IC Simulation Skill

Help startup founders prepare for the conversation that happens behind closed doors — the one where VC partners debate whether to invest. Produce a realistic IC simulation with three distinct partner perspectives, scored across 28 dimensions, with specific coaching on what to prepare. The tone is founder-first: a coaching tool for preparation, not a judgment.

Input Formats

Accept any combination: pitch deck, financial model, data room contents, text descriptions, prior market-sizing or deck-review artifacts, or just a verbal description of the business.

Available Scripts

All scripts are at ${CLAUDE_PLUGIN_ROOT}/skills/ic-sim/scripts/:

• fund_profile.py — Validates fund profile structure (archetypes, check size, thesis, portfolio)
• detect_conflicts.py — Validates conflict assessments and computes summary stats
• score_dimensions.py — Scores 28 dimensions across 7 categories with conviction-based scoring
• compose_report.py — Assembles report with cross-artifact validation; --strict exits 1 on high/medium warnings
• visualize.py — Generates self-contained HTML with SVG charts (not JSON)

Run with: python3 ${CLAUDE_PLUGIN_ROOT}/skills/ic-sim/scripts/<script>.py --pretty [args]

Available References

Read each when first needed — do NOT load all upfront. At ${CLAUDE_PLUGIN_ROOT}/skills/ic-sim/references/:

• partner-archetypes.md — Read before Step 3. Three canonical archetypes with focus areas, debate styles, red flags
• evaluation-criteria.md — Read before Step 5. 28 dimensions across 7 categories with stage-calibrated thresholds
• ic-dynamics.md — Read before Step 5d. How real VC ICs work: formats, decisions, what kills deals
• artifact-schemas.md — Consult as needed when depositing agent-written artifacts

Artifact Pipeline

Every simulation deposits structured JSON artifacts into a working directory. The final step assembles all artifacts into a report and validates consistency. This is not optional.

Rules:

• Deposit each artifact before proceeding to the next step
• For agent-written artifacts, consult references/artifact-schemas.md for the JSON schema
• If a step is not applicable, deposit a stub: {"skipped": true, "reason": "..."}
• Do NOT use isolation: "worktree" for sub-agents — files written in a worktree won't appear in the main $SIM_DIR

Keep the founder informed with brief, plain-language updates at each step. Never mention file names, scripts, or JSON. After each analytical step (4–6), share a one-sentence finding before moving on.

Workflow

Path Setup

SCRIPTS="$CLAUDE_PLUGIN_ROOT/skills/ic-sim/scripts"
REFS="$CLAUDE_PLUGIN_ROOT/skills/ic-sim/references"
if ls "$(pwd)"/mnt/*/ >/dev/null 2>&1; then
  ARTIFACTS_ROOT="$(ls -d "$(pwd)"/mnt/*/ | head -1)artifacts"
elif ls "$(pwd)"/sessions/*/mnt/*/ >/dev/null 2>&1; then
  ARTIFACTS_ROOT="$(ls -d "$(pwd)"/sessions/*/mnt/*/ | head -1)artifacts"
else
  ARTIFACTS_ROOT="$(pwd)/artifacts"
fi

If CLAUDE_PLUGIN_ROOT is empty, fall back: Glob for **/founder-skills/skills/ic-sim/scripts/score_dimensions.py, strip to get SCRIPTS, derive REFS.

If ARTIFACTS_ROOT resolves to $(pwd)/artifacts but no artifacts/ directory exists at $(pwd): The workspace may not be mounted yet. Use Glob with pattern **/artifacts/founder_context.json to locate existing artifacts, and derive ARTIFACTS_ROOT from the result. If nothing is found, mkdir -p "$ARTIFACTS_ROOT" and proceed.

SIM_DIR="$ARTIFACTS_ROOT/ic-sim-{company-slug}"
mkdir -p "$SIM_DIR"

Mode Selection

Ask the user (or infer from context):

1. Interactive — Pause between partner positions for founder input
2. Auto-pilot — Run all sections without pausing
3. Fund-specific — Research a real fund first. Combines with either mode.

Steps 1-2: Extract Startup Profile and Import Prior Artifacts

When files are provided, spawn a general-purpose Task sub-agent to read materials, extract startup profile, and import prior market-sizing/deck-review artifacts. The sub-agent deposits both startup_profile.json and prior_artifacts.json to $SIM_DIR.

If missing fields are flagged, ask the user and patch the artifact. When only text is provided, extract directly without a sub-agent.

Graceful degradation: If Task tool is unavailable, extract directly.

Step 3: Build Fund Profile -> fund_profile.json

REQUIRED — read $REFS/partner-archetypes.md now.

Generic mode: Build a standard early-stage fund profile with the three canonical archetypes.

Fund-specific mode: Use WebSearch to research fund thesis, portfolio, partner backgrounds, check size range, and stage preference. Map real partners to archetype roles.

Validation constraints: check_size_range must be a dict (not a string), stage_focus must be a non-empty array, each source must have url or title.

cat <<'FUND_EOF' | python3 "$SCRIPTS/fund_profile.py" --pretty -o "$SIM_DIR/fund_profile.json"
{...fund profile JSON...}
FUND_EOF

Accepted warnings: Add accepted_warnings array with code, match (case-insensitive), and reason. Compose downgrades matching warnings to "acknowledged".

Step 4: Check Portfolio Conflicts -> conflict_check.json

Review the fund's portfolio against the startup. Assess each company for: direct conflict, adjacent conflict, or customer overlap. Use consistent names between portfolio and conflicts. Duplicates are auto-deduplicated by (company, type) pair.

cat <<'CONFLICT_EOF' | python3 "$SCRIPTS/detect_conflicts.py" --pretty -o "$SIM_DIR/conflict_check.json"
{"portfolio_size": 15, "conflicts": [...]}
CONFLICT_EOF

Step 5: Partner Assessments and Discussion

Step 5a-5c: Parallel Sub-Agent Assessments

REQUIRED — read $REFS/evaluation-criteria.md now.

Spawn 3 general-purpose Task sub-agents in a single message (parallel, no isolation: "worktree"). Each receives: archetype persona, startup_profile, fund_profile, conflict_check, prior_artifacts, and relevant evaluation criteria. Each independently produces partner_assessment_{role}.json. Instruct each sub-agent to return ONLY: (1) the file path written, (2) the verdict, and (3) a one-sentence rationale — do not echo the full assessment back.

Graceful degradation: If Task tool unavailable, generate sequentially with strict persona separation. Set assessment_mode: "sequential" and "assessment_mode_intentional": true in discussion.json.

Step 5d: Orchestrate Discussion -> discussion.json

REQUIRED — read $REFS/ic-dynamics.md now.

Read all 3 assessments. Generate debate: each partner presents, partners respond to each other, build toward consensus. In interactive mode, pause between positions.

Verdict reconciliation: Ensure each partner's verdict reflects their final position after debate, not their opening position. The compose report flags UNANIMOUS_VERDICT_MISMATCH when all partners contradict the consensus.

Discussion-to-Score reconciliation: Before scoring, re-read discussion conclusions. If a dimension was debated as a dealbreaker, ensure the score reflects that severity. Compose flags CONSENSUS_SCORE_MISMATCH when discussion verdict and score diverge.

Step 6: Score Dimensions -> score_dimensions.json

cat <<'SCORE_EOF' | python3 "$SCRIPTS/score_dimensions.py" --pretty -o "$SIM_DIR/score_dimensions.json"
{
  "items": [
    {"id": "team_founder_market_fit", "category": "Team", "status": "strong_conviction", "evidence": "...", "notes": "..."},
    ...all 28 items...
  ]
}
SCORE_EOF

Step 7: Compose and Validate Report

python3 "$SCRIPTS/compose_report.py" --dir "$SIM_DIR" --pretty -o "$SIM_DIR/report.json"

Fix high-severity warnings and re-run. Use --strict to enforce a clean report.

Primary deliverable: Read report_markdown from the output JSON, write it to $SIM_DIR/report.md, and display it to the user in full. Present the file path so the user can access it directly. Then add coaching commentary.

Step 8 (Optional): Generate Visual Report

python3 "$SCRIPTS/visualize.py" --dir "$SIM_DIR" -o "$SIM_DIR/report.html"

Present the HTML file path to the user so they can open the visual report.

Step 9: Deliver Artifacts

Copy final deliverables to workspace root: {Company}_IC_Simulation.md, .html (if generated), .json (optional).

Scoring

• 28 dimensions, each: strong_conviction / moderate_conviction / concern / dealbreaker / not_applicable
• Conviction score: (strong*1.0 + moderate*0.5) / applicable * 100
• Verdicts: invest (>=75%), more_diligence (>=50%), pass (<50%), hard_pass (any dealbreaker)
• One dealbreaker forces hard_pass regardless of score

Cross-Agent Integration

This skill imports artifacts from prior market-sizing and deck-review analyses. Imported artifacts are recorded with dates. Imports older than 7 days are flagged as STALE_IMPORT.