How to install DeepSeek Stack, a free 29-skill pack that pushes DeepSeek V4.1 Flash closer to Claude Opus level coding, step by step.
What is DeepSeek Stack? #
DeepSeek Stack is a free, open source collection of 29 skills (MIT licensed) built to make DeepSeek V4.1 Flash behave more like a disciplined senior engineer instead of a model that rushes into code. It installs with a single command through the skills CLI and works with agents that support the skill format, including Claude Code, Codex, Kline, and Open Code. The pack doesn’t change the underlying model. It changes its habits: planning before coding, verifying before claiming success, and stopping when stuck instead of guessing again.
TL;DR #
- DeepSeek Stack is a 29-skill, MIT-licensed pack designed to close the gap between DeepSeek V4.1 Flash and Claude Opus on everyday coding tasks.
- The core problem it fixes isn’t model intelligence, it’s bad habits : skipping plans, claiming “fixed” without running tests, and repeating failed approaches.
- Installation is a single
npx skills addcommand, and skills are plainskill.mdfiles, so they’reagent-agnostic by design. - Thinking effort matters enormously : turning off extended thinking on DeepSeek V4.1 Flash reportedly dropped performance by close to 30 points in testing.
- DeepSeek’s pricing (far below Opus on both input and output tokens) makes it economical to run multiple attempts, extra review passes, and repeated test cycles on the same task.
- Pairing DeepSeek with Cline Pass (a $9.99/month plan offering access to several open models) enables multi-model review loops, where different models catch different mistakes.
- The pack splits into five functional groups: core, loops, build, ship, and understand , covering everything from planning and verification to pull request automation and UI design review.
pass the audit.
Why does DeepSeek need a skill pack to compete with Opus? #
DeepSeek V4.1 Flash is capable, but out of the box it tends to jump straight into coding without restating the task, skip running tests before declaring a fix complete, and retry the same broken approach with minor tweaks instead of stepping back. On long tasks, it can also lose track of the original goal as context fills up with old tool output.
None of this is a reasoning failure. It’s a workflow failure. A skill pack can’t make a model smarter, but it can force it to plan before acting, check its own work with real commands instead of assumptions, and recognize when it’s stuck rather than burning more attempts on a dead idea. That’s the entire premise behind DeepSeek Stack: close the behavioral gap, not the intelligence gap.
How much cheaper is DeepSeek V4.1 Flash than Opus? #
The pricing difference is large enough to change how you approach a coding session. Claude Opus runs around $4 per million input tokens and $20 per million output tokens. DeepSeek V4.1 Flash, at off-peak hours, runs about 15 cents per million input tokens and 60 cents per million output tokens, with cached input costing a fraction of a cent per million. Even during peak hours, when DeepSeek’s prices roughly double, it remains more than 13 times cheaper than Opus on input and more than 16 times cheaper on output.
That price gap changes the strategy. With Opus, you ration tokens because every attempt is expensive. With DeepSeek, you can afford to run the same task three times and pick the best result, get a second opinion in a fresh context, or rerun a full test suite without worrying about the bill. A large share of DeepSeek Stack’s skills are built specifically around spending cheap tokens generously to buy back quality.
How do you install DeepSeek Stack? #
Installation happens from a project’s terminal with:
npx skills add ai-cookbook/ds-stack
The installer lists available skills and asks which coding agents are in use (Claude Code, Codex, Kline, Open Code, and others). Adding a global flag installs the pack across every project instead of just the current one, and a single-skill flag lets you install just one skill by name (for example, the verify loop) instead of the full set.
Every skill name is prefixed with ds to avoid collisions with other skill packs already installed, and each one is a plain skill.md file, which is why the pack works across most agents that support the skill format at all.
For running DeepSeek directly, the setup involves installing DeepSeek’s own harness (DSH) via npm, then connecting it through a coding agent interface: pasting a DeepSeek API key into provider settings, starting a new task, selecting V4.1 Flash as the model, and critically, setting thinking effort to maximum. According to testing behind the pack, disabling extended thinking cost close to 30 points of performance, more than any single skill in the collection.
What do the core and loop skills actually do? #
Rapidlybuild enterprise-grade software applications.
The core group establishes baseline behavior. One skill acts as an always-on working style: restate the task in one line, read code before editing it, make the smallest viable change, run a real command after every edit, and never claim a task is done without showing the actual output. It also enforces a rule against retrying the same fix a third time after two failures. A companion skill provides a library of engineering principles (fixing root causes instead of symptoms, deleting before adding, validating at system edges) that other skills reference when needed. A help skill recommends which of the 29 skills fits a given task, useful given how large the pack is.
The loop group is where the biggest behavioral gains show up. A planning loop forces the model to write a numbered plan with verifiable checks for multi-file tasks, then review and revise that plan before writing any code. A verification loop runs the actual check, reads the actual error, fixes the root cause, and repeats up to five times before stopping and reporting honestly, without weakening tests to force a pass. An “unstick” skill kicks in when the same error resurfaces twice: it isolates the shared assumption behind the failed fixes, tests that assumption directly, and if confirmed wrong, proposes three different approaches instead of a fourth variation of the same one. A context-reset skill maintains a progress file for long tasks so the model can start a fresh session with a clean summary instead of dragging along a bloated context window.
Where do review, design, and shipping skills fit in? #
Several loop skills focus on review. One sends completed work to a reviewer running in a fresh context, cycling between author and reviewer for up to four rounds. A “jury” variant escalates this by pulling in two or three different models for review until they reach agreement, which is where Cline Pass becomes relevant: DeepSeek can act as the lead while review tasks run as read-only workers on other models such as Kimi K3.1 Pro or GLM 5.3, all under one Cline Pass subscription. Because different models make different mistakes, running review across several of them tends to catch more issues than relying on one.
The build group covers sharpening vague prompts into concrete tasks, designing types and function signatures before writing implementation code, writing failing tests before bug fixes, and checking the “blast radius” of a change against other callers or saved data. Two design skills address DeepSeek’s weak default UI output: one establishes a design direction (tone, fonts, spacing, a list of banned generic patterns like purple-to-blue gradients and centered-hero-with-three-cards layouts) before any code is written, and the other audits existing UI with scored screenshots and fixes the worst problems first.
The ship group automates the path from finished code to merged pull request: opening clean PRs, watching CI and review bots, fixing findings, and merging only when everything passes, with explicit rules against admin bypasses and destructive actions taken without asking first. The understand group rounds things out with plain-language code explanations, summaries of what changed during unattended runs, and a skill that scrubs AI-sounding filler language from docs and PR descriptions.
Is DeepSeek Stack worth setting up? #
Without shadow IT.
For teams or individuals already running significant volumes of coding work through DeepSeek V4.1 Flash, the setup cost is low (one install command, an API key, a thinking-effort toggle) relative to the workflow discipline it adds. It won’t turn DeepSeek into Opus on the hardest debugging problems across large, unfamiliar codebases. But for routine daily coding work, enforcing planning, verification, and stop-when-stuck behavior addresses most of what separates a cheap fast model from a more expensive one: not raw capability, but whether it checks its own work.
Frequently Asked Questions #
What is DeepSeek Stack built for?
It’s an open source, MIT-licensed set of 29 skills meant to improve how DeepSeek V4.1 Flash approaches coding tasks: planning before acting, verifying fixes with real test runs, and avoiding repeated failed attempts.
Does DeepSeek Stack work with any coding agent?
The skills are plain skill.md files, so they work with any agent supporting that format, including Claude Code, Codex, Kline, and Open Code. Installation asks which agents you use and configures accordingly.
How much does DeepSeek V4.1 Flash cost compared to Opus?
Off-peak, DeepSeek V4.1 Flash runs about 15 cents per million input tokens and 60 cents per million output tokens, versus roughly $4 and $20 for Opus. Even at peak pricing, DeepSeek remains more than 13 times cheaper on input and more than 16 times cheaper on output.
Why does thinking effort matter so much?
Testing behind the pack found that disabling extended thinking on DeepSeek V4.1 Flash dropped performance by close to 30 points, more impact than any individual skill, making it the single most important setting to get right.
What is Cline Pass and why does it matter here?
Cline Pass is a $9.99 per month plan giving access to several open models, including DeepSeek V4.1 Flash and others like Kimi K3.1 Pro and GLM 5.3. It enables multi-model review loops where DeepSeek leads a task while other models review it in parallel, catching different classes of mistakes at low cost.