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 SagaPeak/artifacta-mcp --skill persisting-outputsgit clone --depth 1 https://github.com/SagaPeak/artifacta-mcpWrote 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/sagapeak/artifacta-mcp/persisting-outputs)<a href="https://agentmods.dev/skills/sagapeak/artifacta-mcp/persisting-outputs"><img src="https://agentmods.dev/badge/skills/sagapeak/artifacta-mcp/persisting-outputs/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/sagapeak/artifacta-mcp/persisting-outputs"><img src="https://agentmods.dev/badge/skills/sagapeak/artifacta-mcp/persisting-outputs.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 | $0.00084 | $0.02250 |
| Opus 5 | $0.00042 | $0.01125 |
| Sonnet 5 | $0.00017 | $0.00450 |
| Haiku 4.5 | $0.00008 | $0.00225 |
Grade A, and why
persisting-outputs 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 10d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persisting outputs with Artifacta
Artifacta is an artifact store built for AI agents: a hosted MCP server plus a Python CLI/SDK, backed by tenant-scoped storage with sessions, TTLs, and shareable links. This skill covers storing a run's outputs, finding them again later, and handing them off to a human or another system.
Check the connection first
Before a batch of work that will produce outputs, call whoami once. It
takes no arguments and returns the tenant name, plan tier, current usage
(usage_requests_month, usage_storage_bytes), the plan's limits
(plan_requests_limit_month, plan_storage_limit_bytes), active link counts,
and rate limits. Use it to confirm the connection works and to size what you
are about to do against quota — don't discover a storage cap mid-run.
If whoami or the other Artifacta tools are not available in this session
(no MCP connection), skip to CLI fallback below.
When to persist — and when not to
Persist:
- End-of-run deliverables: reports, generated files, datasets, build artifacts, anything the user would want back after the session ends.
- Intermediates that are expensive to regenerate (long computations, large fetches) even if not a final deliverable.
- Anything the user explicitly asks you to save, share, or hand off.
Don't persist:
- Throwaway scratch files with no value once the current step finishes.
- Secrets, credentials, or other sensitive material the user hasn't asked you to store.
- Anything you're unsure about — ask the user rather than guessing either way.
Storing artifacts
store_artifact uploads a file in one call. It requires filename, plus
exactly one of content or path — and which one is safe depends on where
the MCP server runs:
Hosted MCP or the Claude Code plugin (mcp.artifacta.io): never use
path. The server runs remotely, so a path argument resolves on the
server's own container filesystem — not the machine your files are on — and
fails or reads the wrong file. Always send content (base64-encoded bytes,
up to 10 MB decoded); for anything larger, use the large-file flow below.
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.
- 10d ago First seen · 180 lines · 84 tokens per session scan A 7f8c11a1c225
persisting-outputs is a skill published in the GitHub repository SagaPeak/artifacta-mcp (1 stars, last pushed 1mo ago), licensed MIT. It adds 84 tokens to every session and 2,250 once invoked, about $0.0004 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 2026-08-31.
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