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 hannesill/m4 --skill vitrine-apigit clone --depth 1 https://github.com/hannesill/m4Wrote 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/hannesill/m4/vitrine-api)<a href="https://agentmods.dev/skills/hannesill/m4/vitrine-api"><img src="https://agentmods.dev/badge/skills/hannesill/m4/vitrine-api/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/hannesill/m4/vitrine-api"><img src="https://agentmods.dev/badge/skills/hannesill/m4/vitrine-api.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00077 | $0.01886 |
| Opus 5.5 | $0.00031 | $0.00754 |
| Sonnet 5.5 | $0.00015 | $0.00377 |
| Haiku 4.5 | $0.00008 | $0.00189 |
Grade A, and why
vitrine-api 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 4d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vitrine
Vitrine shows results as cards on a board the researcher reads beside the conversation. Cards
belong to a study, stored in the .vitrine folder of the project.
Rules for every script
- One study. Set
STUDY = "short-label"once and passstudy=STUDYto every call that takes it. Cards without a label go to anauto-<time>study per process. To continue earlier work, reuse a label fromvitrine studies. - Run from the project folder. Cards go to the nearest
.vitrineabove the working directory, or toVITRINE_DATA_DIR. A script run from a temporary folder publishes where nobody looks. - Let the server run.
show()starts it, and the researcher's board points at it; stopping or restarting it breaks their view. WithVITRINE_OPEN_BROWSER=0a host app shows the board: point the researcher to the board, not to a URL. - Run
vitrineandpythonfrom the environment M4 is installed in.
Read the board
When the researcher mentions a card, a table, or the board, look before answering.
import vitrine
vitrine.list_studies() # label, dir_name, card_count; CLI: vitrine studies
ctx = vitrine.study_context(STUDY) # cards (card_id, card_type, title, annotations),
# decisions_made, pending_responses
card = vitrine.get_card("a1b2c3") # preview, response_action/values/message, annotations
vitrine.get_selection("a1b2c3") # rows or points the researcher selected
A reference copied from the board, [card:a1b2c3, study:sepsis-v1], names the card. A table's
preview holds 20 rows; the whole table is
<data_dir>/studies/<dir_name>/artifacts/<artifact_id>.parquet (data_dir from
vitrine status --json).
Publish
from vitrine import progress, section, show
STUDY = "sepsis-v1"
section("Cohort", study=STUDY)
handle = show(cohort_df, title="Cohort", source="mimiciv_icu.icustays; scripts/01_cohort.py", study=STUDY)
show(fig, title="Age at admission", description="Included stays by decade of age.", study=STUDY)
show("28-day mortality was **23%** (412 of 1,790).", title="Primary outcome", study=STUDY)
show(final_df, title="Cohort", replace=handle, study=STUDY) # update that card in place
with progress("Fitting the model", study=STUDY) as step: # completes, or fails with the block
step("fold 2 of 5")
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.
- 4d ago Changed · +36 lines · +4 tokens per session 06fafbd89d3a
- 7d ago Changed · -61 lines · +39 tokens per session 2ba82f17627b
- 1mo ago Changed · +1 lines 40966eda933d
- 1mo ago First seen · 158 lines · 34 tokens per session scan A d303c2650257
vitrine-api is a skill published in the GitHub repository hannesill/m4 (46 stars, last pushed 2d ago), licensed MIT. It adds 77 tokens to every session and 1,886 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 2026-08-30.
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