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 run-memorygit 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/run-memory)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/run-memory"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/run-memory/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/run-memory"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/run-memory.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 | $0.00072 | $0.04490 |
| Opus 5.5 | $0.00029 | $0.01796 |
| Sonnet 5.5 | $0.00014 | $0.00898 |
| Haiku 4.5 | $0.00007 | $0.00449 |
Grade A, and why
run-memory 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
run-memory
A playbook that runs again (often as a weekly or monthly automation) must know what its last run found, what it recommended and whether that worked. This atom is that memory. The report is separate (the write-report skill, ../write-report/SKILL.md); a run record is the short, structured note the next run reads.
Who loads it: every playbook that can run more than once for the same brand: research, analysis, ads, social, email, search, creators. Not one-off makers (a single post, one photo set). It runs twice per run: Start before the playbook's own work, End after its report.
What it does not replace. Where a playbook already keeps a working log, that log stays the source of truth: run-paid-campaigns' ads/campaigns/<slug>/state.md + decisions.md (the server's campaign-harness grammar, R<n> ids, ## Checks due), the outcomes-ledger skill's ledgers, analyze-performance's definitions.md. The run record copies their ids and adds only what they lack (the baseline recipe and the result numbers). It never keeps a second, different copy of their state, and when the playbook has its own scoring rule (run-paid-campaigns' "Score last week"), step 3 below is that step. Use its rule and add only what is missing.
Inputs (the calling playbook supplies them)
| Input | Rule |
|---|---|
playbook |
The playbook slug (analyze-performance). Goes in the tags. |
job |
The job in plain words (weekly ads check, monthly read, competitor watch). Never a slug, tool or id. One playbook with several cadences has one job per cadence. Keep it the same on every run, because the next run searches by it. job-slug is job lower-cased and hyphenated. |
subject |
Optional. Only when a job keeps separate runs per campaign, rival or list (spring-launch). Lower-case slug. |
cadence |
daily, weekly, monthly, quarterly, or ad hoc. Used to judge whether the last record is old. |
outcome_sources |
Where this playbook can see whether a recommendation happened and what it did. Examples: meta_read change or push status, its own log file, social_post_list, campaign_read, a ledger. |
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.
- today First seen · 155 lines · 72 tokens per session scan A 9d532c1d6e21
run-memory is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,262 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 4,490 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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