DeepSeek++ is a browser extension that turns the DeepSeek website into an AI workspace with tools, memory, skills, media analysis, and automation. DeepSeek users employ it to manage projects, control browser pages, search the web, reuse context, and export conversations, while the catalogue entries add skills and instructions for that workspace.
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 skills add zhu1090093659/deepseek-pp --skill review-spdgit clone --depth 1 https://github.com/zhu1090093659/deepseek-ppWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zhu1090093659/deepseek-pp/review-spd)<a href="https://agentmods.dev/skills/zhu1090093659/deepseek-pp/review-spd"><img src="https://agentmods.dev/badge/skills/zhu1090093659/deepseek-pp/review-spd/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zhu1090093659/deepseek-pp/review-spd"><img src="https://agentmods.dev/badge/skills/zhu1090093659/deepseek-pp/review-spd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00072 | $0.01548 |
| Opus 5 | $0.00036 | $0.00774 |
| Sonnet 5 | $0.00014 | $0.00310 |
| Haiku 4.5 | $0.00007 | $0.00155 |
Grade A, and why
review-spd 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review SPD
You are executing the Review SPD workflow: a findings-first code review process for changed code. Your primary goal is to identify bugs, regressions, and behavior risks introduced by the changes. Do not turn this into a style review or a broad summary.
Configuration
| Item | Default | Purpose |
|---|---|---|
| Context script | scripts/review-context.py relative to this Review SPD skill directory |
Collect stable git context for review targets |
| Default target | Uncommitted changes | Review working tree and staged changes by default |
| Commit range default | Last 3 days | Used only when the user explicitly requests commit/date review without dates |
| PR base | Auto-detect origin/main, origin/master, then remote default branch |
Base for branch-vs-main review |
| Output style | Findings first | Findings ordered by severity before summaries or notes |
References:
- Reviewer sub-agent template:
references/reviewer-template.md - Final output format:
references/output-format.md
Target Modes
The workflow supports three mutually exclusive review targets:
- Uncommitted mode: Review current uncommitted changes. This is the default.
- Commit-range mode: Review commits in a date range. If the user explicitly asks for commit/date review but gives no range, use the last 3 days.
- Branch / PR mode: Review a branch compared with the main branch or an explicit base branch.
Resolve target conflicts with this priority:
- If
branchis specified, use branch / PR mode. - Else if
sinceoruntilis specified, use commit-range mode. - Else use uncommitted mode.
base only applies to branch / PR mode.
Phase 1: Target Resolution
Extract the review target from the user's request.
Examples:
Resolve the context script from the installed Review SPD skill directory, not from the repository being reviewed. Use the packaged script path in commands, for example:
python <review-spd-skill-dir>/scripts/review-context.py
python <review-spd-skill-dir>/scripts/review-context.py --since "3 days ago"
python <review-spd-skill-dir>/scripts/review-context.py --since 2026-06-28 --until 2026-07-01
python <review-spd-skill-dir>/scripts/review-context.py --branch feature/foo
python <review-spd-skill-dir>/scripts/review-context.py --branch feature/foo --base origin/main
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 140 lines · 72 tokens per session scan A a317246dd0f3
review-spd is a skill published in the GitHub repository zhu1090093659/deepseek-pp (1,848 stars, last pushed 29d ago), licensed Apache-2.0. It adds 72 tokens to every session and 1,548 once invoked, about $0.0004 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.
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