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 yeaight7/agent-powerups --skill tri-model-reviewgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/tri-model-review)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/tri-model-review"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/tri-model-review/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/yeaight7/agent-powerups/tri-model-review"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/tri-model-review.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 | $0.00034 | $0.00528 |
| Opus 5 | $0.00017 | $0.00264 |
| Sonnet 5 | $0.00007 | $0.00106 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
tri-model-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 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tri-Model Review
Tri-model review routes through two external advisor CLIs, then synthesizes both outputs into one answer.
Use this when you want parallel external perspectives.
When to Use
- Backend/analysis + frontend/UI work in one request
- Code review from multiple perspectives (architecture + design/UX)
- Cross-validation where different models may disagree
- Fast advisor-style parallel input without full team runtime orchestration
Requirements
- Ensure you have configured the appropriate
apx ask-*wrappers. - If either wrapper is unavailable, continue with whichever provider is available and note the limitation.
How It Works
1. Decompose the request into two advisor prompts:
- Analysis/architecture/backend prompt
- UX/design/docs/alternatives prompt
2. Run both advisors via the canonical wrappers:
- apx ask-codex "<prompt>"
- apx ask-gemini "<prompt>"
3. Synthesize both outputs into one final response
Execution Protocol
When invoked, follow this workflow:
1. Decompose Request
Split the user request into:
- Architecture prompt: correctness, backend, risks, test strategy
- UX prompt: content clarity, alternatives, edge-case usability, docs polish
- Synthesis plan: how to reconcile conflicts
2. Invoke advisors via Bash
Run both advisors via the Bash tool:
apx ask-codex "<architecture prompt>"
apx ask-gemini "<UX prompt>"
3. Synthesize
Return one unified answer with:
- Agreed recommendations
- Conflicting recommendations (explicitly called out)
- Chosen final direction + rationale
- Action checklist
Fallbacks
If one provider is unavailable:
- Continue with available provider + synthesis
- Clearly note missing perspective and risk
If both unavailable:
- Fall back to a single-model answer and state external advisors were unavailable.
Verification
- Request was decomposed into distinct architecture and UX prompts before invoking advisors
- Both advisors were invoked via the canonical wrappers — or the missing provider was explicitly noted
- Synthesis lists agreed recommendations, explicitly called-out conflicts, the chosen direction with rationale, and an action checklist
- Any provider fallback was stated, including the missing perspective and its risk
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 · 86 lines · 34 tokens per session scan A 8879d95faa77
tri-model-review is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 528 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
github-code-review
Review PRs: diffs, inline comments via gh or REST.
simplify-code
Sequential 3-lens cleanup of recent code changes.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
hqe
Comprehensive codebase health auditing, remediation, and verification skill based on the canonical HQE Protocol v5.0.0.
kodama-constraints
Enforce non-negotiable safety, scope, security, and quality constraints for implementation and review work.
adk-review
Reviews the uncommitted changes in an adk-python working tree and reports correctness, design, public-API stability, test, sample and documentation gaps as a prioritized findings report, fixing them only when asked. Use when the user asks to review local changes, wants a self-review before opening a pull request, asks…