Use this skill whenever the user wants to call DeepSeek V4-Pro / V4-Flash (or its legacy aliases deepseek-chat / deepseek-reasoner), or you see code that imports `from openai import OpenAI` with `base_url="https://api.deepseek.com"`. This skill teaches you the 10 protocol contract rules required to avoid the 16 documented bugs (reasoning_content lifecycle 400, tool-call leakage, max_tokens runaway → V8 string limit, parallel tool delta interleave, prefix-cache invalidation, /beta endpoint remap, etc.) and ships a single drop-in `safe_init.py` script you can copy into the user's project.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Use this skill whenever the user wants to call DeepSeek V4-Pro / V4-Flash (or its legacy aliases deepseek-chat / deepseek-reasoner), or you see code that imports `from openai import OpenAI` with `base_url="https://api.deepseek.com"`. This skill teaches you the 10 protocol contract rules required to avoid the 16 documented bugs (reasoning_content lifecycle 400, tool-call leakage, max_tokens runaway → V8 string limit, parallel tool delta interleave, prefix-cache invalidation, /beta endpoint remap, etc.) and ships a single drop-in `safe_init.py` script you can copy into the user's project.
when_to_use
Any of these triggers should invoke this skill:
• user mentions DeepSeek, deepseek-v4-pro, deepseek-v4-flash, deepseek-chat, deepseek-reasoner, or 深度求索
• code references `https://api.deepseek.com` or `https://api.deepseek.com/anthropic` or `/beta`
• user complains about `400 reasoning_content`, `Invalid string length`, `RangeError`, hangs in chat clients, or "weird tool call behaviour"
• user wants the cheapest possible LLM hosting that is also smart (V4-Flash $0.14/M input miss, $0.0028/M cached → 50× discount)
license
MIT
version
0.2.0
author
Henry Zhang (HenryZ838978)
homepage
https://github.com/HenryZ838978/deepseek-harness
DeepSeek V4 Harness
When you call DeepSeek V4-Pro or V4-Flash via the OpenAI-compatible API, you MUST follow the 10 contract rules below. Violating any one of them causes a documented production bug (the upstream issue and our reproduction probe are cited per rule).
The 10 contract rules (ranked by blast radius)
C1 · Disable thinking unless the task is genuinely reasoning-heavy
deepseek-v4-pro defaults to thinking=enabled. Every call then burns 30-300 reasoning_tokens even on trivial prompts.
client.chat.completions.create(
model="deepseek-v4-pro",
messages=messages,
extra_body={"thinking": {"type": "disabled"}}, # ← saves money on every non-reasoning call
)
For TypeScript / openai-node, put thinking at the top level of the request, not inside extra_body (the JS SDK passes unknown top-level keys through):
C2 · In multi-turn loops, preserve reasoning_content on assistant messages
If thinking IS enabled and you re-send a prior assistant message that has tool_calls, you must include the original reasoning_content field. Otherwise the next request returns:
HTTP 400: The reasoning_content in the thinking mode must be passed back to the API.
(Reproduced in reports/probes/probe_2_reasoning_lifecycle.py 3/3 trials on V4-Pro and V4-Flash, 2026-05-09.)
When a NEW user turn arrives, you MAY strip reasoning_content from prior assistant messages — DeepSeek doesn't require it across user-turn boundaries, and keeping it bloats the prefix-cache key.
C3 · Always set max_tokens (default 4096)
Without an output cap, reasoning_content can stream 8000+ chunks (probes/probe_9_reasoning_runaway.py measured 26 KB / 84 s on a self-doubt prompt) and downstream Electron clients (ChatWise, Cherry Studio) crash with RangeError: Invalid string length once their string buffer hits V8's 512 MB ceiling.
C4 · Streaming: aggregate parallel tool_calls by tc.index, not list order
DeepSeek interleaves chunks across parallel tool calls (probe_7 100% interleave on 3/3 V4-Pro and V4-Flash trials). Use a dict[int, slot]:
tool_call_acc: dict[int, dict] = {}
for chunk in stream:
for tc in (chunk.choices[0].delta.tool_calls or []):
slot = tool_call_acc.setdefault(tc.index, {"id": None, "name": None, "arguments": ""})
if tc.id: slot["id"] = tc.idif tc.function and tc.function.name: slot["name"] = tc.function.name
if tc.function and tc.function.arguments: slot["arguments"] += tc.function.arguments
C5 · Streaming: list buffer + "".join, NOT state += chunk
DeepSeek streams 1-3 chars per reasoning chunk. state.text += chunk is O(n²) string allocation:
buf = []
for chunk in stream:
if c := (chunk.choices[0].delta.content or""):
buf.append(c)
final = "".join(buf)
C6 · Tolerate empty stream chunks
DeepSeek emits ~3 chunks per response with choices == []. Check truthiness before indexing:
for chunk in stream:
choices = chunk.choices or []
ifnot choices:
if chunk.usage isnotNone: usage = chunk.usage
continue
...
C7 · Cap context length under 1,048,576 tokens
The V4-Pro / V4-Flash hard ceiling is exactly 2^20 = 1,048,576 tokens (probe_6b validated). The server enforces len(messages_tokens) + max_tokens <= 1,048,576 and returns 400 if exceeded.
C8 · Cache awareness — do NOT inject volatile content into the system prompt
DeepSeek's prefix cache buckets in 256-token blocks and gives a 50× discount on hits. To maximise:
Do NOT prune or summarise history aggressively (every prune = cache miss)
Do NOT inject "current date: 2026-05-09" or similar into the cached prefix
Do read both prompt_cache_hit_tokens (DeepSeek-native) AND prompt_tokens_details.cached_tokens (OpenAI-shape) — both are returned
C9 · Avoid /beta endpoint when calling V4 with tools
/beta silently remaps deepseek-v4-pro → legacy deepseek-reasoner, which rejects specific tool_choice={"type":"function","function":{"name":"..."}}. Use https://api.deepseek.com for tool-using flows.
C10 · strict: true is empirically OK on V4 (despite #1069 still being open)
You MAY enable function.strict=true and additionalProperties=false on V4-Pro / V4-Flash — the historic JSON-corruption bug (deepseek-ai/DeepSeek-V3#1069) was not reproducible in 32 trials on 2026-05-09. Still validate JSON post-hoc with jsonschema.
Drop-in implementation
If the user's environment allows installing third-party libraries, use any of:
form
install
command
Python lib
pip install deepseek-harness
from deepseek_harness import DeepSeekHarness
Python CLI
pip install deepseek-harness-cli
dsh chat, dsh doctor, dsh validate
MCP server
npx -y @deepseek-harness/mcp
wire into Claude/Cursor/Cline/ChatWise MCP config
If they want zero deps beyond openai, copy scripts/safe_init.py (in this skill folder) into their project. It implements all 10 rules in 200 lines.
Reproduction commands
Every claim in this skill is backed by a runnable probe in reports/probes/:
# Reproduce the reasoning_content 400:
python reports/probes/probe_2_reasoning_lifecycle.py --n 3
# Expected: 3/3 phase-B BadRequestError with the verbatim error message above.# Confirm tool-call leakage rate at 0% on official endpoint:
python reports/probes/probe_3_tool_call_leakage.py --n 30
# Map cache prefix sensitivity:
python reports/probes/probe_5_cache_prefix_sensitivity.py --n 8
