ic-sim

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.

Skill Metadata

• Author: lool-ventures
• Version: managed in founder-skills/.claude-plugin/plugin.json
• Compatibility: Python 3.10+ and uv for script execution.
• Imports (recommended):
• market-sizing:sizing.json — fund alignment and market validation
• deck-review:checklist.json — deck quality assessment
• Exports:
• report.json → fundraise-readiness, dd-readiness

Skill Execution Model (READ FIRST)

This skill runs inline in the main thread (not as a sub-agent). The main thread has full tool access including Bash, and is responsible for orchestrating the full pipeline: running producer scripts, persisting artifacts, and dispatching the ic-sim sub-agent at specific moments.

Two dispatch contexts for the sub-agent:

• Context A — Per-step analytical dispatch (Mitigation 1): Steps 4, 5, and 6 dispatch the ic-sim agent via the Task tool. The novel element here is parallel dispatch: Step 4 (PARTNER_ANALYSIS) dispatches the agent three times simultaneously — one per partner archetype — in a single assistant turn. Steps 5 and 6 (SCORE_DIMENSIONS and DETECT_CONFLICTS) are sequential dispatches. The sub-agent does deep analysis and returns structured JSON. The main thread captures the JSON and pipes it through the producer script. The sub-agent does NOT write artifacts directly.
• Context B — Post-compose coaching dispatch: The final step dispatches the sub-agent after compose_report.py writes report.md. The sub-agent reads report.md, appends ## Coaching Commentary, verifies all canonical artifacts on disk, and returns a structured success payload.

Why this model: In Cowork, sub-agents have a restricted tool allowlist (no Bash). By keeping orchestration in the main thread and dispatching sub-agents only for analytical or post-compose tasks that use only Read/Edit/Glob/Grep, the pipeline works correctly in both Claude Code (CLI) and Cowork.

Tolerant JSON extraction protocol (Context A): After dispatching the sub-agent, capture its final assistant message. The sub-agent should return raw JSON, but may wrap it in `json ... ` fences or add a prose preamble. Extract JSON tolerantly:

1. If the message is wrapped in a `json ... ` (or plain ` ... ` ) fence, strip the fence first.
2. Try to parse the stripped text directly as JSON.
3. If that fails, walk through the text looking for the first { character and try json.JSONDecoder().raw_decode(text[i:]) — this is brace-aware and handles nested objects correctly (unlike regex, which truncates on the first }).
4. If extraction fails entirely, re-prompt the sub-agent with: "Your previous reply could not be parsed as JSON. Return ONLY the JSON object — no markdown fences, no prose preamble."

See founder-skills/references/skill-execution-model.md for the full inline-skill execution model (3 dispatch contexts, Mitigation 1+2, producer contract, Cowork quirks, per-symptom triage).

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)

Also available from ${CLAUDE_PLUGIN_ROOT}/scripts/ (shared):

• founder_context.py — Per-company context management (init/read/merge/validate)

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 4. 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 composing discussion.json. 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.

| Step | Artifact | Producer | |------|----------|----------| | 1 | founder context | founder_context.py read/init | | 2 | startup_profile.json | Agent (heredoc) | | 3 | prior_artifacts.json | Agent (heredoc) | | 4 | fund_profile.json | Agent (heredoc) then fund_profile.py validates | | 5a | conflict_check.json | Context A dispatch: DETECT_CONFLICTS → detect_conflicts.py | | 5b-d | partner_assessment_{visionary,operator,analyst}.json | Context A dispatch: PARTNER_ANALYSIS × 3 in parallel | | 6 | discussion.json | Main thread combines 3 partner returns | | 7 | score_dimensions.json | Context A dispatch: SCORE_DIMENSIONS → score_dimensions.py | | 8 | Report | compose_report.py (writes both report.json and report.md) | | 9 | Coaching | Context B dispatch: POST_COMPOSE_COACHING |

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 (5–7), share a one-sentence finding before moving on.

Workflow

Step 0: Path Setup

Every Bash tool call runs in a fresh shell — variables do not persist. Prefix every Bash call that uses these paths with the variable block below, or substitute absolute paths directly:

SCRIPTS="${CLAUDE_PLUGIN_ROOT}/skills/ic-sim/scripts"
REFS="${CLAUDE_PLUGIN_ROOT}/skills/ic-sim/references"
SHARED_SCRIPTS="${CLAUDE_PLUGIN_ROOT}/scripts"
if ls "$(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 and SHARED_SCRIPTS.

If ARTIFACTS_ROOT resolves to $(pwd)/artifacts but no artifacts/ directory exists at $(pwd): Use Glob with pattern **/artifacts/founder_context.json to locate existing artifacts, and derive ARTIFACTS_ROOT from the result. If nothing is found, mkdir -p "$ARTIFACTS_ROOT" and proceed.

After Step 1 (when the slug is known):

SIM_DIR="$ARTIFACTS_ROOT/ic-sim-${SLUG}"
mkdir -p "$SIM_DIR"
mkdir -p "$SIM_DIR/.staging"   # for ad-hoc sub-agent JSON staging (v0.4.2)
RUN_ID="$(date -u +%Y%m%dT%H%M%SZ)"

Pass RUN_ID to all