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 ondrej-svec/heart-of-gold-toolkit --skill pipelinegit clone --depth 1 https://github.com/ondrej-svec/heart-of-gold-toolkitWrote 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/ondrej-svec/heart-of-gold-toolkit/pipeline)<a href="https://agentmods.dev/skills/ondrej-svec/heart-of-gold-toolkit/pipeline"><img src="https://agentmods.dev/badge/skills/ondrej-svec/heart-of-gold-toolkit/pipeline/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/ondrej-svec/heart-of-gold-toolkit/pipeline"><img src="https://agentmods.dev/badge/skills/ondrej-svec/heart-of-gold-toolkit/pipeline.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 | $0.00026 | $0.05471 |
| Opus 5 | $0.00013 | $0.02736 |
| Sonnet 5 | $0.00005 | $0.01094 |
| Haiku 4.5 | $0.00003 | $0.00547 |
Grade A, and why
pipeline scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `jq` and `curl` on PATH How it starts
The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/guide:pipeline — Daily Content Pipeline
"Time is an illusion. Lunchtime doubly so." — But your daily brief runs on schedule.
Run the full content pipeline: fetch signals, analyze relevance, create daily brief + drafts, edit for voice fidelity, and deliver output with notifications.
Prerequisites
content/config.yamlin the user's project (falls back to plugin defaults if missing)- Python 3.10+ with
feedparserandpyyaml jqandcurlon PATH- Optional:
gwsCLI for Gmail,osascriptfor iMessage
Phase 0: Config Check
Before anything else, check if content/config.yaml exists.
- If it exists: Read it and proceed to Phase 1.
- If it doesn't exist: Tell the user: "No config found. Run
/guide:setupto configure your content pipeline — it takes about 2 minutes and I'll walk you through it." Then stop. Do NOT use the defaults config as a silent fallback for a first run — the user needs to configure their own sources and themes.
Phase 1: Scout (Fetch Sources)
Run the pipeline fetch script to gather all external signals deterministically.
Finding Scripts
The fetch scripts live in this plugin's scripts/ directory. Determine the scripts path:
- If this plugin is at
heart-of-gold-toolkit/plugins/guide/, useheart-of-gold-toolkit/plugins/guide/scripts/ - If installed via marketplace, use the plugin installation path's
scripts/directory - Locate by searching for
fetch-rss.pyin the project tree if unsure
Steps
- Read config from
content/config.yaml - Read voice reference from the path in
voice.referenceconfig field - Run the pipeline fetcher — a single deterministic script that calls ALL configured sources (RSS, Gmail, HN, Events/iCal), combines them, normalizes scores, and writes output:
This script:bash <scripts>/run-pipeline-fetch.sh --config content/config.yaml- Calls each enabled source (fetch-rss.py, fetch-gmail.sh, fetch-hn.sh, fetch-events.py)
- Combines all signals into a single array
- Normalizes scores to 0.0-1.0 with source weight multipliers applied
- Writes
content/pipeline/YYYY-MM-DD/signals.json(with collision avoidance: signals-2.json, etc.) - Writes
content/pipeline/YYYY-MM-DD/fetch-log.jsonwith per-source status - Do NOT run individual fetch scripts yourself. The runner handles all of them.
- Read
signals.jsonfrom the pipeline directory — this is your input for Phase 2 - Read
fetch-log.json— check which sources succeeded/failed. If a source failed, mention it in the brief footer. - Read captures from
content/captures/(or configuredcaptures_dir) — last 7 days of AM/PM captures - Read recent daily briefs — last 3 briefs from
content/daily/for deduplication context
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.
- 12d ago First seen · 442 lines · 26 tokens per session scan A 41f3aee938d9
pipeline is a skill published in the GitHub repository ondrej-svec/heart-of-gold-toolkit (19 stars, last pushed 23d ago), licensed MIT. It adds 26 tokens to every session and 5,471 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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