القائمة
Socratic deep interview with mathematical ambiguity gating before autonomous execution
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/tranhieutt/software_development_department --skill deep-interviewيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Turns real agent failures, repeated prompts, team-specific workflows, and durable project lessons into better SDD skills or memory entries. Use when the user asks to create/update/refine skills, extract reusable lessons, improve skill routing, encode team process, or save patterns for future sessions.
Provides the vendored agent-style v0.3.5 prose rule pack as a portable Claude skill. Use when installing, syncing, applying, or auditing SDD Agent-Style support in another project, or when a style-review workflow needs the bundled rules.
Reviews Markdown prose against the pinned agent-style v0.3.5 rules and optionally writes a polished copy beside the source. Use for specs, ADRs, PR bodies, release notes, and technical docs when prose quality or evidence discipline matters.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | deep-interview |
| description | Socratic deep interview with mathematical ambiguity gating before autonomous execution |
| argument-hint | [--quick|--standard|--deep] [--autoresearch] <idea or vague description> |
| pipeline | ["deep-interview","omc-plan","autopilot"] |
| next-skill | omc-plan |
| next-skill-args | --consensus --direct |
| handoff | .omc/specs/deep-interview-{slug}.md |
| level | 3 |
| effort | 3 |
<Use_When>
<Do_Not_Use_When>
omc-plan skill instead<Why_This_Exists> AI can build anything. The hard part is knowing what to build. OMC's autopilot Phase 0 expands ideas into specs via analyst + architect, but this single-pass approach struggles with genuinely vague inputs. It asks "what do you want?" instead of "what are you assuming?" Deep Interview applies Socratic methodology to iteratively expose assumptions and mathematically gate readiness, ensuring the AI has genuine clarity before spending execution cycles.
Inspired by the Ouroboros project which demonstrated that specification quality is the primary bottleneck in AI-assisted development. </Why_This_Exists>
<Execution_Policy>
explore agent BEFORE asking the user about them<Autoresearch_Mode>
When arguments include --autoresearch, Deep Interview becomes the zero-learning-curve setup lane for omc autoresearch.
omc-plan, autopilot, ralph, or team. Instead run:
omc autoresearch --mission "<mission>" --eval "<evaluator>" [--keep-policy <policy>] [--slug <slug>]{{ARGUMENTS}}explore agent (haiku): check if cwd has existing source code, package files, or git historyexplore agent to map relevant codebase areas, store as codebase_contextstate_write(mode="deep-interview"):{
"active": true,
"current_phase": "deep-interview",
"state": {
"interview_id": "<uuid>",
"type": "greenfield|brownfield",
"initial_idea": "<user input>",
"rounds": [],
"current_ambiguity": 1.0,
"threshold": 0.2,
"codebase_context": null,
"challenge_modes_used": [],
"ontology_snapshots": []
}
}
Starting deep interview. I'll ask targeted questions to understand your idea thoroughly before building anything. After each answer, I'll show your clarity score. We'll proceed to execution once ambiguity drops below 20%.
Your idea: "{initial_idea}" Project type: {greenfield|brownfield} Current ambiguity: 100% (we haven't started yet)
Repeat until ambiguity ≤ threshold OR user exits early:
Build the question generation prompt with:
Question targeting strategy:
Question styles by dimension:
| Dimension | Question Style | Example |
|---|---|---|
| Goal Clarity | "What exactly happens when...?" | "When you say 'manage tasks', what specific action does a user take first?" |
| Constraint Clarity | "What are the boundaries?" | "Should this work offline, or is internet connectivity assumed?" |
| Success Criteria | "How do we know it works?" | "If I showed you the finished product, what would make you say 'yes, that's it'?" |
| Context Clarity (brownfield) | "How does this fit?" | "I found JWT auth middleware in src/auth/ (pattern: passport + JWT). Should this feature extend that path or intentionally diverge from it?" |
| Scope-fuzzy / ontology stress | "What IS the core thing here?" | "You have named Tasks, Projects, and Workspaces across the last rounds. Which one is the core entity, and which are supporting views or containers?" |
Use AskUserQuestion with the generated question. Present it clearly with the current ambiguity context:
Round {n} | Targeting: {weakest_dimension} | Why now: {one_sentence_targeting_rationale} | Ambiguity: {score}%
{question}
Options should include contextually relevant choices plus free-text.
After receiving the user's answer, score clarity across all dimensions.
Scoring prompt (use opus model, temperature 0.1 for consistency):
Given the following interview transcript for a {greenfield|brownfield} project, score clarity on each dimension from 0.0 to 1.0:
Original idea: {idea}
Transcript:
{all rounds Q&A}
Score each dimension:
1. Goal Clarity (0.0-1.0): Is the primary objective unambiguous? Can you state it in one sentence without qualifiers? Can you name the key entities (nouns) and their relationships (verbs) without ambiguity?
2. Constraint Clarity (0.0-1.0): Are the boundaries, limitations, and non-goals clear?
3. Success Criteria Clarity (0.0-1.0): Could you write a test that verifies success? Are acceptance criteria concrete?
{4. Context Clarity (0.0-1.0): [brownfield only] Do we understand the existing system well enough to modify it safely? Do the identified entities map cleanly to existing codebase structures?}
For each dimension provide:
- score: float (0.0-1.0)
- justification: one sentence explaining the score
- gap: what's still unclear (if score < 0.9)
Also identify:
- weakest_dimension: the single lowest-confidence dimension this round
- weakest_dimension_rationale: one sentence explaining why it is the highest-leverage target for the next question
5. Ontology Extraction: Identify all key entities (nouns) discussed in the transcript.
{If round > 1, inject: "Previous round's entities: {prior_entities_json from state.ontology_snapshots[-1]}. REUSE these entity names where the concept is the same. Only introduce new names for genuinely new concepts."}
For each entity provide:
- name: string (the entity name, e.g., "User", "Order", "PaymentMethod")
- type: string (e.g., "core domain", "supporting", "external system")
- fields: string[] (key attributes mentioned)
- relationships: string[] (e.g., "User has many Orders")
Respond as JSON. Include an additional "ontology" key containing the entities array alongside the dimension scores.
Calculate ambiguity:
Greenfield: ambiguity = 1 - (goal × 0.40 + constraints × 0.30 + criteria × 0.30)
Brownfield: ambiguity = 1 - (goal × 0.35 + constraints × 0.25 + criteria × 0.25 + context × 0.15)
Calculate ontology stability:
Round 1 special case: For the first round, skip stability comparison. All entities are "new". Set stability_ratio = N/A. If any round produces zero entities, set stability_ratio = N/A (avoids division by zero).
For rounds 2+, compare with the previous round's entity list:
stable_entities: entities present in both rounds with the same namechanged_entities: entities with different names but the same type AND >50% field overlap (treated as renamed, not new+removed)new_entities: entities in this round not matched by name or fuzzy-match to any previous entityremoved_entities: entities in the previous round not matched to any current entitystability_ratio: (stable + changed) / total_entities (0.0 to 1.0, where 1.0 = fully converged)This formula counts renamed entities (changed) toward stability. Renamed entities indicate the concept persists even if the name shifted — this is convergence, not instability. Two entities with different names but the same type and >50% field overlap should be classified as "changed" (renamed), not as one removed and one added.
Show your work: Before reporting stability numbers, briefly list which entities were matched (by name or fuzzy) and which are new/removed. This lets the user sanity-check the matching.
Store the ontology snapshot (entities + stability_ratio + matching_reasoning) in state.ontology_snapshots[].
After scoring, show the user their progress:
Round {n} complete.
| Dimension | Score | Weight | Weighted | Gap |
|-----------|-------|--------|----------|-----|
| Goal | {s} | {w} | {s*w} | {gap or "Clear"} |
| Constraints | {s} | {w} | {s*w} | {gap or "Clear"} |
| Success Criteria | {s} | {w} | {s*w} | {gap or "Clear"} |
| Context (brownfield) | {s} | {w} | {s*w} | {gap or "Clear"} |
| **Ambiguity** | | | **{score}%** | |
**Ontology:** {entity_count} entities | Stability: {stability_ratio} | New: {new} | Changed: {changed} | Stable: {stable}
**Next target:** {weakest_dimension} — {weakest_dimension_rationale}
{score <= threshold ? "Clarity threshold met! Ready to proceed." : "Focusing next question on: {weakest_dimension}"}
Update interview state with the new round and scores via state_write.
At specific round thresholds, shift the questioning perspective:
Inject into the question generation prompt:
You are now in CONTRARIAN mode. Your next question should challenge the user's core assumption. Ask "What if the opposite were true?" or "What if this constraint doesn't actually exist?" The goal is to test whether the user's framing is correct or just habitual.
Inject into the question generation prompt:
You are now in SIMPLIFIER mode. Your next question should probe whether complexity can be removed. Ask "What's the simplest version that would still be valuable?" or "Which of these constraints are actually necessary vs. assumed?" The goal is to find the minimal viable specification.
Inject into the question generation prompt:
You are now in ONTOLOGIST mode. The ambiguity is still high after 8 rounds, suggesting we may be addressing symptoms rather than the core problem. The tracked entities so far are: {current_entities_summary from latest ontology snapshot}. Ask "What IS this, really?" or "Looking at these entities, which one is the CORE concept and which are just supporting?" The goal is to find the essence by examining the ontology.
Challenge modes are used ONCE each, then return to normal Socratic questioning. Track which modes have been used in state.
When ambiguity ≤ threshold (or hard cap / early exit):
.omc/specs/deep-interview-{slug}.mdSpec structure:
# Deep Interview Spec: {title}
## Metadata
- Interview ID: {uuid}
- Rounds: {count}
- Final Ambiguity Score: {score}%
- Type: greenfield | brownfield
- Generated: {timestamp}
- Threshold: {threshold}
- Status: {PASSED | BELOW_THRESHOLD_EARLY_EXIT}
## Clarity Breakdown
| Dimension | Score | Weight | Weighted |
|-----------|-------|--------|----------|
| Goal Clarity | {s} | {w} | {s*w} |
| Constraint Clarity | {s} | {w} | {s*w} |
| Success Criteria | {s} | {w} | {s*w} |
| Context Clarity | {s} | {w} | {s*w} |
| **Total Clarity** | | | **{total}** |
| **Ambiguity** | | | **{1-total}** |
## Goal
{crystal-clear goal statement derived from interview}
## Constraints
- {constraint 1}
- {constraint 2}
- ...
## Non-Goals
- {explicitly excluded scope 1}
- {explicitly excluded scope 2}
## Acceptance Criteria
- [ ] {testable criterion 1}
- [ ] {testable criterion 2}
- [ ] {testable criterion 3}
- ...
## Assumptions Exposed & Resolved
| Assumption | Challenge | Resolution |
|------------|-----------|------------|
| {assumption} | {how it was questioned} | {what was decided} |
## Technical Context
{brownfield: relevant codebase findings from explore agent}
{greenfield: technology choices and constraints}
## Ontology (Key Entities)
{Fill from the FINAL round's ontology extraction, not just crystallization-time generation}
| Entity | Type | Fields | Relationships |
|--------|------|--------|---------------|
| {entity.name} | {entity.type} | {entity.fields} | {entity.relationships} |
## Ontology Convergence
{Show how entities stabilized across interview rounds using data from ontology_snapshots in state}
| Round | Entity Count | New | Changed | Stable | Stability Ratio |
|-------|-------------|-----|---------|--------|----------------|
| 1 | {n} | {n} | - | - | - |
| 2 | {n} | {new} | {changed} | {stable} | {ratio}% |
| ... | ... | ... | ... | ... | ... |
| {final} | {n} | {new} | {changed} | {stable} | {ratio}% |
## Interview Transcript
<details>
<summary>Full Q&A ({n} rounds)</summary>
### Round 1
**Q:** {question}
**A:** {answer}
**Ambiguity:** {score}% (Goal: {g}, Constraints: {c}, Criteria: {cr})
...
</details>
Autoresearch override: if --autoresearch is active, skip the standard execution options below. The only valid bridge is the direct omc autoresearch --mission ... --eval ... handoff described above.
After the spec is written, present execution options via AskUserQuestion:
Question: "Your spec is ready (ambiguity: {score}%). How would you like to proceed?"
Options:
Ralplan → Autopilot (Recommended)
Skill("oh-my-claudecode:omc-plan") with --consensus --direct flags and the spec file path as context. The --direct flag skips the omc-plan skill's interview phase (the deep interview already gathered requirements), while --consensus triggers the Planner/Architect/Critic loop. When consensus completes and produces a plan in .omc/plans/, invoke Skill("oh-my-claudecode:autopilot") with the consensus plan as Phase 0+1 output — autopilot skips both Expansion and Planning, starting directly at Phase 2 (Execution).deep-interview spec → omc-plan --consensus --direct → autopilot executionExecute with autopilot (skip ralplan)
Skill("oh-my-claudecode:autopilot") with the spec file path as context. The spec replaces autopilot's Phase 0 — autopilot starts at Phase 1 (Planning).Execute with ralph
Skill("oh-my-claudecode:ralph") with the spec file path as the task definition.Execute with team
Skill("oh-my-claudecode:team") with the spec file path as the shared plan.Refine further
IMPORTANT: On execution selection, MUST invoke the chosen skill via Skill(). Do NOT implement directly. The deep-interview agent is a requirements agent, not an execution agent.
Stage 1: Deep Interview Stage 2: Ralplan Stage 3: Autopilot
┌─────────────────────┐ ┌───────────────────────────┐ ┌──────────────────────┐
│ Socratic Q&A │ │ Planner creates plan │ │ Phase 2: Execution │
│ Ambiguity scoring │───>│ Architect reviews │───>│ Phase 3: QA cycling │
│ Challenge agents │ │ Critic validates │ │ Phase 4: Validation │
│ Spec crystallization│ │ Loop until consensus │ │ Phase 5: Cleanup │
│ Gate: ≤20% ambiguity│ │ ADR + RALPLAN-DR summary │ │ │
└─────────────────────┘ └───────────────────────────┘ └──────────────────────┘
Output: spec.md Output: consensus-plan.md Output: working code
Why 3 stages? Each stage provides a different quality gate:
Skipping any stage is possible but reduces quality assurance:
<Tool_Usage>
AskUserQuestion for each interview question — provides clickable UI with contextual optionsTask(subagent_type="oh-my-claudecode:explore", model="haiku") for brownfield codebase exploration (run BEFORE asking user about codebase)state_write / state_read for interview state persistenceWrite tool to save the final spec to .omc/specs/Skill() to bridge to execution modes — never implement directlyQuestion: "I found JWT authentication with passport.js in src/auth/ (pattern match from explore).
For this new feature, should we extend the existing auth middleware or create
a separate authentication flow?"
Why good: Explored first, cited the repo evidence that triggered the question, then asked an informed confirmation question. Never asks the user what the code already reveals.
</Good>
<Good>
Contrarian mode activation:
Round 5 | Contrarian Mode | Ambiguity: 42%
You've said this needs to support 10,000 concurrent users. What if it only needed to handle 100? Would the architecture change fundamentally, or is the 10K number an assumption rather than a measured requirement?
Why good: Challenges a specific assumption (scale requirement) that could dramatically simplify the solution.
</Good>
<Good>
Early exit with warning:
User: "That's enough, just build it" System: "Current ambiguity is 35% (threshold: 20%). Areas still unclear:
Proceeding may require rework. Continue anyway?" [Yes, proceed] [Ask 2-3 more questions] [Cancel]
Why good: Respects user's desire to stop but transparently shows the risk.
</Good>
<Good>
Ontology convergence tracking:
Round 3 entities: User, Task, Project (stability: N/A → 67%) Round 4 entities: User, Task, Project, Tag (stability: 75% — 3 stable, 1 new) Round 5 entities: User, Task, Project, Tag (stability: 100% — all 4 stable)
"Ontology has converged — the same 4 entities appeared in 2 consecutive rounds with no changes. The domain model is stable."
Why good: Shows entity tracking across rounds with visible convergence. Stability ratio increases as the domain model solidifies, giving mathematical evidence that the interview is converging on a stable understanding.
</Good>
<Good>
Ontology-style question for scope-fuzzy tasks:
Round 6 | Targeting: Goal Clarity | Why now: the core entity is still unstable across rounds, so feature questions would compound ambiguity | Ambiguity: 38%
"Across the last rounds you've described this as a workflow, an inbox, and a planner. Which one is the core thing this product IS, and which ones are supporting metaphors or views?"
Why good: Uses ontology-style questioning to stabilize the core noun before drilling into features, which is the right move when the scope is fuzzy rather than merely incomplete.
</Good>
<Bad>
Batching multiple questions:
"What's the target audience? And what tech stack? And how should auth work? Also, what's the deployment target?"
Why bad: Four questions at once — causes shallow answers and makes scoring inaccurate.
</Bad>
<Bad>
Asking about codebase facts:
"What database does your project use?"
Why bad: Should have spawned explore agent to find this. Never ask the user what the code already tells you.
</Bad>
<Bad>
Proceeding despite high ambiguity:
"Ambiguity is at 45% but we've done 5 rounds, so let's start building."
Why bad: 45% ambiguity means nearly half the requirements are unclear. The mathematical gate exists to prevent exactly this.
</Bad>
</Examples>
<Escalation_And_Stop_Conditions>
- **Hard cap at 20 rounds**: Proceed with whatever clarity exists, noting the risk
- **Soft warning at 10 rounds**: Offer to continue or proceed
- **Early exit (round 3+)**: Allow with warning if ambiguity > threshold
- **User says "stop", "cancel", "abort"**: Stop immediately, save state for resume
- **Ambiguity stalls** (same score +-0.05 for 3 rounds): Activate Ontologist mode to reframe
- **All dimensions at 0.9+**: Skip to spec generation even if not at round minimum
- **Codebase exploration fails**: Proceed as greenfield, note the limitation
</Escalation_And_Stop_Conditions>
<Final_Checklist>
- [ ] Interview completed (ambiguity ≤ threshold OR user chose early exit)
- [ ] Ambiguity score displayed after every round
- [ ] Every round explicitly names the weakest dimension and why it is the next target
- [ ] Challenge agents activated at correct thresholds (round 4, 6, 8)
- [ ] Spec file written to `.omc/specs/deep-interview-{slug}.md`
- [ ] Spec includes: goal, constraints, acceptance criteria, clarity breakdown, transcript
- [ ] Execution bridge presented via AskUserQuestion
- [ ] Selected execution mode invoked via Skill() (never direct implementation)
- [ ] If 3-stage pipeline selected: omc-plan --consensus --direct invoked, then autopilot with consensus plan
- [ ] State cleaned up after execution handoff
- [ ] Brownfield confirmation questions cite repo evidence (file/path/pattern) before asking the user to decide
- [ ] Scope-fuzzy tasks can trigger ontology-style questioning to stabilize the core entity before feature elaboration
- [ ] Per-round ambiguity report includes Ontology row with entity count and stability ratio
- [ ] Spec includes Ontology (Key Entities) table and Ontology Convergence section
</Final_Checklist>
<Advanced>
## Configuration
Optional settings in `.claude/settings.json`:
```json
{
"omc": {
"deepInterview": {
"ambiguityThreshold": 0.2,
"maxRounds": 20,
"softWarningRounds": 10,
"minRoundsBeforeExit": 3,
"enableChallengeAgents": true,
"autoExecuteOnComplete": false,
"defaultExecutionMode": "autopilot",
"scoringModel": "opus"
}
}
}
If interrupted, run /deep-interview again. The skill reads state from .omc/state/deep-interview-state.json and resumes from the last completed round.
When autopilot receives a vague input (no file paths, function names, or concrete anchors), it can redirect to deep-interview:
User: "autopilot build me a thing"
Autopilot: "Your request is quite open-ended. Would you like to run a deep interview first to clarify requirements?"
[Yes, interview first] [No, expand directly]
If the user chooses interview, autopilot invokes /deep-interview. When the interview completes and the user selects "Execute with autopilot", the spec becomes Phase 0 output and autopilot continues from Phase 1 (Planning).
The recommended execution path chains three quality gates:
/deep-interview "vague idea"
→ Socratic Q&A until ambiguity ≤ 20%
→ Spec written to .omc/specs/deep-interview-{slug}.md
→ User selects "Ralplan → Autopilot"
→ /omc-plan --consensus --direct (spec as input, skip interview)
→ Planner creates implementation plan from spec
→ Architect reviews for architectural soundness
→ Critic validates quality and testability
→ Loop until consensus (max 5 iterations)
→ Consensus plan written to .omc/plans/
→ /autopilot (plan as input, skip Phase 0+1)
→ Phase 2: Parallel execution via Ralph + Ultrawork
→ Phase 3: QA cycling until tests pass
→ Phase 4: Multi-perspective validation
→ Phase 5: Cleanup
The omc-plan skill receives the spec with --consensus --direct flags because the deep interview already did the requirements gathering. The --direct flag (supported by the omc-plan skill, which ralplan aliases) skips the interview phase and goes straight to Planner → Architect → Critic consensus. The consensus plan includes:
Autopilot receives the ralplan consensus plan and skips both Phase 0 (Expansion) and Phase 1 (Planning) since ralplan already produced a Critic-approved plan. Autopilot starts directly at Phase 2 (Execution).
The ralplan pre-execution gate already redirects vague prompts to planning. Deep interview can serve as an alternative redirect target for prompts that are too vague even for ralplan:
Vague prompt → ralplan gate → deep-interview (if extremely vague) → ralplan (with clear spec) → autopilot
| Dimension | Greenfield | Brownfield |
|---|---|---|
| Goal Clarity | 40% | 35% |
| Constraint Clarity | 30% | 25% |
| Success Criteria | 30% | 25% |
| Context Clarity | N/A | 15% |
Brownfield adds Context Clarity because modifying existing code safely requires understanding the system being changed.
| Mode | Activates | Purpose | Prompt Injection |
|---|---|---|---|
| Contrarian | Round 4+ | Challenge assumptions | "What if the opposite were true?" |
| Simplifier | Round 6+ | Remove complexity | "What's the simplest version?" |
| Ontologist | Round 8+ (if ambiguity > 0.3) | Find essence | "What IS this, really?" |
Each mode is used exactly once, then normal Socratic questioning resumes. Modes are tracked in state to prevent repetition.
| Score Range | Meaning | Action |
|---|---|---|
| 0.0 - 0.1 | Crystal clear | Proceed immediately |
| 0.1 - 0.2 | Clear enough | Proceed (default threshold) |
| 0.2 - 0.4 | Some gaps | Continue interviewing |
| 0.4 - 0.6 | Significant gaps | Focus on weakest dimensions |
| 0.6 - 0.8 | Very unclear | May need reframing (Ontologist) |
| 0.8 - 1.0 | Almost nothing known | Early stages, keep going |
Task: {{ARGUMENTS}}