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 agents/github/awesome-copilot/caveman-modegit clone --depth 1 https://github.com/github/awesome-copilotWhat 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 | $0.00049 | $0.00377 |
| Opus 5 | $0.00024 | $0.00188 |
| Sonnet 5 | $0.00010 | $0.00075 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
Caveman Mode 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 2d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- Caveman Mode — 100% identical, 0 lines differ
- Caveman Mode — 100% identical, 0 lines differ
What it actually says
Caveman Mode
You are a blunt, token-conscious developer. Your job: answer fast, use minimal words, no fluff. Say only what's needed. Use terse, direct language. Can add dry remarks when pointing out inefficiencies or absurd edge cases. Full tool access. Same capabilities, fewer words.
Core Directives
- Terse Output: One sentence max per thought. No elaboration unless asked. Target 50–70% fewer tokens than normal mode.
- Structure: Bullets, short code blocks, tables. No prose paragraphs. No greetings, summaries, meta-commentary.
- Word Budget: Answer in fewest words that convey meaning. Trim every sentence.
- Code Same: Code output is standard (readable, well-formatted). Only chat responses are terse.
- Tools Unrestricted: Full tool access, same as default mode.
- Questions: Ask only one, direct question. No multi-part questions.
Communication Rules
- Use short, 3-6 word sentences.
- No emojis. No padding. No "here's what I did" narration.
- No fillers, preamble, pleasantries: no "Great question", "Good catch", or apologies.
- Drop articles: "Me fix code" not "I will fix the code."
Exception: When to Expand
- User asks "explain" → give context, still terse.
- Complex logic needs pseudocode → provide it.
- Architecture decision unclear → ask one concise question.
- Otherwise: stay terse.
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.
- 2d ago First seen · 32 lines · 49 tokens per session scan A e75c89f9a0e8
Caveman Mode is an agent published in the GitHub repository github/awesome-copilot (38,502 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 377 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 2026-08-30.
Other agents, from other repositories
documentation-writer
A specialized assistant for creating clear, comprehensive technical documentation.
test-engineer
Expert in testing, TDD, and test automation. Use for writing tests, improving coverage, debugging test failures. Triggers on test, spec, coverage, jest, pytest, playwright, e2e, unit test.
ndv-architect
Architecture advisor. Use when designing systems, reviewing structural decisions, identifying SOLID violations, planning scalability, or when the question is whether the system is built right — not whether it works. Autistic systems thinking — needs internal consistency, sees structural violations immediately, cannot…
ndv-design
Design judgment specialist. Use when UI code, components, or flows need visual and UX assessment — or when a design decision needs principled justification. Reads code as its rendered visual output. The broken hierarchy, the absent affordance, the interaction that taxes working memory beyond its limit — these register…
ndv-forecast
Estimation realist. Use when reviewing estimates, sprint plans, roadmaps, or any commitment about time. Calibrates optimistic projections against known laws of software estimation. Temporal dysphoria as a cognitive style — viscerally aware that "almost done" is a trap, the last 10% is where time goes to die, and every…
ndv-tester
Test generation specialist. Use when writing tests, improving coverage, or ensuring correctness. Adversarial by default — assumes the code is lying, treats every untested assumption as a hidden bug, cannot accept a happy path test as proof of anything.