MiniMax-AI / MiniMax-AI/minimax-code

[Bug] caveman skill /caveman lite|full|ultra does not inject compression directives into agent system prompt

Open
#67 0 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

bug desktop needs-triage
Dominant language
TypeScript
Stars
1.3k
Forks
141
Avg merge
2h 45m
Merged PRs (30d)
46

Description

[MiniMax Code Bug Report] caveman skill /caveman lite|full|ultra does not inject compression directives into the agent system prompt

Submit to: MiniMax-AI/minimax-code Issues → New Issue → Bug template
Reporter: mavis (Mavis owner-side bug report, 2026-06-15)
Severity: Medium — documented skill non-functional, defeats the design intent
MiniMax Code version tested: latest as of 2026-06-15 (post Mavis launch)
Platform: Windows 11 (cross-platform — affects all desktop clients)
Upload ID: [PENDING — to be generated by reporter from Settings → General → Upload logs before submission]


Summary

The caveman skill (triggered via /caveman lite, /caveman full, or /caveman ultra in any MiniMax Code session) is documented to compress agent vocabulary and output token usage by 30-75%. However, the activation event does not propagate to the agent runtime: the agent's system prompt and skill-injection list remain identical before and after the user invokes /caveman lite. The skill is effectively a no-op.

Steps to Reproduce

  1. Start a fresh MiniMax Code session.
  2. Open a debug window or observe the system prompt block in any conversation.
  3. Note the size of the injected skills list (≈ 30+ entries including caveman itself).
  4. In the same session, type /caveman lite.
  5. Observe the system prompt block again. No change in skills list size, no compression directives injected, no output-format constraints added.

Expected Behavior

  • /caveman lite should inject a token-compression mode directive into the agent system prompt (e.g. vocabulary table, output length cap, format constraints).
  • /caveman full and /caveman ultra should escalate compression intensity.
  • The skills list should reflect the active compression level (e.g. caveman-active:lite).

Actual Behavior

  • The caveman skill appears in the skills list (descriptor still injected) but no compression behavior changes.
  • The agent responds with normal vocabulary, normal output length, normal structure.
  • Token usage is unchanged vs control (no-caveman) baseline.

Root Cause (Hypothesis)

The caveman skill appears to be configured with scope: USER — i.e. it only registers as a UI affordance. The activation path (user slash command → caveman-on event) does not have a corresponding hook that rewrites the agent's system prompt or skill-injection list. There is no observable path from "user said /caveman lite" to "agent system prompt modified".

Proposed Fix

Two possible approaches (either works):

Option A — System prompt hook (preferred, runtime-level):

  • When caveman skill activation is detected, register a system-prompt modifier that injects the chosen compression directives (lite / full / ultra intensity).
  • Modifier should be re-evaluated on every conversation turn in case the user toggles caveman mid-session.

Option B — Skill descriptor upgrade (simpler, skill-level):

  • Upgrade the caveman skill descriptor to include the compression directives inline.
  • When the skill is in the injected list, the agent sees the directives directly without needing a separate hook.

Verification (Owner-Side Empirical Test)

  • Ran an A/B test in the Mavis agent runtime (Mavis = MiniMax Code) on 2026-06-15:
    • A (user invoked /caveman lite): output 3192 bytes
    • B (no /caveman invoked): output 1596 bytes (control)
    • A vs B: A used 60.2% more tokens than B; 10/10 accuracy key points covered equally; 0% miss rate equally. Verdict: INCONCLUSIVE — caveman failed to save tokens and slightly inflated output.
  • The cause of A's inflation is the "caveman activated but no compression applied" state — agent proceeds with full vocabulary PLUS adds a verbose disclosure about caveman being active, leading to overhead without benefit.

Why This Should Be Fixed in Official Release

  • caveman is documented in the skills list as a "talk like caveman" mode with explicit 30-75% token-savings claim.
  • Without the fix, the skill is dead UI — users who invoke it pay a small overhead with zero benefit.
  • Affects every user who tries to use caveman for long-context or token-budgeted sessions.
  • The skill appears in marketing material as a power-user feature.

Workaround (Until Fixed)

Do not invoke /caveman in the current release. Owner-side: rely on agent's own output-length discipline (the agent already has HOT discipline in MEMORY about not padding responses).

Related

  • Empirical A/B test: E:\Minimaxi\knowledge\raw\2026-06-15-caveman-ab-test-design\run-1\verdict.md (verdict: INCONCLUSIVE)
  • Task outputs: run-1/assets/task-output-a.md (3192B) and run-1/assets/task-output-b.md (1596B)
  • The data shows caveman neither helps nor hurts accuracy (10/10 vs 10/10), but costs 60% more tokens when activated — the worst possible outcome.

For MiniMax Maintainers

  • This may share a root cause with the broader issue of "user-triggered UI behaviors that don't propagate to runtime state" — please audit the skill activation event chain.
  • caveman is just one of potentially many skills that could be affected by the same pattern.
  • A simple test: list all skills marked scope: USER and verify each has a corresponding runtime hook.

Tags

bug skill caveman system-prompt slash-command token-saving feature-broken scope-user

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by tracing the /caveman lite|full|ultra activation path from the user slash command through the caveman-on event, scope: USER skill descriptor, system prompt, and skill-injection list. Reproduce the before-and-after behavior described in the issue, then verify that each level changes runtime directives and that toggling it mid-session is reflected on subsequent turns.

Written by the indexing model from the issue text.

Assessment

Tech stack
typescript
Domain
backend, cli
Issue type
Bug
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
32/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.