Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
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 skills add gooseworks-ai/goose-skills --skill outcomes-ledgergit clone --depth 1 https://github.com/gooseworks-ai/goose-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/skills/gooseworks-ai/goose-skills/outcomes-ledger)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/outcomes-ledger"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/outcomes-ledger/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/outcomes-ledger"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/outcomes-ledger.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00076 | $0.02666 |
| Opus 5.5 | $0.00030 | $0.01066 |
| Sonnet 5.5 | $0.00015 | $0.00533 |
| Haiku 4.5 | $0.00008 | $0.00267 |
Grade A, and why
outcomes-ledger 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 3d ago.
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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
outcomes-ledger
Every list a playbook hands over gets acted on outside GooseWorks: the human emails the prospects, messages the creators. This atom is how what happened comes back, so the next run never repeats a person who said no and gets better from what worked. It never contacts anyone and never requires outcomes: no outcomes, carry on.
Parameters (the loading playbook sets them)
entity: person |
entity: creator |
|
|---|---|---|
| Loaded by | research-audience (writes rows when a list is shown), write-outreach (updates statuses from replies and results) | work-with-creators |
| Ledger file | research/audience/ledger.csv |
creators/roster.csv |
| Lessons document (file → brand document) | research/audience/lessons/<YYYY-MM-DD>.md → Outreach lessons — <Brand> — <YYYY-MM-DD> |
creators/lessons/<YYYY-MM-DD>.md → Creator lessons — <Brand> — <YYYY-MM-DD> |
| Entity columns (after the required ones) | segment, tier, value_tier, signal_type, email_status, region_flag, email, email_candidate, profile_url, root_domain, route, sequence_id, opener_id, first_touch_date, last_touch, reply_category, recontact_after |
role, tier, deal_structure, vetting_depth, wave, wave_criterion, wave_result, quoted_rate, then the roster's own columns |
| Status values | listed · contacted · replied · meeting · not-a-fit · bounced · unsubscribed · do-not-contact |
shortlisted · contacted · replied · quoted · declined · signed · posted · never-again |
| Suppressing statuses (permanent) | bounced · unsubscribed · do-not-contact |
never-again |
| Timed exclusions (not suppression) | not-a-fit — the account is skipped for 6 months, then eligible again; contacted with no reply after the sequence ended — the person is skipped for 12 weeks on every channel (cool-down) |
declined — skipped until the human re-opens it |
| Match keys, in order | email (or email_candidate) → profile URL → name + root domain; account rows: root domain + route | handle + platform → profile URL |
| Grouped by (for lessons) | segment, tier, signal type, email status, region flag, sequence, opener and touch, reply category | role, tier, deal structure, vetting depth, wave |
| Re-check | recheck_by = date listed + 30 days |
revet_by = vetted on + 30 days |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago Changed 0f65220e9220
- 8d ago First seen · 82 lines · 76 tokens per session scan A 68ec452224a7
outcomes-ledger is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,272 stars, last pushed 2d ago), licensed MIT. It adds 76 tokens to every session and 2,666 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-21.
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