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 key4ng/lite-cc --skill ensemble-reviewgit clone --depth 1 https://github.com/key4ng/lite-ccWrote 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/key4ng/lite-cc/ensemble-review)<a href="https://agentmods.dev/skills/key4ng/lite-cc/ensemble-review"><img src="https://agentmods.dev/badge/skills/key4ng/lite-cc/ensemble-review.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 | $0.00019 | $0.00337 |
| Opus 5 | $0.00010 | $0.00169 |
| Sonnet 5 | $0.00004 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
ensemble-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 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.
What it actually says
Ensemble Review
Steps
-
Get the diff to review:
- Run
git diff main...HEADvia bash to get the current branch's changes - If no changes found, tell the user there are no changes to review
- Run
-
Spawn 3 subagents, each with a different model. Give each the diff as part of the prompt:
Models to use:
oci/openai.gpt-5.4oci/google.gemini-2.5-flashoci/xai.grok-code-fast-1
Use
spawn_subagentfor each model with this prompt (include the diff inline):Review this code diff. Identify bugs, security issues, performance problems, and readability concerns. For each issue, provide the file, line, severity (critical/warning/info), and a brief explanation.
Use tools: ["read_file", "list_files", "grep"] so reviewers can check surrounding code.
-
Collect all 3 reviews. Note any subagents that failed and which models succeeded.
-
Synthesize a unified report:
- Consensus issues: flagged by 2+ models (high confidence)
- Unique findings: flagged by only 1 model (review manually)
- Summary: overall assessment combining all perspectives
-
Present the report to the user.
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 · 40 lines · 19 tokens per session scan A 635f5e96c3b1
ensemble-review is a skill published in the GitHub repository key4ng/lite-cc (4 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 337 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.
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