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 jonathan-vella/apex-accelerator --skill golden-principlesgit clone --depth 1 https://github.com/jonathan-vella/apex-acceleratorWrote 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/jonathan-vella/apex-accelerator/golden-principles)<a href="https://agentmods.dev/skills/jonathan-vella/apex-accelerator/golden-principles"><img src="https://agentmods.dev/badge/skills/jonathan-vella/apex-accelerator/golden-principles/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/jonathan-vella/apex-accelerator/golden-principles"><img src="https://agentmods.dev/badge/skills/jonathan-vella/apex-accelerator/golden-principles.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 51 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00089 | $0.00625 |
| Opus 5 | $0.00044 | $0.00313 |
| Sonnet 5 | $0.00018 | $0.00125 |
| Haiku 4.5 | $0.00009 | $0.00063 |
Grade A, and why
golden-principles 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 8d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Golden Principles
These 10 principles govern how every agent operates in this repository. They are adapted from the Harness Engineering philosophy for agent-driven infrastructure development.
Rules
These 10 principles are the agent operating rules. Detailed explanations follow in The 10 Principles.
- Repository Is the System of Record — all context lives in-repo
- Map, Not Manual — instructions point to deeper sources
- Enforce Invariants, Not Implementations — set boundaries, allow autonomous expression
- Parse at Boundaries — validate inputs and outputs at module edges
- AVM-First, Security Baseline Always — prefer Azure Verified Modules + non-negotiable security baseline
- Golden Path Pattern — prefer shared utilities over hand-rolled helpers
- Composable Workflows — small, well-bounded steps that compose
- Human Approval at Critical Gates — explicit gates between steps
- Adversarial Review — challenger subagents stress-test creative outputs
- Continuous Lessons — capture observations and feed back into the system
Steps
Applying the principles to a new agent or skill:
- Read all 10 principles before designing the agent or skill
- For each design decision, ask which principles apply (typically 2–3 will dominate)
- Run the per-principle test listed in The 10 Principles
- Where a principle conflicts with an implementation choice, change the implementation — principles are non-negotiable
- Document deviations in an ADR if a principle was knowingly relaxed
The 10 Principles
Each principle has a non-negotiable rule and a quick test for compliance. The
canonical detail (full text + per-principle tests + the "How to Apply These
Principles" section for agents, contributors, and code review) lives in
references/principles.md. The summary list above
is a one-line index; for any decision-making use, load the reference. The
two sources are kept in sync — if you spot drift, update the reference and
sync the summary.
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.
- 8d ago First seen · 54 lines · 89 tokens per session scan A 5b692c6e795a
golden-principles is a skill published in the GitHub repository jonathan-vella/apex-accelerator (50 stars, last pushed 5d ago), licensed MIT. It adds 89 tokens to every session and 625 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 2026-09-03.
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