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 Nagiliant/Genesis-Legacy-V2 --skill genesis-a4git clone --depth 1 https://github.com/Nagiliant/Genesis-Legacy-V2Wrote 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/nagiliant/genesis-legacy-v2/genesis-a4)<a href="https://agentmods.dev/skills/nagiliant/genesis-legacy-v2/genesis-a4"><img src="https://agentmods.dev/badge/skills/nagiliant/genesis-legacy-v2/genesis-a4/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/nagiliant/genesis-legacy-v2/genesis-a4"><img src="https://agentmods.dev/badge/skills/nagiliant/genesis-legacy-v2/genesis-a4.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00048 | $0.00749 |
| Opus 5 | $0.00024 | $0.00375 |
| Sonnet 5 | $0.00010 | $0.00150 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
A4 — North Star Direction 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 11d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A4 — North Star Direction (Gap Analysis)
The vision-gap check — and emphatically not a passive alignment scorecard. A4 asks: compared to the North Star (the perfect-world vision for this feature), what is missing, drifting, or underbuilt — and what should we add, build, or change now to close that gap? For every quality dimension it asks "what would perfect look like here, and what is the delta?" and produces concrete improvements to make, not checkmarks. This lens requires top-tier judgment.
Inputs
northstar.md(required — refuse if missing) · the implementation code in scope · the micro specs ·design.md- The constitution · prior A1–A3 reports · the project mode
- Accumulated
domain-learnings.mdfrom prior A4 runs (extra, project-earned quality expectations)
Process
- Read the North Star deeply — internalize every dimension, compass question, and emotional quality; understand what "perfect" means for this feature.
- Walk each quality dimension (weighted Critical / Important / Nice). For each: what does perfect look like for this implementation? what is the current state? what is the gap? Score Aligned / Drifting / Missing. For anything drifting or missing, state the specific change that closes the gap. Critical-dimension gaps are urgent.
- Walk the compass questions — apply each to the implementation's decisions; flag any that fail, and state what passing would look like.
- Assign severity by dimension weight: Critical → High/Critical · Important → Medium · Nice → Low (still fixed).
- Make every finding actionable — never "this doesn't feel right," always a concrete change (e.g. "the settings panel shows 12 options at once; the North Star calls for cognitive-load minimization — group into 3 categories of 4").
- Capture domain learnings — record domain-specific quality observations that emerged from the work itself, appended to
domain-learnings.md, tagged with the feature/domain. They accumulate: each future A4 run reads them as additional expectations. This is how the audit gets smarter about a domain over time. - Write the report to
audits/{scope}-a4.md: per-dimension Aligned/Drifting/Missing with gap analysis, compass-question results, actionable findings, captured observations, and an overall direction assessment.
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.
- 11d ago First seen · 42 lines · 48 tokens per session scan A d36925928418
A4 — North Star Direction is a skill published in the GitHub repository Nagiliant/Genesis-Legacy-V2 (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 749 once invoked, about $0.0002 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 2026-08-31.
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