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-a2git 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-a2)<a href="https://agentmods.dev/skills/nagiliant/genesis-legacy-v2/genesis-a2"><img src="https://agentmods.dev/badge/skills/nagiliant/genesis-legacy-v2/genesis-a2/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-a2"><img src="https://agentmods.dev/badge/skills/nagiliant/genesis-legacy-v2/genesis-a2.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 | $0.00039 | $0.00584 |
| Opus 5 | $0.00019 | $0.00292 |
| Sonnet 5 | $0.00008 | $0.00117 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
A2 — Spec 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 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A2 — Spec Review (FR/AC Compliance)
The mechanical compliance check. A2 walks every Functional Requirement (FR) and every Acceptance Criterion (AC) in the master spec and renders a binary verdict — Pass or Fail — against the implementation, with evidence. It makes no judgment about code quality (that is A3), direction (A4), or architecture (A1). Exhaustive and non-judgmental: each requirement either is satisfied by the code or it is not.
Inputs
spec.md(required — the source of all FRs/ACs) · all micro specs in scope · the actual code files they reference- The constitution · prior audit reports for this scope (e.g. the A1 report) for cumulative context · the project mode
Process
- Build the requirement checklist: extract every FR and AC that applies to this scope (for a group, the FRs/ACs its micro specs cover).
- Scan each file against each requirement — an exhaustive file-by-file pass. Produce a per-requirement verdict (Pass/Fail) with specific evidence.
- Review the scan for accuracy — correct false positives and false negatives, add context. (Two stages: a fast exhaustive pass, then a careful review pass.)
- (Brownfield) same File-Operations-table scoping and Inherited/Introduced classification as A1.
- Write the report to
audits/{scope}-a2.md: a coverage table listing every FR/AC with Pass/Fail and its evidence, a finding per failure with reasoning trace, and an overall verdict.
Discipline
A2 is binary and evidence-bound. A well-formed finding cites (1) the exact FR/AC ID with its wording, (2) the implementation evidence that fails it, and (3) a Pass/Fail verdict with no ambiguity. If a requirement is itself ambiguous, A2 flags the ambiguity rather than guessing intent — it never invents a passing or failing interpretation to resolve unclear spec language.
Shared machinery
Finding format, the zero-tolerance loop (loops to zero findings), and conviction scoring are shared across all lenses — see docs/the-audit-system.md. A2 may run alongside A1, but A3 still waits on A1 being clean.
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 · 39 lines · 39 tokens per session scan A 045cd62f5996
A2 — Spec Review is a skill published in the GitHub repository Nagiliant/Genesis-Legacy-V2 (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 584 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.
Other skills, from other repositories
repo-consistency-sweep
Proactive defect-class detection that handles the lower-value half of code review (per Bacchelli and Bird 2013) so human reviewers stay focused on design, intent, and knowledge transfer. Catches convention drift, ordering bugs, type-safety gaps, security and multi-tenant invariants (CWE-grounded), and operability…
pr-feedback-ingest
Turn PR feedback (Greptile, CI, bots, humans) into a structured traceable backlog aligned with TASKSTATE.md, DECISIONS.md, and IMPLEMENTATIONPLAN.md so the next execution step can be a narrow implement-approved-slice or a small planning touch without losing alignment. Corrective scope only. Use when a PR is open or…
review-hard
Review the current task changes for real correctness, safety, and maintainability risks before slice closure or PR prep, and recommend the smallest safe next step. Surfaces meaningful issues (not cosmetic feedback); not a replacement for external review systems. Returns no-op when the review would not materially…
verify-against-rubric-fleet
Orchestrator-workers generalization of verify-against-rubric to N=many artifacts against ONE locked rubric. Dispatches N stateless Sonnet sub-agents in parallel; each receives ONE artifact + the SAME rubric + read-only tools (no TASKSTATE, no DECISIONS, no prior history); returns structured per-criterion verdict.…
atom-audit-fleet
Orchestrator-workers variant of atom-audit. Dispatches N Haiku workers (3-5 atoms each) to audit every atom under packages/design-system/src/atoms/ in parallel against COMPONENTGUIDELINES.md rules; merges per-worker rows into ATOMAUDIT.md table. Use when atom count >= 6 (per cost-effectiveness threshold) AND…
graphql-contract-review
Review a GraphQL schema and a Backend-for-Frontend (BFF) contract BEFORE implementation, against a GraphQL-specific checklist: schema shape and nullability (null-bubbling), errors-as-data unions, N+1 and DataLoader, query cost and depth limits, cursor-connection pagination, federation entity ownership, breaking-change…