = safe-default policy, inference = model reasoning, assumption = auto-answerer fallback). Show this so the user sees what the interview is converging on, not a bare counter. Note: this Q&A lives in the auto-session state, so session_status surfaces it even though ouroboros_query_events returns nothing for the auto session, and it shows only the last 3 answers (each truncated). Keep it low-noise: relay the pending question and any newly answered rounds, not the same 3 entries every poll.
  8. If the job status is non-terminal (queued, running, or another active status), keep waiting. Do not tell the user to call job tools themselves.
  9. When the job reaches a terminal status, the polling owner calls ouroboros_job_result(job_id) and summarize the final auto-session outcome. If the final auto result is detached, keep tracking the surfaced downstream job/Ralph handles when available instead of presenting detached as completion.
  10. If response.meta.status == "delegated_to_plugin" and response.meta.job_id is None, report that OpenCode plugin mode delegated the work to the child Task/session. Do not call job wait/result without a real job id; follow the host Task widget/session lifecycle.

Use short progress relays; the goal is “I am still watching this for you,” not a wall of logs.

Show full SKILL.md (761 more words)Show less

Active Conductor decision policy

English is the canonical instruction language; render facts naturally in the user's current conversation language.

For attention_required, treat recommended_host_actions as authoritative:

  1. VERIFY with at most one short-lived read-only host child. If unavailable, surface the evidence and stop before mutation.
  2. DECIDE from the ordered menu only after engine ownership is closed.
  3. LOG selected with ouroboros_record_conductor_decision before ACT.
  4. ACT only a menu-listed registered tool. Auto may start at most one bounded deterministic, non-relaxing successor for that attention event and must pass the audited directive/decision/predecessor receipts exactly.
  5. LOG exactly one terminal completed, failed, or declined result. Do not silently retry. Any specification-changing proposal is escalated to the user; Auto never relaxes the approved goal, ACs, constraints, or non-goals itself.
Canonical stop_reason_code taxonomy
LayerCodeSurfaceMeaning
Interviewinterview_max_rounds_exhaustedlast_error_code, result.stop_reason_codeAuto interview ran max_interview_rounds without ledger+backend mutual closure, no section was safely defaultable, and no partial defaults applied — i.e. genuine deadlock with nothing the policy could close.
Interviewinterview_unsafe_gaps_remainlast_error_code, result.stop_reason_codeAuto interview ran max_interview_rounds with at least one section safely defaultable and at least one section remaining unsafe (e.g. CONFLICTING ledger entry, production/credential context). Partial defaults are rolled back so the persisted transcript and ledger stay aligned; resume can address the unsafe gap and re-run.
Interviewinterview_phase_deadlinelast_error_code, result.stop_reason_codeInterview phase exceeded its per-phase timeout.
Ralphiteration_timeoutblocker text + (future) result.stop_reason_codeA single Ralph iteration exceeded its per-iteration timeout.
Ralphwall_clock_exhaustedblocker text + (future) result.stop_reason_codeThe Ralph wall-clock budget was exhausted before convergence.
Ralphoscillation_detectedblocker text + (future) result.stop_reason_codeRalph oscillated between two grade states without making progress.
Ralphgrade_regressingblocker text + (future) result.stop_reason_codeA subsequent Ralph generation produced a strictly worse grade than its predecessor.
Ralphmax_generations reachedblocker text + (future) result.stop_reason_codeRalph hit its configured generation cap before reaching A grade.

Blockers without a canonical code keep using the free-form last_error text. Ralph-layer codes are surfaced via blocker text today; their result-envelope promotion is tracked as a follow-up.

Interview closure mode taxonomy

When result.status == "seed_ready", result.interview_closure_mode distinguishes how the interview was closed:

ValueMeaning
NoneMutual agreement — both the backend and the ledger declared the seed ready in the same round. The default healthy path.
"ledger_only"PR-B1 / #1148: max_rounds hit; the ledger was structurally complete but the backend refused to declare closure. The interview closes on ledger-only consensus. Defaulted sections (if any) are tagged in result.defaulted_sections.
"safe_default"PR-B2: max_rounds hit; the safe-default policy successfully filled every remaining required gap with auditable assumptions. Synthesis was pushed back into the persisted transcript so the seed generator sees the same assumptions the ledger records. Defaulted sections are tagged in result.defaulted_sections.

Genuine-deadlock and partial-unsafe outcomes do not set interview_closure_mode; they reach a blocked terminal with the matching stop_reason_code above instead.

Assumption-source provenance (PR-C2 / #1157)

result.assumptions: tuple[str, ...] (the existing list of assumption texts) is now accompanied by result.assumption_sources: tuple[AssumptionRecord, ...], where each AssumptionRecord is a frozen dataclass with:

FieldTypeMeaning
textstrThe assumption text (same surface as the corresponding assumptions entry where present).
sourcestrOne of "assumption" (auto-answerer fallback), "inference" (model reasoning), "conservative_default" (safe-default policy). These are the three assumption-class LedgerSource values that produce assumption_only_sections.
confidencefloatPer-entry confidence as recorded by the ledger.

assumption_sources is a broader surface than assumptions — it includes inference- and conservative-default-class entries that assumptions (filtered to LedgerSource.ASSUMPTION only) does not surface. Callers wanting to know which assumptions the system made on the user's behalf should read assumption_sources; callers preserving the older string-only contract continue to read assumptions.

The pipeline must not hang indefinitely: all loops are bounded and timeout failures return a resumable auto_session_id. Resume with ooo auto --resume <auto_session_id>. Use --skip-run to stop after the A-grade Seed. --complete-product is deprecated and ignored: the run job owns run → evaluate → ralph. The chained Ralph is bounded by execution.auto_evolve_max_generations (default 3) generations, each capped by Ralph's per-iteration timeout — it is finite, but there is no single total wall-clock cap, because the chain dispatch passes no max_total_seconds. The auto session's --timeout no longer bounds it. The CLI-only --show-ledger flag prints assumptions/non-goals; MCP skill responses already include the same ledger summary when available.

Your final response MUST end with exactly one breadcrumb footer line:

◆ <current state> → next: <recommended action>

Derive <current state> from live session state via ouroboros_session_status when that MCP projection is available; otherwise derive it from this skill's actual outcome. Never use a linear Step N of M footer because Ouroboros is an evolutionary loop. When the next action is genuinely a choice, list 2-3 honest options in the next: clause. The breadcrumb line must be the last line of the response.

© Q00, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/auto of Q00/ouroboros.

Open the folder on GitHubat commit 0df5b98

Compare with similar skills

Auto 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.

Auto compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Auto this skillQ00/ouroboros6.2k—~5.3kAutomated safety check: WarnMIT
MCP Server Builderanthropics/skills180k62 repos~2.3kAutomated safety check: PassApache-2.0
Hook Development for Claude Code Pluginsanthropics/claude-plugins-official37k11 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k34 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers296k2 repos~5.1kAutomated safety check: PassMIT
Claude Code Agent Developmentanthropics/claude-plugins-official37k8 repos~2.8kAutomated safety check: PassApache-2.0

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Categories

Questions about Auto

What does Auto do?

Automatically converge from goal to A-grade Seed and execute it. Auto is an agent skill from Q00/ouroboros.

When should I use Auto?

Auto fits situations like: agent Workflows work in your project.

How do I install Auto in Claude Code?

Run `npx skills add Q00/ouroboros --skill auto -a claude-code`. Or copy the skill folder (skills/auto in Q00/ouroboros) into .claude/skills/auto in your project. Claude Code loads it when a task matches its description.

How do I install Auto in Codex?

Run `npx skills add Q00/ouroboros --skill auto -a codex`. Or copy the skill folder (skills/auto in Q00/ouroboros) into .agents/skills/auto in your project. Codex loads it when a task matches its description.

Can I use Auto in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Q00/ouroboros --skill auto -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto, .gemini/skills/auto, .github/skills/auto and .opencode/skills/auto in your project.

What does Auto need to run?

Going by SKILL.md and its folder, Auto needs the command-line tools its instructions call (cursor).

Does Auto access the network?

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.

Is Auto safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): contains instruction-override wording (e.g. “without asking the user”). Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Auto use?

Auto is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Auto use?

About 5.3k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Auto?

Skills that share tags, products or a category with Auto: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 37k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 296k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Auto?

Q00 (a GitHub user) maintains it in Q00/ouroboros, which has 6,189 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 6, 2026.

Source: Q00/ouroboros on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.