discover who matters for your decision and what you need to learn about them before engaging
You describe a decision or initiative, the AI helps you identify relevant stakeholder groups, maps what you already know (and what you do not), and generates targeted Deep Research prompts to fill your knowledge gaps. The output is a self-contained Stakeholder Map you carry into the next recipe.
Stakeholder Discovery Engine
TL;DR
How To Start
STEP 1Describe Your Decision
-
topic_description
· text · required
The decision, initiative, policy change, or topic requiring stakeholder analysis. More detail produces better stakeholder candidates. -
user_role
· text · optional
Your role relative to the decision: decision-maker, advocate, analyst, implementer, or other. Helps calibrate which stakeholders matter most. -
skip_deep_research
· boolean · optional · default false
Set to true if you already have strong stakeholder knowledge. The recipe will still produce a Stakeholder Map but will skip Deep Research prompt generation. -
known_stakeholders
· text · optional
Any stakeholders you already know are relevant, as a starting point for discovery.
STEP 2Answer Scoping Questions
STEP 3Review Stakeholder Candidates
STEP 4Share What You Know
STEP 5Confirm Persona Count
STEP 6Receive Your Stakeholder Map
Usage Examples
How AI Reads This Recipe
- SCOPE the decision by asking targeted questions about decision type, stakes, timeline, user role, and organizational context. Skip questions the user has already answered.
- PROPOSE stakeholder candidates organized by category with context-specific relevance explanations. Frame proposals as “worth considering,” not assertions.
- WAIT for user confirmation before including any stakeholder on the map. Every stakeholder must be explicitly confirmed.
- MAP knowledge by probing each stakeholder across five dimensions: relationship to decision, known position, known concerns, priority areas, and influence level. Accept brief answers and mark unknowns as knowledge gaps.
- ASSESS complexity and recommend a persona count (3–6). Explain the rationale and recommend prioritization if more stakeholders are confirmed than the ceiling allows.
- GENERATE targeted Deep Research prompts calibrated to knowledge gap density. Each prompt must name a specific stakeholder, specify information type, and provide enough context for effective searching.
- DELIVER a self-contained Stakeholder Map readable without conversation context, structured for attachment to the Stakeholder Persona Builder (RCP-081).
When to Use This Recipe
- Need to understand who is affected by or can influence a decision before you engage them.
- Are preparing for a major initiative with multiple stakeholders and want to map the landscape first.
- Want to identify knowledge gaps about stakeholder positions and generate targeted research prompts to fill them.
- Plan to run the full Stakeholder Analysis Workflow (080 → 081 → 082 → 083) for structured stakeholder engagement preparation.
- Need to determine how many stakeholder personas to build for a discussion simulation.
Recipe FAQ
Q.What if I have more than six stakeholders?
Q.Can I skip Deep Research if I already know my stakeholders well?
Q.Does the AI make up stakeholder opinions?
Q.How is this different from the Customer Persona Fundamentals recipe (RCP-055)?
Q.What do I do with the Deep Research prompts?
Q.Can I use this recipe without running the rest of the workflow?
Version History
THE ACTUAL RECIPE
RCP-000-000-080-STAKEHOLDER-DISCOVERY-ENGINE
The CRAFT Recipe
# BEHAVIORAL RULES
# ===========================================================
#
# These rules govern AI behavior throughout this recipe.
# They are written in plain language so any AI platform
# can follow them. These rules override any conflicting
# tendencies in the AI’s default behavior.
#
# RULE 1 — NO FABRICATION OF STAKEHOLDER KNOWLEDGE:
# NEVER invent stakeholder concerns, positions, priorities,
# or sentiments. The AI may PROPOSE stakeholder categories
# based on the topic context (e.g., “HR is typically involved
# in workforce policy decisions”), but NEVER fabricate what
# specific stakeholders think, want, or fear. If the user
# hasn’t provided information about a stakeholder’s position,
# mark it as a KNOWLEDGE GAP, not an assumption.
#
# RULE 2 — USER CONFIRMS ALL STAKEHOLDERS:
# Every stakeholder on the final map must be explicitly
# confirmed by the user. The AI proposes candidates; the
# user decides who belongs. Never add a stakeholder to the
# map without user confirmation. Never remove a stakeholder
# the user wants to keep, even if the AI considers them
# low-relevance.
#
# RULE 3 — HONEST KNOWLEDGE GAP IDENTIFICATION:
# When the user doesn’t know a stakeholder’s position or
# concerns, acknowledge the gap directly. Do not fill it
# with plausible-sounding guesses. Instead, generate a
# targeted DR prompt designed to surface that specific
# information. Knowledge gaps are valuable findings, not
# failures.
#
# RULE 4 — APPROPRIATE CANDIDATE SOURCING:
# When proposing stakeholder candidates, draw from the
# topic context the user has provided, general knowledge
# of organizational structures, and common stakeholder
# patterns for the type of decision described. Frame
# proposals as “typically involved” or “worth considering”
# rather than “your stakeholders are.” The user’s domain
# knowledge always supersedes the AI’s general patterns.
#
# RULE 5 — COMPLEXITY-APPROPRIATE SCOPING:
# After stakeholder discovery, assess topic complexity
# and recommend a persona count between 3 and 6. The
# ceiling is 6 — if more stakeholders are genuinely
# relevant, recommend the user run multiple focused
# simulations rather than overloading one. Provide
# rationale for the recommended count. User confirms
# or overrides.
#
# RULE 6 — DR PROMPT QUALITY:
# Deep Research prompts must be specific enough to surface
# actionable stakeholder intelligence. Each prompt should
# target a specific knowledge gap, name the stakeholder
# group, and specify what type of information would be
# most useful (positions, concerns, precedents, regulatory
# context, historical behavior). Avoid vague prompts like
# “research stakeholder X” — instead, ask questions like
# “What positions have [industry group] organizations
# publicly taken on [specific policy]?”
#
# RULE 7 — AI-DETERMINED DR PROMPT COUNT:
# The AI determines how many DR prompts are needed based
# on the knowledge gap assessment. More gaps = more prompts.
# Simple topics with well-known stakeholders may need 2-3
# prompts. Complex multi-sector topics with significant
# unknowns may need 8-12. The AI explains its reasoning
# for the prompt count. User can request more or fewer.
#
# RULE 8 — WORKFLOW CONTEXT PRESERVATION:
# The Stakeholder Map output must be structured as a
# self-contained text document that can be attached to
# the RCP-081 chat session. It must include: the original
# topic/decision description, all confirmed stakeholders
# with their known positions and knowledge gaps, the
# recommended persona count, and the DR prompts. A reader
# encountering this document without the conversation
# context should understand the full scope.
#
# RULE 9 — NO FALSE AUTHORITY:
# Do not present stakeholder mapping as equivalent to
# professional stakeholder engagement, political analysis,
# or organizational consulting. This tool helps PREPARE
# for real stakeholder engagement — it does not replace it.
# Frame all outputs as “preparation for real-world
# engagement” rather than “stakeholder analysis complete.”
#
# RULE 10 — ONE PHASE AT A TIME:
# Do not rush through phases. Complete scoping before
# proposing candidates. Complete candidate confirmation
# before mapping knowledge. Complete knowledge mapping
# before generating DR prompts. Each phase builds on
# confirmed output from the previous phase.
#
# RULE 11 — WHEN YOU DO NOT KNOW:
# If the user describes an industry, organization, or
# context the AI has limited knowledge of, say so. Do
# not invent plausible-sounding stakeholder categories
# for unfamiliar domains. Instead, ask the user to
# identify the key groups, and the AI will help structure
# and map them.
#
# RULE 12 — SKIP DR PATH:
# If the user indicates they want to skip Deep Research
# (they already know their stakeholders well), adjust
# the output accordingly. The Stakeholder Map is still
# produced, but DR prompts are replaced with a note
# indicating the user is proceeding with existing
# knowledge. Flag any significant knowledge gaps the
# user should be aware of even without DR.
#
# =========================================================== # ===========================================================
# RECIPE PARAMETERS
# =========================================================== RECIPE_PARAMETERS = {
“topic_description”: {
“type”: “string”,
“required”: True,
“description”: (
“The decision, initiative, policy change, or “
“topic requiring stakeholder analysis. More “
“detail produces better stakeholder candidates.”
),
“example”: (
“Our company (200-person SaaS startup) is “
“considering adopting a 4-day work week as “
“a permanent policy change.”
)
},
“user_role”: {
“type”: “string”,
“required”: False,
“default”: “”,
“description”: (
“The user’s role relative to the decision: “
“decision-maker, advocate, analyst, implementer, “
“or other. Helps calibrate stakeholder mapping.”
),
“example”: “VP of People Operations, decision advocate”
},
“skip_deep_research”: {
“type”: “boolean”,
“required”: False,
“default”: False,
“description”: (
“Set to True if the user already has strong “
“stakeholder knowledge and wants to proceed “
“directly to RCP-081 without DR. The recipe “
“will still produce a Stakeholder Map but will “
“skip DR prompt generation.”
)
},
“known_stakeholders”: {
“type”: “string”,
“required”: False,
“default”: “”,
“description”: (
“Any stakeholders the user already knows are “
“relevant. Provided as a starting point — the “
“discovery process may identify additional groups.”
),
“example”: (
“CEO (supportive), CFO (concerned about costs), “
“Engineering leads (enthusiastic), Clients “
“(worried about availability)”
)
}
} # ===========================================================
# PHASE 1: TOPIC SCOPING
# =========================================================== #AI->H::RecipeActive: (RCP-080 Stakeholder Discovery Engine v3.00a)
#AI->H::Status: (Phase 1 — Topic Scoping) # Present scoping questions based on {topic_description} SCOPING_QUESTIONS = {
“decision_type”: (
“What exactly is being decided? (Policy change, “
“new initiative, strategic pivot, investment, “
“organizational restructure, or something else?)”
),
“stakes”: (
“What’s at stake? What could go well, and what “
“could go wrong? (Consider financial, operational, “
“cultural, competitive, and reputational dimensions.)”
),
“timeline”: (
“What’s the timeline? Are you exploring, committed, “
“or already implementing? When would decisions “
“need to be finalized?”
),
“user_position”: (
“What’s your role in this? (Decision-maker, “
“advocate, analyst, implementer, advisor?) “
“Who has final authority?”
),
“organizational_context”: (
“Brief organizational context: size, industry, “
“culture, any relevant history with similar “
“decisions?”
)
} # Present scoping questions to user
# If {user_role} is provided, acknowledge it and adjust
# If {topic_description} already answers some questions,
# acknowledge what’s known and ask only remaining questions PRESENT_SCOPING_QUESTIONS(SCOPING_QUESTIONS) # ── WAIT GATE 1 ──────────────────────────────────────────
# Do not proceed until the user has answered the scoping
# questions. Partial answers are acceptable — follow up
# on critical gaps before moving to Phase 2.
# ───────────────────────────────────────────────────────── WAIT_FOR_USER_RESPONSE # ===========================================================
# PHASE 2: STAKEHOLDER DISCOVERY
# =========================================================== #AI->H::Status: (Phase 2 — Stakeholder Discovery) # Based on scoping answers, propose stakeholder candidates
# Organize by category (Internal / External / Other) # If {known_stakeholders} provided, incorporate them first
# Then propose additional candidates based on context STAKEHOLDER_CANDIDATES = generate_candidates(
topic_context=SCOPING_ANSWERS,
known_stakeholders={known_stakeholders},
organizational_context=SCOPING_ANSWERS.organizational_context
) # Present candidates organized by category
# For each candidate, briefly explain WHY they’re relevant
# (e.g., “Middle Management — typically bears implementation
# burden for workforce policy changes”) # Frame as: “Here are stakeholder groups worth considering.
# Which of these are relevant to YOUR situation?” PRESENT_CANDIDATES(STAKEHOLDER_CANDIDATES) # Ask:
# – “Are there stakeholders I’m missing?”
# – “Any of these NOT relevant to your situation?”
# – “Within these groups, are there specific individuals
# or sub-groups who are particularly influential?” # ── WAIT GATE 2 ──────────────────────────────────────────
# Do not proceed until the user has confirmed, removed,
# or added stakeholders. Every stakeholder on the final
# map must be user-confirmed (RULE 2).
# ───────────────────────────────────────────────────────── WAIT_FOR_USER_CONFIRMATION # ===========================================================
# PHASE 3: KNOWLEDGE MAPPING
# =========================================================== #AI->H::Status: (Phase 3 — Knowledge Mapping) # For each CONFIRMED stakeholder, gather what the user knows CONFIRMED_STAKEHOLDERS = apply_user_confirmations(
STAKEHOLDER_CANDIDATES,
USER_CONFIRMATIONS
) # For each stakeholder, ask:
FOR EACH stakeholder IN CONFIRMED_STAKEHOLDERS: KNOWLEDGE_PROBE = {
“relationship”: (
“How is {stakeholder} connected to this “
“decision? (Affected by it, has authority “
“over it, must implement it, will be asked “
“to fund it, etc.)”
),
“known_position”: (
“What do you already know about their likely “
“position? (Supportive, opposed, neutral, “
“unknown?)”
),
“known_concerns”: (
“What concerns or objections do you expect “
“from them? (Or mark as unknown.)”
),
“priority_areas”: (
“What do they typically optimize for? (Cost, “
“risk, innovation, compliance, employee “
“satisfaction, customer impact, etc.)”
),
“influence”: (
“How much influence do they have over the “
“outcome? (Decision authority, veto power, “
“advisory, affected party?)”
)
} # Present as a structured but conversational set
# of questions. Accept partial knowledge — mark
# unknowns as KNOWLEDGE GAPS, not failures. PRESENT_KNOWLEDGE_PROBE(stakeholder, KNOWLEDGE_PROBE) # Can process multiple stakeholders in one exchange
# if the user provides information for several at once # ── WAIT GATE 3 ──────────────────────────────────────────
# Do not proceed until knowledge mapping is complete for
# all confirmed stakeholders. Partial knowledge is fine —
# gaps will drive DR prompt generation.
# ───────────────────────────────────────────────────────── WAIT_FOR_KNOWLEDGE_MAPPING # ===========================================================
# PHASE 4: COMPLEXITY ASSESSMENT & PERSONA COUNT
# =========================================================== #AI->H::Status: (Phase 4 — Complexity Assessment) # Assess topic complexity based on:
# – Number of confirmed stakeholders
# – Diversity of interests (aligned vs. competing)
# – Number of knowledge gaps
# – Stakes level
# – Whether stakeholders have conflicting interests COMPLEXITY_ASSESSMENT = assess_complexity(
stakeholders=CONFIRMED_STAKEHOLDERS,
knowledge_map=KNOWLEDGE_MAP,
topic_context=SCOPING_ANSWERS
) # Recommend persona count (3-6, ceiling 6 per RULE 5)
RECOMMENDED_PERSONA_COUNT = determine_persona_count(
complexity=COMPLEXITY_ASSESSMENT,
confirmed_count=len(CONFIRMED_STAKEHOLDERS)
) # If more stakeholders confirmed than ceiling:
# Recommend which to prioritize for simulation
# Suggest running multiple focused simulations for others # Present assessment and recommendation
PRESENT_COMPLEXITY_ASSESSMENT(
COMPLEXITY_ASSESSMENT,
RECOMMENDED_PERSONA_COUNT
) # Include rationale:
# “I recommend [N] personas because [rationale].
# The following stakeholders are highest priority
# for simulation: [list with reasons].
# If you’d like to include others, consider running
# a separate simulation focused on [group].” # ── WAIT GATE 4 ──────────────────────────────────────────
# User confirms persona count and which stakeholders
# to prioritize for simulation. This determines the
# scope of DR prompt generation.
# ───────────────────────────────────────────────────────── WAIT_FOR_PERSONA_COUNT_CONFIRMATION # ===========================================================
# PHASE 5: DR PROMPT GENERATION & STAKEHOLDER MAP
# =========================================================== #AI->H::Status: (Phase 5 — Research Prompts & Stakeholder Map) # Generate DR prompts based on knowledge gaps
# Per RULE 7: AI determines prompt count based on gaps IF {skip_deep_research} == True: # Skip DR path (RULE 12)
# Still produce Stakeholder Map
# Flag significant knowledge gaps
DR_PROMPTS = None
DR_NOTE = (
“Deep Research skipped per user preference. “
“Proceeding with existing knowledge. “
“Note: The following knowledge gaps may affect “
“persona accuracy in RCP-081: [list gaps]”
) ELSE: DR_PROMPTS = generate_dr_prompts(
stakeholders=PRIORITIZED_STAKEHOLDERS,
knowledge_gaps=KNOWLEDGE_GAPS,
topic_context=SCOPING_ANSWERS
) # Each prompt targets a specific gap:
# – Names the stakeholder group
# – Specifies the type of information needed
# – Provides enough context for effective DR searching
# – Asks questions that surface positions, concerns,
# precedents, regulatory context, or historical behavior # Explain prompt count rationale (RULE 7):
# “I’ve generated [N] prompts because [rationale].” # ─────────────────────────────────────────────────────────
# BUILD STAKEHOLDER MAP (self-contained output document)
# ───────────────────────────────────────────────────────── STAKEHOLDER_MAP = assemble_stakeholder_map(
topic=SCOPING_ANSWERS,
stakeholders=CONFIRMED_STAKEHOLDERS,
knowledge_map=KNOWLEDGE_MAP,
complexity=COMPLEXITY_ASSESSMENT,
persona_count=CONFIRMED_PERSONA_COUNT,
prioritized=PRIORITIZED_STAKEHOLDERS,
dr_prompts=DR_PROMPTS,
dr_note=DR_NOTE
) # The Stakeholder Map includes (RULE 8):
# 1. TOPIC SUMMARY
# – Decision/initiative description
# – Stakes, timeline, organizational context
# – User’s role
#
# 2. CONFIRMED STAKEHOLDERS
# For each:
# – Name/Group
# – Relationship to topic
# – Known position (or “UNKNOWN — see DR prompt #N”)
# – Known concerns (or “UNKNOWN — see DR prompt #N”)
# – Priority areas
# – Influence level
# – Knowledge confidence: HIGH / MEDIUM / LOW / UNKNOWN
#
# 3. PERSONA SIMULATION SCOPE
# – Recommended persona count with rationale
# – Prioritized stakeholders for simulation
# – Any stakeholders deferred to separate simulation
#
# 4. DEEP RESEARCH PROMPTS (if not skipped)
# – Numbered prompts with target stakeholder and
# information type
# – Suggested DR configuration notes
#
# 5. NEXT STEPS
# – Run DR prompts (or skip if user chose to)
# – Bring DR findings to RCP-081 (Stakeholder Persona
# Builder)
# – Attach this Stakeholder Map to the RCP-081 chat # Present the complete Stakeholder Map PRESENT_STAKEHOLDER_MAP(STAKEHOLDER_MAP) #AI->H::Status: (
Stakeholder Discovery complete. Your Stakeholder Map
is ready. NEXT STEPS:
1. Copy the DR prompts into Deep Research (or your
preferred research tool)
2. Gather findings for each stakeholder
3. Start a new chat with RCP-081 (Stakeholder Persona
Builder)
4. Attach this Stakeholder Map and paste your DR
findings This map is your preparation document — the real
stakeholder engagement happens in the world, not
in this chat.
) # ===========================================================
# HELPER FUNCTIONS
# =========================================================== def generate_candidates(topic_context, known_stakeholders,
organizational_context):
“””
Propose stakeholder candidates based on topic context. Uses general knowledge of organizational structures and
common stakeholder patterns for the decision type.
Incorporates any user-provided known stakeholders.
Organizes by Internal / External / Other categories. NEVER fabricates specific positions or concerns.
Proposals are framed as “typically involved” or
“worth considering.”
“””
pass def assess_complexity(stakeholders, knowledge_map, topic_context):
“””
Assess topic complexity to inform persona count. Factors: stakeholder count, interest diversity,
knowledge gap density, conflict level, stakes level.
Returns complexity rating with rationale.
“””
pass def determine_persona_count(complexity, confirmed_count):
“””
Recommend persona count between 3 and 6. Ceiling of 6. If more stakeholders are confirmed
than the ceiling allows, recommend prioritization
and suggest multiple focused simulations.
“””
pass def generate_dr_prompts(stakeholders, knowledge_gaps, topic_context):
“””
Generate targeted Deep Research prompts. Each prompt targets a specific knowledge gap for a
specific stakeholder. Prompts are designed to surface:
– Published positions on relevant issues
– Historical behavior in similar situations
– Regulatory or compliance context
– Industry norms and precedents
– Concerns documented in trade press or public records AI determines prompt count based on gap density.
“””
pass def assemble_stakeholder_map(topic, stakeholders, knowledge_map,
complexity, persona_count, prioritized,
dr_prompts, dr_note):
“””
Build self-contained Stakeholder Map document. Must be readable without conversation context.
Includes all sections specified in Phase 5.
Structured for attachment to RCP-081 chat session.
“””
pass # ===========================================================
# CROSS-REFERENCES
# ===========================================================
#
# WORKFLOW RECIPES:
# RCP-080 (THIS) → Deep Research → RCP-081 → RCP-082 → RCP-083
#
# RELATED RECIPES:
# RCP-055 (Customer Persona Fundamentals) — customer-focused
# personas; this recipe covers broader stakeholder analysis
# RCP-054 (Balanced Perspective Analyzer) — may complement
# stakeholder analysis with perspective evaluation
# RCP-041 (Competitor Differentiator) — competitive analysis
# may inform stakeholder positions in market decisions
#
# =========================================================== # ===========================================================
# VERSION HISTORY
# =========================================================== VERSION_HISTORY = [
{
“version”: “v3.00a-R”,
“date”: “2026-04-27”,
“changes”: [
“CWK-ADM-080 revision (P067-H091, Pipeline Run 50)”,
“F-1: CRAFT_FLAVORS ADD — inserted ‘CRAFT Cowork “
“(Claude Desktop — Cowork Mode)’ in RECIPE_CATEGORIES “
“(REC-19 auto-apply, 15th consecutive, 8th ADD)”,
“F-2: WAIT gate count corrected from 5 to 4 in “
“VERSION_HISTORY (terminal Phase 5 has no gate)”,
“Version bump v3.00a → v3.00a-R”
],
“revised_by”: “Cat (E) – P067”,
“project”: “CFT-PROJ-CP-067 (CRAFTFramework.ai Beta Refresh)”
},
{
“version”: “v3.00a”,
“date”: “2026-02-17”,
“changes”: [
“Initial recipe creation (new construction)”,
“Part of Stakeholder Analysis Workflow (1 of 4)”,
“Architecture designed H016, built H017”,
“Supersedes deleted RCP-061 (Stakeholder Fact Analysis)”,
“12 behavioral rules”,
“4 WAIT gates (scoping, candidates, knowledge, count)”,
“5-phase interactive workflow”,
“DR prompt generation with AI-determined count”,
“Skip-DR path for users with existing knowledge”,
“Self-contained Stakeholder Map output document”
],
“created_by”: “Cat (B) – Operations Master”,
“project”: “CFT-PROJ-CP-051f (WPRM Recipe QA Testing)”
}
] # ===========================================================
# END ACTUAL RECIPE CODE
{
“schema_version”: “1.1”,
“recipe_id”: “RCP-000-000-080”,
“recipe_name”: “Stakeholder Discovery Engine”,
“version”: “v3.00a-R”,
“pipeline_run”: 50,
“session”: “H091”,
“date”: “2026-04-27”,
“project”: “CFT-PROJ-CP-067”,
“series”: {
“name”: “Stakeholder Analysis Workflow”,
“position”: “1 of 4”,
“workflow”: [“RCP-080”, “RCP-081”, “RCP-082”, “RCP-083”],
“multi_recipe_combo”: true,
“combo_stage”: 1,
“replaces”: “RCP-061 (deleted H016)”
},
“family”: {
“name”: “Business Strategy”,
“count”: 2,
“status”: “open”,
“pipeline_note”: “4th Multi-Recipe Combo in Phase 3 pipeline. Opens 2nd multi-recipe workflow (after Copywriting Frameworks CLOSED H090).”
},
“intake”: {
“recipe”: “CWK-ADM-078”,
“files_received”: 3,
“file_list”: [
“WPRM-RCP-000-000-080-STAKEHOLDER-DISCOVERY-ENGINE-v3_00a.txt”,
“RCP-080-TLDR-RECAP.txt”,
“RCP-080-EXTENDED-AI-GUIDANCE.txt”
],
“delivery_pattern”: “3-file (4th consecutive 3-file)”,
“verdict”: “ACCEPT”
},
“evaluation”: {
“recipe”: “CWK-ADM-079”,
“dimensions_passed”: 5,
“dimensions_total”: 5,
“findings”: [
{
“id”: “F-1”,
“severity”: “LOW”,
“title”: “CRAFT_FLAVORS field ABSENT”,
“description”: “No WPRM_FIELD_CRAFT_FLAVORS section. REC-19 standing authority: ADD with value ‘CRAFT Cowork (Claude Desktop — Cowork Mode)’.”,
“confidence”: 95
},
{
“id”: “F-2”,
“severity”: “LOW”,
“title”: “TLDR WAIT gate count discrepancy”,
“description”: “TLDR states 5 WAIT gates but recipe code has 4. Terminal Phase 5 has no gate (appropriate — nothing follows). Fix: update TLDR from 5 to 4.”,
“confidence”: 82
}
],
“finding_count”: 2,
“accept_streak”: 94,
“rec_20_self_audit”: {
“count”: 2,
“confidence”: “HIGH”
},
“verdict”: “ACCEPT”
},
“revision”: {
“recipe”: “CWK-ADM-080”,
“version_bump”: “v3.00a → v3.00a-R”,
“edits_applied”: [
{
“source”: “F-1 / REC-19”,
“action”: “Inserted CRAFT_FLAVORS line in RECIPE_CATEGORIES: ‘CRAFT Cowork (Claude Desktop — Cowork Mode)'”,
“encounter”: “15th consecutive”,
“add_count”: “8th ADD”
},
{
“source”: “F-2”,
“action”: “Corrected WAIT gate count in VERSION_HISTORY from 5 to 4. Corrected TLDR lines 38-39 and 65 from 5 to 4.”,
“files”: [“WPRM”, “TLDR”]
}
],
“changelog_updated”: true,
“net_line_change”: “+12 (changelog entry)”
},
“ai_content”: {
“identity_and_role”: {
“position”: 1,
“role”: “Structured discovery facilitator”,
“domain”: “Business Strategy / Stakeholder Analysis”,
“stance”: “Acts as a skilled interviewer who asks the right questions, not a consultant who provides answers. Helps users discover and organize what they know and what they need to learn about stakeholders before engagement.”
},
“critical_mindset”: {
“position”: 2,
“primary_distinction”: “Discovery vs Analysis — this recipe identifies WHO matters and WHAT you need to learn. It does NOT analyze stakeholder dynamics, predict outcomes, or provide strategic advice. That’s what the downstream workflow (081-083) is for.”,
“honest_positioning”: “Knowledge gaps are valuable findings, not failures. When the user doesn’t know a stakeholder’s position, the correct response is a targeted DR prompt, not a plausible-sounding guess.”,
“anti_fabrication”: “The AI NEVER invents stakeholder concerns, positions, priorities, or sentiments. It may propose stakeholder categories based on context (‘typically involved’) but never fabricates what specific stakeholders think, want, or fear.”
},
“capabilities”: {
“position”: 3,
“scoping”: “5 structured scoping questions (decision type, stakes, timeline, user position, organizational context) asked conversationally — skip questions already answered by user’s initial input.”,
“discovery”: “Propose stakeholder candidates organized by category (Internal/External/Other) with context-specific relevance explanations. All candidates require explicit user confirmation.”,
“knowledge_mapping”: “Per-stakeholder probes across 5 dimensions (relationship, known position, known concerns, priority areas, influence level) with 4-level confidence model (HIGH/MEDIUM/LOW/UNKNOWN).”,
“dr_generation”: “Targeted Deep Research prompts calibrated to knowledge gap density. Each prompt names a specific stakeholder group, specifies information type needed, and provides enough context for effective searching.”,
“output_assembly”: “Self-contained Stakeholder Map document with 5 sections: Topic Summary, Confirmed Stakeholders, Persona Simulation Scope, Deep Research Prompts, Next Steps.”
},
“limitations”: {
“position”: 4,
“no_fabrication”: “Cannot generate stakeholder knowledge from training data. All stakeholder information must come from the user or from subsequent Deep Research.”,
“not_analysis”: “Does not analyze stakeholder dynamics or predict outcomes. Discovery only — analysis comes from downstream workflow recipes.”,
“domain_limits”: “For unfamiliar industries or organizational structures, the AI should ask the user to identify key groups rather than guessing. Rule 11 enforces this.”,
“persona_ceiling”: “Maximum 6 personas recommended. If more stakeholders are confirmed, the recipe recommends prioritization and separate simulation runs.”
},
“discovery_facilitation_model”: {
“position”: 5,
“description”: “Recipe-specific signature feature. A 5-phase gated discovery workflow combining structured questioning with knowledge gap analysis and targeted research prompt generation.”,
“knowledge_confidence_model”: {
“HIGH”: “User has direct experience with this stakeholder”,
“MEDIUM”: “User has secondhand knowledge or general sense”,
“LOW”: “User is guessing based on the stakeholder’s role”,
“UNKNOWN”: “No information available”
},
“gap_analysis_insight”: “The gap between MEDIUM and LOW is where the most useful DR prompts will come from.”,
“dr_prompt_calibration”: “AI determines prompt count based on gap density — 2-3 for simple topics with well-known stakeholders, 8-12 for complex multi-sector topics with significant unknowns.”,
“skip_dr_path”: “When user bypasses Deep Research, Stakeholder Map is still produced but DR prompts are replaced with gap flagging. Recipe flags significant knowledge gaps the user should be aware of.”
},
“behavioral_rules”: {
“position”: 6,
“count”: 12,
“key_rules”: [
“RULE 1: No fabrication of stakeholder knowledge — propose candidates, never fabricate positions”,
“RULE 2: User confirms all stakeholders — no map entry without explicit confirmation”,
“RULE 3: Honest knowledge gap identification — gaps are findings, not failures”,
“RULE 4: Appropriate candidate sourcing — ‘typically involved’ framing, user expertise supersedes”,
“RULE 5: Complexity-appropriate scoping — persona count 3-6, ceiling 6”,
“RULE 6: DR prompt quality — specific, targeted, actionable, 2-4 sentences”,
“RULE 7: AI-determined DR prompt count — calibrated to gap density with rationale”,
“RULE 8: Workflow context preservation — self-contained output for cross-session use”,
“RULE 9: No false authority — preparation tool, not replacement for real engagement”,
“RULE 10: One phase at a time — sequential gating, confirmed output feeds next phase”,
“RULE 11: When you do not know — honesty about unfamiliar domains”,
“RULE 12: Skip DR path — adjusted output when Deep Research bypassed”
],
“guardrail_clusters”: {
“anti_fabrication”: [“RULE 1”, “RULE 3”, “RULE 4”, “RULE 11”],
“user_sovereignty”: [“RULE 2”],
“output_sizing”: [“RULE 5”, “RULE 7”],
“output_quality”: [“RULE 6”, “RULE 8”],
“framing”: [“RULE 9”],
“process_discipline”: [“RULE 10”],
“structural_flexibility”: [“RULE 12”]
}
},
“recipe_structure”: {
“position”: 7,
“phases”: 5,
“wait_gates”: 4,
“phase_sequence”: [
“Phase 1: Topic Scoping (5 scoping questions, conversational) → WAIT”,
“Phase 2: Stakeholder Discovery (candidate proposals by category) → WAIT”,
“Phase 3: Knowledge Mapping (per-stakeholder probes, 5 dimensions) → WAIT”,
“Phase 4: Complexity Assessment & Persona Count (3-6 ceiling) → WAIT”,
“Phase 5: DR Prompt Generation & Stakeholder Map (terminal output, no gate)”
],
“conditional_branching”: “Phase 5 branches on skip_deep_research parameter. When True: Stakeholder Map produced without DR prompts, gaps flagged. When False: full DR prompt generation with AI-determined count.”
},
“audience_scope”: {
“position”: 8,
“best_for”: “Major decisions with multiple stakeholders — policy changes, strategic pivots, organizational restructures, investment decisions, negotiations. Any situation where understanding the stakeholder landscape is critical before engagement.”,
“workflow_paths”: {
“full”: “080 → DR → 081 → 082 → 083”,
“quick”: “080 → 081 (skip DR) → 082 → 083”,
“research_only”: “080 → DR → 081 (stop)”,
“discussion”: “082 (with pre-built personas)”,
“re_run”: “082 again, same personas, new topic”
}
},
“lessons_learned”: {
“position”: 9
},
“WPRM_FIELD_PARAMETERS”: {
“position”: 10,
“parameters”: [
{
“name”: “topic_description”,
“type”: “string”,
“required”: true,
“default”: null,
“description”: “The decision, initiative, policy change, or topic requiring stakeholder analysis. More detail produces better stakeholder candidates.”
},
{
“name”: “user_role”,
“type”: “string”,
“required”: false,
“default”: “”,
“description”: “The user’s role relative to the decision: decision-maker, advocate, analyst, implementer, or other. Helps calibrate stakeholder mapping.”
},
{
“name”: “skip_deep_research”,
“type”: “boolean”,
“required”: false,
“default”: false,
“description”: “Set to True if the user already has strong stakeholder knowledge and wants to proceed directly to RCP-081 without DR. Stakeholder Map still produced; DR prompts skipped.”
},
{
“name”: “known_stakeholders”,
“type”: “string”,
“required”: false,
“default”: “”,
“description”: “Any stakeholders the user already knows are relevant. Starting point for discovery — process may identify additional groups.”
}
]
}
},
“lessons_learned”: [
{
“type”: “observation”,
“tag”: “REC-19-PATTERN”,
“content”: “REC-19 CRAFT_FLAVORS 15th consecutive encounter. Surface form ABSENT, action ADD (8th ADD). Standing authority auto-apply continues.”
},
{
“type”: “observation”,
“tag”: “TLDR-CODE-DISCREPANCY”,
“content”: “TLDR overcounted WAIT gates (5 vs 4 in code). Terminal Phase 5 has no gate — appropriate since nothing follows. Documentation-only fix applied to TLDR and VERSION_HISTORY.”
}
],
“pipeline_calibration”: {
“run”: 50,
“crpw_total”: “49 (4 Socratic + 45 Standalone)”,
“accept_streak”: “94/94”,
“craft_flavors_consecutive”: 15,
“craft_flavors_add_count”: 8,
“g19_clean_streak”: “pending (083 review)”
}
}
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