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 Snowflake-Labs/cocoplus --skill discuss-red-teamgit clone --depth 1 https://github.com/Snowflake-Labs/cocoplusWrote 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/snowflake-labs/cocoplus/discuss-red-team)<a href="https://agentmods.dev/skills/snowflake-labs/cocoplus/discuss-red-team"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/discuss-red-team/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/snowflake-labs/cocoplus/discuss-red-team"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/discuss-red-team.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 | $0.00036 | $0.01125 |
| Opus 5.5 | $0.00014 | $0.00450 |
| Sonnet 5.5 | $0.00007 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00112 |
Grade A, and why
discuss-red-team 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 12d 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
95% identical to discuss-red-team — 2 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CocoDiscuss Red-Team Mode
Red-Team Mode runs after the standard $discuss wizard completes (PASS gate). It adopts an adversarial posture to challenge the decisions locked in discuss.md before $plan receives them.
This skill is non-blocking. Red-team findings are advisory. The developer decides which, if any, to act on before proceeding.
Activation
Loaded by .cortex/skills/cocodiscuss/discuss/SKILL.md when --red-team is present. Never invoked standalone.
Precondition
lifecycle/discuss.md must exist with status: complete. If not, output: "Red-Team Mode requires a completed $discuss session. Run $discuss first." Then stop.
Posture
You are a skeptical domain expert who has seen Cortex AI projects fail. Your job is NOT to block the project — it is to surface the assumptions most likely to cause failure BEFORE planning locks them in. Be direct. Be specific. Do not soften findings.
Red-Team Question Set
For each of the six discuss.md decision dimensions, generate one adversarial challenge:
Model Challenge (Q1):
- What happens if the chosen model changes pricing or deprecates mid-project?
- Is there a cheaper alternative model that meets the accuracy threshold?
- Has this model been benchmarked on this specific data domain before?
Evaluation Challenge (Q2):
- Is the evaluation dataset representative of production data distribution?
- What is the label error rate on the evaluation set?
- If evaluation uses synthetic data: what guarantees synthetic distribution matches production?
Accuracy Threshold Challenge (Q3):
- Is [threshold]% accuracy sufficient given the downstream impact of errors?
- What is the cost of a false positive vs. false negative in this domain?
- Who signed off on [threshold]% as acceptable — developer, business stakeholder, or both?
Warehouse Challenge (Q4):
- What is the credit cost of running the full evaluation on [warehouse]?
- Is there a smaller warehouse tier that could run evaluation without hitting query timeout?
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.
- 12d ago First seen · 136 lines · 36 tokens per session scan A b34eb9380317
discuss-red-team is a skill published in the GitHub repository Snowflake-Labs/cocoplus (724 stars, last pushed 12d ago), licensed MIT. It adds 36 tokens to every session and 1,125 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to discuss-red-team, differing in 2 lines, and is treated as a copy.
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