kv-tool-loop-stability

Testing & Quality

Use this skill when certifying mesh-llm KV/cache stability under repeated OpenAI tool-call loops, same-prefix cache reuse, suffix-prefill limits, or native Skippy slot/decode/eviction failures.

QUICK START

How to use this skill

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Open your project in Codex.
Copy the prompt below and paste it into your agent.
Review the proposed files and risks before you approve installation.

Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/Mesh-LLM/mesh-llm/blob/HEAD/.agents/skills/kv-tool-loop-stability/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/kv-tool-loop-stability/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

KV Tool-Loop Stability

Use this skill when changing Skippy KV slot cleanup, prefix-cache lookup, OpenAI tool-loop behavior, agent harnesses, or any runtime path related to llama_decode failed, failed to find a memory slot, low same-prefix cache reuse, or proactive eviction failures.

Workflow

Attach to an existing OpenAI-compatible /v1 endpoint. This harness does not start nodes, load models, join meshes, or change routing policy.
Prefer a direct model when reproducing Skippy KV/cache issues. Use auto only when intentionally validating routed behavior.
Run --print-plan first and confirm the models, attempts, pressure_turns, timeout, cache thresholds, output directory, and native logs.
Pass the active Skippy native log when available. The harness checkpoints native logs at run start and scans only appended bytes.
Preserve the evidence directory: manifest.json, results.jsonl, summary.json, summary.md, and transcripts/*.jsonl.

Commands

Preview the run without touching the endpoint:

scripts/qa-kv-tool-loop-stability.py \
  --base-url http://127.0.0.1:9337/v1 \
  --models Qwen/Qwen2.5-3B-Instruct-GGUF:q4_k_m \
  --attempts 5 \
  --pressure-turns 8 \
  --timeout 180 \
  --min-cached-tokens 2048 \
  --suffix-prefill-limit 256 \
  --native-log ~/.mesh-llm/runtime/<pid>/logs/skippy-native.log \
  --output-dir target/kv-tool-loop-stability/local \
  --print-plan

Run the certification:

scripts/qa-kv-tool-loop-stability.py \
  --base-url http://127.0.0.1:9337/v1 \
  --models Qwen/Qwen2.5-3B-Instruct-GGUF:q4_k_m \
  --attempts 5 \
  --pressure-turns 8 \
  --timeout 180 \
  --min-cached-tokens 2048 \
  --suffix-prefill-limit 256 \
  --native-log ~/.mesh-llm/runtime/<pid>/logs/skippy-native.log \
  --output-dir target/kv-tool-loop-stability/local

Reporting Rules

Report the model list, attempts, pressure turns, timeout, cache thresholds, success rate, native log paths, and output directory.
Include the summary verdict and failing phase details from summary.md or summary.json.
Do not paste full prompts, auth headers, huge stable prefixes, or private endpoint data.
If no native log is available, say that native-log scanning was not run.

Validation

When changing this harness, run:

python3 -m unittest scripts.tests.test_qa_kv_tool_loop_stability
python3 -m py_compile scripts/qa-kv-tool-loop-stability.py scripts/tests/test_qa_kv_tool_loop_stability.py