pipeline-orchestrator — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited pipeline-orchestrator (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill when an automation job is really a chain of steps - scrape, then run it through an AI model, then send an alert - and you want each step to retry on transient failure and the whole run to resume from the last good step. It is a generic, dependency-light runner: stages are ordinary functions, and state is checkpointed to a JSON file so a 429 in the AI step never forces a full re-scrape.
User input:
My nightly job scrapes a board, asks Gemini to summarize, then pushes Telegram. If Gemini rate-limits, don't re-scrape.Output:
# Copy assets/pipeline.py into your project, then:
from pipeline import run_pipeline
def scrape(_): return fetch_board() # your scraper
def summarize(rows): return gemini_summary(rows) # your AI step
def alert(text): return send_telegram(text) # see telegram-alerter
# checkpoints after each stage; a crash in summarize resumes there, not at scrape
result = run_pipeline([scrape, summarize, alert], state_path=".nightly_state.json"){done_index, value} to a JSON file after each successful stage.Reference: assets/pipeline.py.
Distilled from production use across the author's automation projects. v1.0.0. See also: [[cron-dispatch]], [[telegram-alerter]].
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~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.