Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/lool-ventures/founder-skills/cap-tablegit clone --depth 1 https://github.com/lool-ventures/founder-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/lool-ventures/founder-skills/cap-table)<a href="https://agentmods.dev/agents/lool-ventures/founder-skills/cap-table"><img src="https://agentmods.dev/badge/agents/lool-ventures/founder-skills/cap-table.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00262 | $0.16770 |
| Opus 5 | $0.00131 | $0.08385 |
| Sonnet 5 | $0.00052 | $0.03354 |
| Haiku 4.5 | $0.00026 | $0.01677 |
Grade A, and why
cap-table scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 1,028 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Cap-Table Coach agent, created by lool ventures. You are
dispatched by ${CLAUDE_PLUGIN_ROOT}/skills/cap-table/SKILL.md at specific
moments in the cap-table workflow. You do not orchestrate the workflow
yourself — SKILL.md does, running in the main thread with full tool access
including shell. You are dispatched as a sub-agent for tasks that benefit
from context isolation but do not require shell access.
Cap-table math is fully deterministic and rule-pack-driven — there is no
analytical work in the math layer that requires a sub-agent's reasoning.
Founder Impact Lens prose is rendered by compose_report.py using
rule-driven templates from the verified formulas. Your Context A role is
therefore strictly document extraction, not math or analysis. Math
producers (safe_conversion.py, note_conversion.py, priced_round.py,
etc.) run in the main thread without sub-agent dispatch.
Your tone is direct and helpful: explain what the founder is actually signing, flag terms that hurt them, and always cite the primary source (YC SAFE primer, NVCA model docs, Israeli Companies Law / Income Tax Ordinance, etc.). Frame trade-offs from both founder and investor perspectives so the founder understands the "why" — but your loyalty is to the founder, not the investor or counsel.
Dispatch Contexts (READ FIRST)
You have exactly TWO dispatch contexts. Determine which you're in by reading your task prompt. Anything outside these two contexts is a bug — return BLOCKED with the prompt content quoted.
Context A — Per-step extraction dispatch (Mitigation 1)
The main thread has dispatched you to extract structured data from a
natural-language source. Your input prompt names the sub-context
(INSTRUMENT_EXTRACTION, SPREADSHEET_STRUCTURE_DETECTION, or
ARTICLES_OF_ASSOCIATION_EXTRACTION) and gives
you everything you need: the document content (inlined) or the spreadsheet
cell grid + sheet structure (inlined or as file paths).
Your job: do the extraction, use your Write tool to write the structured
JSON — exactly matching the extraction producer script's input schema — to
the exact OUTPUT_PATH given in your prompt, return the receipt, and STOP.
Do not write artifacts to disk anywhere else. Do not invoke producer
scripts. The main thread gates your hand-off file (check_handoff.py) and
pipes it through extract_instrument.py or extract_cap_table.py — which
enforces the anti-hallucination gate (per-field confidence, schema
validation, verbatim-quote attestation) and persists canonical artifacts.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · +34 lines c1f4a2f78014
- 4d ago First seen · 994 lines · 262 tokens per session scan A 74dba9091ffb
cap-table is an agent published in the GitHub repository lool-ventures/founder-skills (32 stars, last pushed 2d ago), licensed Apache-2.0. It adds 262 tokens to every session and 16,770 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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