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 agentmods add skills/dr-code/tessera/debatenpx skills add dr-code/tessera --skill debategit clone --depth 1 https://github.com/dr-code/tesseraWhat 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 | $0.00034 | $0.00690 |
| Opus 5 | $0.00017 | $0.00345 |
| Sonnet 5 | $0.00007 | $0.00138 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
debate 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 yesterday.
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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/debate — Multi-Round Claude vs GPT Debate
Usage
/debate "<topic or question>"
Description
Structured 3-round debate between Claude (native) and GPT (via Codex CLI). Claude and GPT take positions, critique each other, then Claude synthesizes a verdict. Tessera graph context is used when available.
Instructions
Phase 0: Context
- If tessera MCP is configured in this session, call
graph_continuethengraph_retrievewith the debate topic keywords to pull relevant codebase context. - Note any architectural patterns, constraints, or decisions relevant to the topic.
Phase 1: Claude's Initial Position
Formulate a clear stance on the topic with 3 supporting reasons. Include any codebase context found. State what would change your mind.
Phase 2: GPT's Position (Round 1)
Send the topic and context to Codex:
codex exec "You are a senior software architect in a structured debate. Topic: <TOPIC>. Codebase context (if any): <CONTEXT>. Give: (1) your clear stance, (2) top 3 reasons, (3) biggest risk of your approach, (4) what evidence would change your mind. Be direct and specific. No hedging."
Record GPT's full position.
Phase 3: Claude's Critique + GPT's Response (Round 2)
Claude critiques GPT's position: identify flawed assumptions, missing edge cases, or contradictions with project constraints. Then send:
codex exec "Debate round 2. Topic: <TOPIC>. Your position: <GPT_POSITION>. Claude's critique: <CLAUDE_CRITIQUE>. Respond: either defend your stance with new evidence OR concede specific points. If you concede, state what you now believe. Be precise."
Phase 4: Synthesis
Claude produces the final verdict:
- Summarize both positions
- Note where agreement was reached
- Note where genuine disagreement remains
- Issue a clear recommendation with confidence level (high / medium / low)
Phase 5: Lock Decision (if tessera available)
If tessera MCP is active and the debate reached a clear verdict, lock it permanently:
graph_lock_decision(
summary = "<topic>: <verdict and rationale in one sentence>",
scope = "project" | "module",
files = [<files the decision applies to, if any>]
)
This persists the debate outcome so graph_continue surfaces it in future turns and graph_action_summary can reference it. Do NOT call graph_action_summary to record — that is for reading history, not writing. graph_lock_decision is the write path.
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.
- yesterday First seen · 72 lines · 34 tokens per session scan A 5dc96ee1cbfb
debate is a skill published in the GitHub repository dr-code/tessera (1 stars, last pushed 21d ago), licensed MIT. It adds 34 tokens to every session and 690 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…