Borrowing it
Nothing to install: this file belongs to frostney/GocciaScript. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/frostney/GocciaScript/main/.agents/skills/profile-report-review/SKILL.mdgit clone --depth 1 https://github.com/frostney/GocciaScriptWrote 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/frostney/gocciascript/profile-report-review)<a href="https://agentmods.dev/skills/frostney/gocciascript/profile-report-review"><img src="https://agentmods.dev/badge/skills/frostney/gocciascript/profile-report-review/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/frostney/gocciascript/profile-report-review"><img src="https://agentmods.dev/badge/skills/frostney/gocciascript/profile-report-review.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 | $0.00096 | $0.01486 |
| Opus 5 | $0.00048 | $0.00743 |
| Sonnet 5 | $0.00019 | $0.00297 |
| Haiku 4.5 | $0.00010 | $0.00149 |
Grade A, and why
profile-report-review 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Profile Report Review
Use this skill to review already-uploaded GocciaScript VM profile reports from main CI. Do not generate new profiles or change CI unless the user explicitly asks.
Sources
Prefer the exact GitHub Actions artifact when reviewing a specific run. Use Blob daily pointers for retained main-run history and window comparison.
test262-profileartifact /test262-profiles/Blob namespace: full-corpus conformance-profile runs. Check commit SHA, test262 SHA, mode, corpus size, timeout/memory settings, and wrapper-infra counts before comparing trends. Therunblock carriesdurationSeconds,passed,failed,timeouts,totalRun, andtest262Sha;pathBreakdown[]carries per-pathtotalOpcodes/allocations/functionSelfTimeNs.benchmark-profileartifact /benchmark-profiles/Blob namespace: deterministic bytecode benchmark profiles. Check commit SHA, deterministic mode, profile count, benchmark file set, and whether the normal benchmark baseline for the same run moved in the same direction. Per-file data is underpathBreakdown[](totalOpcodes,functionSelfTimeNs); benchmark deterministic mode recordsallocationsas0(not tracked here — use test262 for allocation trends).
If both artifact and Blob payloads exist for the same run, confirm commit SHA, mode, generated timestamp, and profile count match before using them together.
Profile artifacts are retained ~30 days, so review the whole retained window, not two runs. Two adjacent runs cannot separate a real change from runner noise. Enumerate every run's artifact and build a time series:
gh api --paginate 'repos/{owner}/{repo}/actions/artifacts?per_page=100' \
--jq '.artifacts[] | select(.name=="benchmark-profile" and .expired==false)
| [.created_at, (.workflow_run.id|tostring), .workflow_run.head_sha] | @tsv'
benchmark-profile is tiny (~60 KB) — download the full window. test262-profile
is large (~90 MB each) — extract only *-aggregate.json and discard the details
unless a specific run warrants a deep dive. Map each commit to its PR
(git log -1 --format=%s <sha>) so any movement is attributable.
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 · 116 lines · 96 tokens per session scan A 4bd52e090d61
profile-report-review is a skill published in the GitHub repository frostney/GocciaScript (20 stars, last pushed yesterday), licensed MIT. It adds 96 tokens to every session and 1,486 once invoked, about $0.0005 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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