# Seed > Generate validated Seed specifications from interview results - Skill: `q00/seed` (Agent Skill) - Install (CLI): `npx skillmds@latest add q00/seed` - Raw SKILL.md: https://api.skillmd.com/api/skills/q00/seed/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: Q00 (https://skillmd.com/u/q00) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/q00/seed --- # /ouroboros:seed Generate validated Seed specifications from interview results. ## Required Skill Capabilities - `ask_user` — ask human-judgment questions through the active runtime's user-question surface. - `inspect_code` — read repo-local agent roles and recover exact context from local files before guessing. - `call_mcp` — use available Ouroboros MCP tools directly, including runtime tool discovery when a deferred MCP surface must be loaded. - `run_shell` — run bounded local commands for audit-trail writes and setup steps. - `refine_answer` — confirm free-form user decisions before treating them as accepted seed revisions. - `maintain_ledger` — keep QA scores, candidate decisions, rejected proposals, and audit trail keys visible. ## Usage ``` ooo seed [session_id] /ouroboros:seed [session_id] ``` **Trigger keywords:** "crystallize", "generate seed" ## Instructions When the user invokes this skill: ### Python Runtime (Required) Before running any shell snippet below, define this resolver in the same shell. It accepts only Python 3.12 or newer, prefers `python3` and then `python`, and uses uv as the final fallback. Call `ouroboros_python` directly and quote every argument passed to it; the function preserves arguments and heredoc/stdin input. Only the probe and child interpreter discard inherited CPython path-selection overrides; the caller shell keeps its environment unchanged. ```bash ouroboros_python() { if command -v python3 >/dev/null 2>&1 && (unset PYTHONHOME PYTHONPATH PYTHONPLATLIBDIR PYTHONEXECUTABLE __PYVENV_LAUNCHER__; command python3 -c 'import sys; raise SystemExit(sys.version_info < (3, 12))') >/dev/null 2>&1 then (unset PYTHONHOME PYTHONPATH PYTHONPLATLIBDIR PYTHONEXECUTABLE __PYVENV_LAUNCHER__; command python3 "$@") return fi if command -v python >/dev/null 2>&1 && (unset PYTHONHOME PYTHONPATH PYTHONPLATLIBDIR PYTHONEXECUTABLE __PYVENV_LAUNCHER__; command python -c 'import sys; raise SystemExit(sys.version_info < (3, 12))') >/dev/null 2>&1 then (unset PYTHONHOME PYTHONPATH PYTHONPLATLIBDIR PYTHONEXECUTABLE __PYVENV_LAUNCHER__; command python "$@") return fi if command -v uv >/dev/null 2>&1; then (unset PYTHONHOME PYTHONPATH PYTHONPLATLIBDIR PYTHONEXECUTABLE __PYVENV_LAUNCHER__; command uv run --no-project --quiet --python '>=3.12' python "$@") return fi printf '%s\n' 'Ouroboros skills require Python >= 3.12 or uv on PATH.' >&2 return 127 } ``` ### Load MCP Tools (Required before Path A/B decision) The Ouroboros MCP tools are often registered as **deferred tools** that must be explicitly loaded before use. **You MUST perform this step before deciding between Path A and Path B.** 1. Use the active runtime's `call_mcp` capability to find and load the seed generation MCP tool through runtime tool discovery when needed: ``` tool discovery query: "+ouroboros seed" ``` 2. The tool will typically be named `mcp__plugin_ouroboros_ouroboros__ouroboros_generate_seed` (with a plugin prefix). After runtime tool discovery returns, the tool becomes callable through the active runtime's `call_mcp` capability. 3. If the tool is callable — already exposed, or loaded by discovery — proceed to **Path A**. An empty discovery result for an already-exposed tool is expected, not a failure. Proceed to **Path B** only if the tool is genuinely absent (no Ouroboros MCP server). **IMPORTANT**: Do NOT skip this step. Do NOT assume MCP tools are unavailable just because they don't appear in your immediate tool list. They are almost always available as deferred tools that need to be loaded first. **CRITICAL — deferred-schema guard (prevents "Invalid tool parameters"):** This skill makes `ouroboros_*` MCP calls across multiple turns, and each turn runs in a fresh tool context. A deferred tool's schema loaded on one turn is NOT guaranteed to still be loaded on the next. If you call any `ouroboros_*` MCP tool while its schema is not loaded in the **current** turn, the runtime rejects the call with **"Invalid tool parameters"** before it ever reaches the server. Therefore: **immediately before EVERY `ouroboros_*` MCP call in this skill, re-run the tool-discovery load query for the specific MCP tool you are about to call** (idempotent — a no-op when the schema is already loaded) so the correct schema is guaranteed present for that call. Use `"+ouroboros seed"` before `ouroboros_generate_seed`, `"+ouroboros qa"` before `ouroboros_qa`, and `"+ouroboros lateral"` before `ouroboros_lateral_think`. If a load ever returns no matching tool (and the tool is not already callable — an empty load for an already-exposed tool is an expected no-op, not absence), switch to the documented fallback / Path B instead of retrying the failing call. ### Path A: MCP Mode (Preferred) If the `ouroboros_generate_seed` MCP tool is available (loaded via runtime tool discovery above): 1. Determine the interview session: - If `session_id` provided: Use it directly - If no session_id: Check conversation for a recent `ouroboros_interview` session ID - If none found but THIS conversation already settled the goal, the constraints, and verifiable success criteria (a lateral decision, a brownfield scan, or plain discussion that converged): take the **interview-less path** below. Do not send the user to `ooo interview` just to repeat what they already told you. - If none found and the material is not settled: Ask the user **Interview-less path** (`session_context`): ``` Tool: ouroboros_generate_seed Arguments: session_context: goal: acceptance_criteria: [] constraints: [] decisions: [] project_type: greenfield | brownfield ``` Every value enters the Seed byte-for-byte, so shell chains in an AC (`ruff check && pytest`) are expected and allowed. - If the response has `status: "gap_questions_required"`, it lists the exact 1-5 questions the Seed still needs. Ask the user those questions only, merge the answers into `session_context`, and call again. That is the whole interview: it shrinks to the gaps the session left open. - If the response contains Seed YAML, continue at step 3 with that YAML. 2. Call the MCP tool through the active runtime's `call_mcp` capability: ``` Tool: ouroboros_generate_seed Arguments: session_id: ``` 3. The tool extracts requirements from persisted interview state, calculates ambiguity score, and generates the Seed YAML. **Seed generation response shapes**: Branch only after an actual Seed YAML artifact is available. - If the response has `status: "delegated_to_subagent"` and `dispatch_mode: "plugin"`, keep the returned `session_id`, wait for the plugin-managed subagent result, then extract the Seed YAML from that result. Do not run the advisory QA check using the delegation envelope as the artifact. - If the response directly contains Seed YAML, extract that YAML directly. - If neither shape yields Seed YAML, stop and ask the user to resume generation or provide the missing artifact; do not fabricate a seed just to satisfy the advisory QA check. 4. Run the single-pass Advisory QA Check below, then present the seed as final and proceed to "After Seed Generation". Do not enter any refinement iteration unless the user explicitly opts in. **Advantages of MCP mode**: Automated ambiguity scoring (must be <= 0.2), structured extraction from persisted interview state, reproducible. ### Path B: Plugin Fallback (No MCP Server) If the MCP tool is NOT available, fall back to agent-based generation: 1. Read `src/ouroboros/agents/seed-architect.md` and adopt that role. 2. Recover the interview requirements before drafting; do not invent missing context: - If `session_id` was provided, first identify context for that same session: use current-thread interview Q&A only when it clearly belongs to that `session_id`, and use current-thread corrections only when they explicitly amend that same interview or seed request. - If same-session conversation context is incomplete, use the active runtime's `inspect_code` / `run_shell` capabilities to look for persisted interview artifacts under the Ouroboros data directory (for example `~/.ouroboros/data/`), exported session artifacts, or other exact local records for that ID. - If both same-session conversation context and a persisted artifact are available, merge them conservatively: keep the persisted transcript as evidence, but let explicit same-thread user corrections or clarifications supersede older persisted wording. - If no `session_id` was provided, use current-thread interview Q&A only when it is complete enough to identify one coherent interview; otherwise ask which interview or requirements summary should be seeded. - If no matching artifact is found, or if local artifacts plus matching conversation history still do not provide enough requirements, ask the user for the missing interview transcript / concise requirement summary, or ask them to run or resume `ooo interview`. Do not generate a seed from an absent or mismatched transcript. 3. Generate a Seed YAML specification from the recovered requirements. 4. Run the single-pass Advisory QA Check below, then present the seed as final and proceed to "After Seed Generation". Do not enter any refinement iteration unless the user explicitly opts in. ### Advisory QA Check (single pass, non-blocking) After Path A or Path B produces a seed, run QA **exactly once** and surface the verdict as advisory information. **The verdict never blocks.** The seed is presented as final regardless of score; the user decides whether any refinement is worth their time. Do not run QA-until-PASS iterations — that loop is retired because it front-loads heavy interaction the user did not ask for. The generation (Path A `ouroboros_generate_seed` or Path B agent role) runs **exactly once** and establishes the seed's ontology. Any later revision is a direct YAML edit by you (main session) — do not call `ouroboros_generate_seed` again. It does not accept revision hints, and re-running it would discard the established ontology. **Advisory bar**: `pass_threshold: 0.90` (stricter than default 0.80 — seeds are structural specs). The bar labels the verdict; it does not gate anything. **Check**: 1. Establish the QA evaluator for this run: - **MCP QA mode**: Load the QA tool via the active runtime's `call_mcp` capability using runtime tool discovery query `"+ouroboros qa"` if not already loaded. - **Fallback QA mode**: If MCP is unavailable, read `src/ouroboros/agents/qa-judge.md`, adopt that evaluator role, and return its exact JSON schema: lowercase `verdict` (`pass`/`revise`/`fail`), numeric `score`, `dimensions`, `differences`, `suggestions`, and `reasoning`. In this mode there is no MCP-owned `qa_session_id`; track iteration history in the audit block and local loop ledger instead. 2. Obtain a QA verdict using the available mode: **MCP QA mode** — call QA on the generated seed through the active runtime's `call_mcp` capability: ``` Tool: ouroboros_qa Arguments: artifact: quality_bar: "Seed must be internally consistent, acceptance_criteria must be measurable and testable, constraints must be concrete (no vague terms), ontology_schema must cover all entities referenced in goal/criteria, and there must be no contradictions between fields. acceptance_criteria must also be parsimonious in the ontological sense: a criterion names a state of the finished work a user can see is true, while an implementation step names a means of reaching it, and only the first belongs in the list. Read each criterion beside its siblings — one intelligible only as a move toward a sibling is that sibling's means and belongs merged into the outcome it serves, and flagging that is as important as flagging a missing piece, since it commits the seed to an unverified path. How many criteria a goal has follows from that judgment, so weigh each criterion against its siblings." artifact_type: "document" pass_threshold: 0.90 seed_content: qa_session_id: iteration_history: ``` **Fallback QA mode** — skip the tool call and evaluate the current seed text under the QA Judge role from step 1, using the same quality bar and threshold. Treat the locally produced verdict exactly like the MCP verdict for the advisory presentation below. **QA response shapes**: Branch only after a usable verdict is available. - In MCP QA mode, if the response has `status: "delegated_to_subagent"` and no verdict payload, keep the returned `qa_session_id`, wait for the plugin-managed subagent result, then parse that result as the QA verdict. Do not treat the delegation envelope itself as PASS/REVISE/FAIL. - In MCP QA mode, if the response already includes a scored verdict, parse that inline verdict directly. - In fallback QA mode, parse the exact QA Judge JSON. Normalize `verdict` to uppercase only for the labels below (`pass`→PASS, `revise`→REVISE, `fail`→FAIL). Treat `differences` and `suggestions` as advisory findings; do not add non-schema fields such as `loop_action`. - If the user later opts into a refinement pass, append the parsed verdict plus applied/rejected revision decisions to `iteration_history` before that next QA pass. 3. Present the advisory verdict and the final seed — always in this order, never gated on score: 1. One advisory line: `QA advisory: — score X.XX (bar 0.90).` 2. If the verdict is below the bar, list the top 2–3 QA suggestions as short advisory bullets — findings, not tasks. Do not apply any of them automatically. 3. Present the complete final Seed YAML in a fenced `yaml` block. 4. If the verdict was REVISE or FAIL, offer exactly one opt-in line — e.g. `Want a refinement pass on these findings? Otherwise the seed stands as-is.` For FAIL (< 0.40) additionally mention that `ooo interview` (revisit requirements) or `ooo unstuck` (challenge assumptions) may serve better than YAML edits. Then proceed to "After Seed Generation" regardless of the answer being pending — the seed is final unless the user opts in. 4. Only if the user explicitly opts in, run one **Wonder → Reflect → Refine → Restate** pass (below), re-run the QA check once on the revised seed for an updated advisory line, and present the revised YAML. Each additional pass requires a fresh explicit opt-in; never chain passes autonomously. #### Wonder → Reflect → Refine → Restate (opt-in refinement pass) This refinement pass mirrors the Double Diamond Define cycle: **diverge via multiple perspectives first, then converge through debate, user decision, and structural application.** Revisions must NEVER be auto-applied by the main session alone — *"No candidate is accepted by default."* (Symposium User Adoption Gate) Four explicit phases per pass: - **Wonder** — diverge: collect raw proposals from independent sources - **Reflect** — debate: surface where sources agree and where they conflict - **Refine** — user gate: human picks which proposals enter the next seed - **Restate** — apply: edit YAML in place with accepted items only **Phase 1 — Wonder (diverge): collect raw proposals from available sources** **Source 1 — QA Judge** (structural, external) The `suggestions` from the QA verdict. These are gaps, contradictions, and quality issues in the YAML itself. QA cannot see the interview. **Source 2 — Socrates** (dialectical, user-intent evidence) You are Socrates — the Socratic facilitator lens from `skills/interview/SKILL.md` and `src/ouroboros/agents/socratic-interviewer.md`. Review the current seed YAML against verifiable interview evidence, in this order: 1. If a `session_id` exists, first use available persisted interview/session state for that session. Path A may run from `ooo seed ` in a fresh conversation, so persisted state can be the only reliable dialectic record. 2. Use conversation memory when it is available in the current thread. 3. If no persisted state or conversation evidence is available for a point, mark Socrates output as `no Socrates-only proposal: dialectic context unavailable` for that point. Do not invent user preferences, rejected scope, or interview nuance. From the available evidence, surface 2–4 items neither QA nor lateral personas can see: - Did the user emphasize a constraint that got softened or dropped? - Did something the user explicitly rejected sneak back in? - Did the seed flatten nuance the user spent multiple turns clarifying? - Are there silent assumptions the user never agreed to? - Does wording contradict stated priorities (e.g., "MVP in a week" but 8 acceptance criteria)? If QA and Socrates conflict, do not resolve the conflict silently in Wonder. Carry both candidates into Reflect as a divergent signal, cite the available evidence for each side, and let the Refine user gate choose the resolution. Do not assume the Socratic lens is automatically authoritative; QA can be correct when no user-intent evidence contradicts it. **Source 3 — `ouroboros_lateral_think` (independent perspectives, MCP-only when available)** Attempt to load the MCP tool with the active runtime's `call_mcp` capability using runtime tool discovery query `"+ouroboros lateral"` if needed. If the tool loads, call it through the active runtime's `call_mcp` capability to collect 5 independent MCP personas or isolated perspectives: ``` Tool: ouroboros_lateral_think Arguments: problem_context: | User opted into a refinement pass (QA advisory score X.XX, bar 0.90). Current seed YAML: QA suggestions: - - Original user goal from interview: current_approach: "The seed as currently drafted (above)." persona: "all" failed_attempts: - - ... ``` The 5 personas return distinct revision angles: - **hacker**: unconventional workarounds (e.g., reframe a constraint instead of adding criteria) - **researcher**: knowledge the seed assumes but doesn't pin down - **simplifier**: criteria/constraints to *remove* for sharper convergence - **architect**: structural reorganization without expansion - **contrarian**: challenges to assumptions the seed treats as settled **Parsing persona outputs when lateral MCP is available**: Each persona returns free-form prose, not a structured list. After the parallel call returns, read each persona's text and extract its concrete proposals into discrete candidates (one revision per candidate, not bundled). If a persona's output is purely abstract advice with no actionable revision, drop it from the candidate list rather than inventing one. Aim for 1–2 candidates per persona — if a persona produced 5, pick the 2 most concrete and discard the rest. **Lateral response shapes**: `ouroboros_lateral_think` does not have one universal synchronous shape. After calling it with all personas, branch on the returned shape before extracting candidates: - **Plugin delegation**: If the response has `status: "delegated_to_subagent"`, `dispatch_mode: "plugin"`, and an `_subagents` array, wait for every plugin-managed subagent result. Extract concrete revision candidates from those returned persona texts. Do not attempt to parse candidates from the envelope prompts themselves. - **Inline fallback with dispatch block**: If the response returns markdown `content` plus the hidden sentinel ``, keep the visible markdown as the lateral scaffold. If the active runtime can dispatch isolated subagents, decode the sentinel JSON (`dispatch_mode`, `persona_count`, `payloads`) and send each `payload.prompt` + `payload.context` through that isolated subagent surface, then extract candidates from the returned persona texts. If the runtime cannot dispatch subagents, synthesize candidates directly from the visible inline persona sections. - **Inline fallback without dispatch block**: Treat the returned markdown as the complete lateral output and synthesize candidates directly from the visible persona sections. Do not split solely on `---` if doing so would corrupt user-provided content; prefer section headers and visible persona boundaries. If runtime tool discovery cannot load `ouroboros_lateral_think`, do not emulate lateral personas or read persona files directly. Record `no lateral proposals: MCP lateral tool unavailable` as Source 3 output and proceed with QA plus Socrates/available sources. The User Adoption Gate still applies to any proposed revision. **Phase 2 — Reflect (debate): structure proposals by agreement and conflict** Do not just dedupe. Read all proposals from the available Wonder sources (Sources 1–2, plus Source 3 only when `ouroboros_lateral_think` loaded successfully) and surface the *structure of the debate*: - **Convergent signals (strong)**: same revision proposed by ≥2 independent sources. Example: QA says "criterion 3 is unmeasurable" AND simplifier says "drop criterion 3 or sharpen it" → strong signal to act on criterion 3. - **Divergent signals (decisions)**: sources conflict. Example: researcher says "add User entity to ontology" but simplifier says "remove the User reference from goal — single-user implied". This is a decision the *user* must resolve, not the main session. - **Singleton signals (weaker)**: one source only. Keep but mark as weaker. - **Balance signal**: count expansion proposals (add) vs convergence proposals (sharpen/remove). Show the ratio above the user gate as information, not warning — e.g., `Balance: 4 expand / 2 sharpen / 1 remove`. Both directions are legitimate; the user decides what mix to accept. Output of Reflect: a tagged candidate list with per-item metadata `(sources_backing, type=expand|sharpen|remove|resolve_conflict)`. **Phase 3 — Refine (User Adoption Gate)** Use the active runtime's `ask_user` capability with executable single-choice questions only. Do not ask one multi-select question or present options that can be selected contradictorily. Ask sequential single-choice questions in this order: 1. For each conflict group, ask one question with exactly one option per mutually exclusive resolution plus "Leave unchanged"; handle the runtime's free-form "Other" response if available. Record the chosen option as accepted and mark the other options in that group rejected. 2. For non-conflicting convergent signals, ask one single-choice batch question: "Apply all strong non-conflicting revisions, review one by one, or skip them?" If the user chooses review, ask each revision as a Yes/No/Other single-choice question. 3. For singleton signals, ask one single-choice batch question: "Review singleton revisions one by one, skip all singleton revisions, or other?" If the user chooses review, ask each revision as a Yes/No/Other single-choice question. 4. Always include a skip option at the batch level: "None of the above / keep current seed for now". If selected, skip applying this candidate batch; the current seed simply stands as-is. Convergent signals still appear first in summaries, conflicts second, singletons last. Conflict questions must be asked before any non-conflicting batch is applied so contradictory revisions cannot both enter the next seed. ``` Refinement pass — QA advisory score X.XX Which revisions should enter the next seed? (Nothing accepted by default. Questions are single-choice and may be sequential.) Strong (multiple sources agree): A. [QA + Simplifier] Criterion 3 "easy to use" — sharpen to measurable predicate B. [QA + Socrates] Re-add "single-user only" constraint dropped from iter-0 Conflicts (mutually exclusive — pick at most one per group): C1. [Researcher] Add User entity to ontology C2. [Simplifier] Remove User reference from goal (single-user implied) C3. Neither — leave ontology untouched on this point Singletons: D. [Contrarian] Constraint "no external DB" contradicts criterion 7 E. [Architect] Group 3 user-management criteria under one parent F. [Hacker] Replace "user authentication" with "device-local key file" Other: G. None of the above (keep current seed) H. Other — describe a different change ``` Portable gate example: ```json { "questions": [{ "question": "Conflict: how should the seed handle User in the ontology?", "header": "Conflict C", "options": [ {"label": "Add User", "description": "Accept C1 and reject C2/C3"}, {"label": "Remove User", "description": "Accept C2 and reject C1/C3"}, {"label": "Leave unchanged", "description": "Accept C3 and reject C1/C2"} ], "multiSelect": false }] } ``` Balance line shown above the question: `Balance: 4 expand / 2 sharpen / 1 remove` (informational, not a warning). Track all rejected candidates across iterations and pass them as `failed_attempts` to subsequent `ouroboros_lateral_think` calls when the MCP lateral tool is available, so personas don't re-propose them. **Phase 4 — Restate (apply accepted only)** Edit the previous seed YAML in place. Apply ONLY user-accepted items. Do not start from scratch. Do not lose fields that were already correct. Do not call `ouroboros_generate_seed` again — that tool runs only at initial generation. If the user skips all proposed revisions, the current seed stands unchanged. After applying accepted items (or after a full skip), re-run the QA check once for an updated advisory line and present the complete Seed YAML in a fenced `yaml` block so the standing artifact is explicit. Do not start another pass without a fresh explicit opt-in. Common edit shapes (both expansion and convergence are legitimate when the user accepted them): - Sharpen: replace vague phrase with measurable predicate (`"fast"` → `"p95 latency < 200ms"`) - Tighten: harden a soft constraint (`"some kind of storage"` → `"SQLite, single file, no server"`) - Make implicit explicit: surface a silent assumption as a constraint - Remove: drop a contradicting or redundant criterion - Expand (when accepted): add an ontology entity, criterion, or constraint that fills a gap the user confirmed **Audit trail** After each revision, append a brief audit block to `~/.ouroboros/seed-revisions/.md` (create the directory if it doesn't exist) capturing: iteration N, QA score, all candidates with source tag, user's accept/reject decisions, and the resulting diff vs. previous iteration. This makes the convergence path inspectable and lets the user replay decisions later. Choose `revision_key` deterministically: - If `session_id` exists, use that exact `session_id`. - If no `session_id` exists (common in Path B), derive a stable seed label from the seed goal or project name plus the current UTC timestamp, for example `-YYYYMMDDTHHMMSSZ`. Once derived, reuse the same key for every iteration in the current seed run. - If the filesystem write is unavailable, include the same audit block in the assistant response instead of silently dropping it. Format: ```markdown ## Iteration N — score X.XX ### Candidates - [A] [QA+Simplifier] sharpen criterion 3 — **accepted** - [B] [Socrates] re-add single-user constraint — **accepted** - [C1] [Researcher] add User entity — rejected - [C2] [Simplifier] remove User from goal — **accepted** - [D] [Contrarian] resolve no-DB / criterion-7 conflict — rejected - ... ### Diff vs. iteration N-1 - criteria[2]: "easy to use" → "first-time user completes flow in < 3 clicks" - constraints: + "single-user only" - goal: "...for users..." → "...for the single operator..." ``` ## Seed Components The seed contains: - **GOAL**: Clear primary objective - **CONSTRAINTS**: Hard limitations (e.g., Python >= 3.12, no external DB) - **ACCEPTANCE_CRITERIA**: Measurable success criteria - **ONTOLOGY_SCHEMA**: Data structure definition (name, fields, types) - **EVALUATION_PRINCIPLES**: Quality principles with weights - **EXIT_CONDITIONS**: When the workflow should terminate - **METADATA**: Version, timestamp, ambiguity score, interview ID ## Example Output ```yaml goal: Build a CLI task management tool constraints: - Python >= 3.12 - No external database - SQLite for persistence acceptance_criteria: - Tasks can be created - Tasks can be listed - Tasks can be marked complete ontology_schema: name: TaskManager description: Task management domain model fields: - name: tasks type: array description: List of tasks - name: title type: string description: Task title evaluation_principles: - name: completeness description: All requirements are implemented weight: 1.0 - name: usability description: CLI commands are clear and easy to use weight: 0.7 exit_conditions: - name: all_criteria_met description: All acceptance criteria pass criteria: 100% of acceptance criteria are satisfied - name: tests_green description: The project test suite passes criteria: Required automated tests exit successfully metadata: ambiguity_score: 0.15 ``` ## After Seed Generation On successful seed generation, first announce: ``` Your seed has been crystallized! ``` Then check `~/.ouroboros/prefs.json` for `star_asked`. If `star_asked` is not set to `true`, use the active runtime's `ask_user` capability with this single question: ```json { "questions": [{ "question": "If Ouroboros helped clarify your thinking, a GitHub star supports continued development. Ready to unlock Full Mode?", "header": "Next step", "options": [ { "label": "\u2b50 Star & Setup", "description": "Star on GitHub + run ooo setup to enable run, evaluate, status" }, { "label": "Just Setup", "description": "Skip star, go straight to ooo setup for Full Mode" } ], "multiSelect": false }] } ``` - **Star & Setup**: Run `gh api -X PUT /user/starred/Q00/ouroboros`, merge `{"star_asked": true}` into `~/.ouroboros/prefs.json`, then read and execute `../setup/SKILL.md` - **Just Setup**: Merge `{"star_asked": true}` into `~/.ouroboros/prefs.json`, then read and execute `../setup/SKILL.md` - **Other** (user provides custom text): Merge `{"star_asked": true}` into `~/.ouroboros/prefs.json`, skip setup Create `~/.ouroboros/` directory if it doesn't exist. Preserve existing keys such as `welcomeShown`, `welcomeCompleted`, and `welcomeVersion` when updating `star_asked`: ```bash ouroboros_python - <<'PY' import json, os path = os.path.expanduser('~/.ouroboros/prefs.json') os.makedirs(os.path.dirname(path), exist_ok=True) try: with open(path, encoding='utf-8') as f: prefs = json.load(f) if not isinstance(prefs, dict): prefs = {} except Exception: prefs = {} prefs['star_asked'] = True with open(path, 'w', encoding='utf-8') as f: json.dump(prefs, f, indent=2) f.write('\n') PY ``` If `star_asked` is already `true`, skip the question and just announce: ``` Your seed has been crystallized! ◆ Current state → next: `ooo run` to execute this seed (requires `ooo setup` first) ``` ## RFC #1392 State Breadcrumb Footer Your final response MUST end with exactly one breadcrumb footer line: ``` ◆ → next: ``` Derive `` 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.