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 Ingramradical235/anty-framework --skill review-enginegit clone --depth 1 https://github.com/Ingramradical235/anty-frameworkWrote 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/ingramradical235/anty-framework/review-engine)<a href="https://agentmods.dev/skills/ingramradical235/anty-framework/review-engine"><img src="https://agentmods.dev/badge/skills/ingramradical235/anty-framework/review-engine/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/ingramradical235/anty-framework/review-engine"><img src="https://agentmods.dev/badge/skills/ingramradical235/anty-framework/review-engine.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 | $0.00066 | $0.01558 |
| Opus 5 | $0.00033 | $0.00779 |
| Sonnet 5 | $0.00013 | $0.00312 |
| Haiku 4.5 | $0.00007 | $0.00156 |
Grade A, and why
review-engine 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 9d 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.
This is a copy
100% identical to review-engine — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Engine
When to Apply
- After each periodic scan
- When buffer zone changes
- When Actions complete but Goal doesn't progress
- Quarterly strategy reviews
- When the founder asks "why isn't this working?"
Core Framework
5-Question Review (Strict Priority Order)
Start at Question 1. Only proceed deeper if the answer is "yes" (problem isn't at that level):
| Priority | Question | Scope | If "No" |
|---|---|---|---|
| 1 | Has the Goal progressed since last review? | WHERE | Re-run WHERE analysis to locate new gap |
| 2 | Were approved Actions executed as planned? | Execution | Investigate blockers, check relay runner compliance |
| 3 | Was the Action plan well-constructed? | Planning | Review Step breakdown, batch config, template fit |
| 4 | Were the chosen Actions appropriate? | HOW | Re-run 3-option choice with different approaches |
| 5 | Was the root-cause analysis correct? | WHY | Rebuild causal structure diagram from scratch |
This prevents the common trap of never questioning upstream assumptions.
KPI Propagation Monitoring (4-Layer)
Actions are monitored for whether effects propagate upward through the causal chain:
Action: "Post 4 thought-leadership threads per week"
|
KPI Layer 1 (Execution): Threads posted per week
-> "Is the Action being executed?" (target: 4/week)
|
KPI Layer 2 (Propagation): Engagement rate per thread
-> "Is it producing the expected intermediate effect?"
|
KPI Layer 3 (Driver metric): Monthly new followers
-> "Is the Driver metric moving?"
|
KGI (Goal metric): Paying customers
-> "Is it reaching the Goal?"
4-Layer Anomaly Patterns:
| Pattern | Diagnosis | Response |
|---|---|---|
| Layer 1 failing | Action not being executed | Check blockers, re-prioritize, enforce relay runner |
| L1 OK, L2 stalling | Action executed but ineffective | Trigger WHY analysis on L1-L2 gap |
| L1-2 OK, L3 stalling | Intermediate effect not converting | KPI tree may need re-decomposition |
| All layers OK, KGI stalling | Causal model may be wrong | Full review (Question 5) |
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.
- 9d ago First seen · 152 lines · 66 tokens per session scan A ad0ce5606c99
review-engine is a skill published in the GitHub repository Ingramradical235/anty-framework (1 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 1,558 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to review-engine, differing in 0 lines, and is treated as a copy.
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