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 ainova-systems/intelligence-sync --skill intelligence-review-skillsgit clone --depth 1 https://github.com/ainova-systems/intelligence-syncWrote 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/ainova-systems/intelligence-sync/intelligence-review-skills)<a href="https://agentmods.dev/skills/ainova-systems/intelligence-sync/intelligence-review-skills"><img src="https://agentmods.dev/badge/skills/ainova-systems/intelligence-sync/intelligence-review-skills.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.02143 |
| Opus 5 | $0.00011 | $0.01071 |
| Sonnet 5 | $0.00004 | $0.00429 |
| Haiku 4.5 | $0.00002 | $0.00214 |
Grade A, and why
intelligence-review-skills 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 7d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review the intelligence layer
Read-only audit of the project's rules, agents and skills, ending in a punch-list. This skill owns the generic audit — everything true of any repository. A project that adds laws of its own layers a thin project audit on top and invokes this one; it never re-implements these checks.
Name reflects the umbrella usage of "skills" for all AI artifacts (rules + agents + skills).
Scope: what to read, and what to leave alone
-
Resolve the layout — never assume folder names. The umbrella is the directory holding
config.yaml; the engine module is the directory under it holdingscripts/sync.shandscripts/VERSION(conventionallysync/). Read authoring conventions from<module>/docs/CONVENTIONS.mdand theintelligence-authoringrule. -
Enumerate from
config.yaml, not from a guessed path. The artifacts are exactly the directories listed undersources.rules,sources.agentsandsources.skills— there may be several groups (e.g. a shared one and a project one), they may be nested, and an@<pack>(or inlinegit+) entry is a remote pack declared underpacks:. Take the list from the config; a literalintelligence/rules/is wrong in any project that named things differently. -
Skip everything the engine owns. Sources under
<module>/(<module>/rules,<module>/agents,<module>/skills/intelligence-*) are upstream-owned:update.shreplaces them wholesale, so a local "fix" there is deleted at the next update. Never propose an edit to them. If one of them is genuinely wrong, or a generic check is missing from this skill, that is a proposal to upstream — say so in the report rather than patching locally. -
Never read or edit generated output (
.claude/,.cursor/,.github/,.codex/,.agents/,.pi/,.opencode/,AGENTS.md). Sync owns those entirely; the finding always belongs to the source.
Steps
- Pull git history (when available) for each artifact — last edit, edit count, first-add date. A stale candidate has no recent edits and nothing cross-referencing it.
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.
- 7d ago First seen · 87 lines · 22 tokens per session scan A 906c9a3b68d0
intelligence-review-skills is a skill published in the GitHub repository ainova-systems/intelligence-sync (4 stars, last pushed 10d ago), licensed MIT. It adds 22 tokens to every session and 2,143 once invoked, about $0.0001 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
agentic-code-review
Use when reviewing a diff, pull request, branch, or AI-generated code for correctness, security, regression, test, performance, and maintainability risks before merge.
repo-health-check
Use when auditing a repo before changes to find the smallest safe PR, quality risks, stale docs, missing tests, ignored-file gaps, or agent setup issues. Best first skill for unfamiliar codebases.
safe-change
Trace, implement, test, and independently review one bounded software change. Use for ordinary feature, bug-fix, or refactor work after the repository is bootstrapped; do not use for broad repository cleanup, production operations, or undefined rewrites.
readiness
Use when the user wants to analyze, audit, or assess their codebase for AI agent readiness. Also use for "readiness report", "how ready is my project", "analyze my codebase", "audit my repo", "check my setup", or "what should I improve".
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation.
superpowers
Use when starting creative work, planning a multi-step task, isolating a workspace, wrapping a branch, or giving/getting code review. Not when the task is a one-line fix with no design surface.