name: forkprobe
description: Recommend a small set of candidate skills or artifact-generation pipelines for an open-ended task, then compare their outputs so the user can decide what actually helps. Use when the user is unsure if a particular skill would improve their output, when comparing 2+ skills for the same task, when they naturally ask to compare skills without saying forkprobe, or when explicitly invoked with /forkprobe. Chinese examples include "我想比较几个科研写作 skill", "帮我看看哪个 skill 更适合这段", "比较几个去 AI 味写作 skill", "比较几个论文作图 skill", "比较几个市场调研/调研报告 skill", "比较几个网页制作 skill", "比较几个产品宣传片 skill", "比较几种动效视频路线", and "用几个 skill 对同一条口播做粗剪". Especially valuable for writing, PPTX, scientific figure, research report, finished webpage, product-promo video, motion-graphics video, and talking-head rough-cut comparison. Do NOT use for simple deterministic tasks where skill choice is obvious or for casual conversation.

forkprobe

Stop guessing which AI skill works. See it side by side.

What this skill does

Recommends a small candidate set for the user's task, then compares completing that task with each candidate skill or pipeline versus without a skill/pipeline baseline. Candidate recommendation combines ForkProbe's curated catalog, automatically indexed local Skills, EverMind Skill Hub, GitHub discovery, and explicit BYO sources, then dedupes and scores before asking the user to confirm. For text tasks, it spawns parallel subagents in the current platform (Claude Code, Codex, or DeepSeek Harness), collects outputs, generates a local HTML report, and lets the user pick the winner. For file-producing tasks such as PPTX, scientific figures, research reports, webpages, and videos, it compares artifact-generation pipelines and renders a report with file links, previews or playback.

v0.10 scope: All v0.9 workflows plus the installable forkprobe-dsh native DeepSeek Harness plugin. In DSH, prefer the forkprobe_compare tool for confirmed text comparisons: it fans candidates out through the registered native subagent provider, runs an optional judge, opens the local Report, waits for the user's Continue action, and returns the selected output to the same Agent. Use forkprobe_resume when the original wait window has ended. The plugin must not start nested dsh processes or copy credentials, and candidate subagents receive no tools. The existing --platform deepseek_harness runners remain the compatibility path for file-producing Artifact tasks.

v0.9 scope retained: Text candidates, AI judging, and scientific-figure, research-report, webpage, and finished-video Artifact runners accept --platform deepseek_harness through the official dsh --profile headless profile. Use FORKPROBE_PLATFORM=deepseek_harness when invoking through an Agent that does not expose an unambiguous harness signal. Text compatibility runs default to read-only; file-producing runs default to workspace-write.

v0.8 scope retained: The optional anonymous Winner feedback loop persists the local handoff and continues the Agent task in the same action. Share only the privacy-safe task type, compared Skill names, and final choice when the Report checkbox is enabled. Queue events locally, send them asynchronously to the official Cloudflare Worker, and keep community statistics separate from source quality and benchmark priors.

v0.7 scope retained: Scan installed Skills under Codex, Agents, Claude, and project-local Skill directories; query EverMind Skill Hub and GitHub with sanitized scene terms; merge with curated and BYO candidates; dedupe by fingerprint/source; show source, license, installed state, and public quality signals; and preserve the confirmation gate before any candidate runs. --local-only keeps curated and installed-local discovery while skipping EverMind and live GitHub discovery.

v0.6 scope retained: Finished-video comparison remains divided into product promos, motion graphics, and talking-head rough cuts. Never score candidates from different video scenes in one report.

When to invoke

User says: "should I use [skill]" / "is [skill] worth it" / "compare with and without skill"
User asks: "which skill is best for X" (we don't pick — we show)
User naturally asks to compare skills, even if they do not say "forkprobe"
User explicitly types /forkprobe
First time encountering a domain where multiple candidate skills exist
User says they already picked a forkprobe winner and wants to continue, e.g. "我选好了", "已经选好 skill 了", "继续吧", or "用我刚选的继续"

Chinese trigger examples:

"我想比较几个科研写作 skill"
"帮我看看哪个 skill 更适合这段"
"先别直接改，并排试几个 skill"
"用几个不同 skill 跑一下看看差别"
"哪个 skill 改出来更自然"
"比较几个去 AI 味写作 skill"
"帮我评估一下这些 skill 哪个更好"
"先跑 baseline 和几个写作 skill 对比一下"
"基于一个文档，我想做一个 PPT，但是想多对比几个 skill 的效果"
"比较几个 PPT skill，看哪个做出来的 PPT 更好"
"比较几个论文作图 skill，看哪个机制图成品更好"
"比较几个调研报告 skill，看哪个报告证据链更可靠"
"比较几个网页制作 skill，看哪个 Landing Page 成品更好"
"用几个前端 skill 并排生成 Dashboard，让我看桌面端和移动端效果"
"比较几个产品宣传片 skill，看哪个 MP4 成片更好"
"把这组数据做成动效视频，并排比较 Remotion 和 HyperFrames"
"用几个 skill 对同一条口播原片做粗剪，让我选择切点最自然的一版"

When NOT to invoke

Simple deterministic tasks where skill choice is obvious
Conversational / exploratory requests (no comparable artifact)
User has already picked a skill and just wants to use it

How to invoke

Step 1: Understand the task and deliverable type

If the user has not provided enough detail, ask for the task goal and the content to process:

"你想完成什么任务？请贴上原文或描述目标，我会先推荐一组可对比的 skill。"

Do not require the user to know skill names. Natural task descriptions are enough.

First classify the deliverable:

User intent	Deliverable type	Compare mode
polish/rewrite/summarize/rebuttal/PPT outline	`text` or `ppt_outline`	`text`
"做一个 PPT", "生成 PPT", "PPTX", "比较 PPT skill 效果"	`pptx`	`artifact`
"画图", "生成示意图", "生成科研图/论文 figure 成品"	`visual_artifact`	`artifact`
"市场调研报告", "公司调研", "竞品分析", "用户研究报告", "文献综述", "投研报告"	`research_report`	`artifact`
"制作网页", "完整网站", "Landing Page", "Dashboard", "Web App", "HTML 成品"	`web_artifact`	`artifact`
"产品宣传片", "产品视频", "动效视频", "口播粗剪", "视频成片"	`video_artifact`	`artifact`

Important PPT rule:

If the user says they want to "做一个 PPT" or compare PPT skills, assume they want a PPTX artifact.
Do not rewrite the task as "不要生成 PPTX" or "只比较 PPT 方案" unless the user explicitly asks for outline-only output.
If ambiguous, ask one short clarification: "你要比较最终 PPTX 成品，还是先只比较 PPT 方案/大纲?"

Step 2: Discover and recommend candidate skills or pipelines

Before running the comparison, recommend 3-5 candidates and wait for user confirmation. Always include baseline.

Hard interaction rule:

Do not run compare.py, figure_artifact.py --run, research_artifact.py --run, web_artifact.py --run, video_artifact.py --run, or any artifact-generation command before the user confirms the candidate shortlist.
For market research / research report tasks, research_artifact.py is only the runner. It must not be used as the first step. First run scripts/recommend.py, show the shortlist, and ask the user to confirm, remove, or add candidates.
For finished webpage tasks, web_artifact.py is only the runner. First use scripts/recommend.py, explain the candidate differences, and wait for confirmation. A request for a brief, wireframe, or prompt without a finished page stays in text mode.
For finished-video tasks, video_artifact.py is only the runner. First use scripts/recommend.py, show candidates from exactly one video scene, and wait for confirmation. A request for only a script, storyboard, or video brief stays in text mode.
If a user says "use ForkProbe" and gives a task, stop after the recommendation message unless they have already explicitly confirmed the exact candidates in the same message.

Default discovery flow:

Start with local curated candidates from forkprobe's catalog.
Automatically scan installed Skills under ~/.codex/skills, ~/.agents/skills, ~/.claude/skills, ~/.dsh/skills, and project-level Skill roots. Index SKILL.md metadata locally and never auto-install or auto-run a result.
In parallel, query EverMind Skill Hub and GitHub discovery using sanitized scene terms such as academic writing, PPTX, scientific figure, or frontend website. Do not send the user's raw task, document, or local path.
Verify remote candidates have an exact runnable repository/subdirectory source when possible. Reject an ambiguous repository-root reference for a nested Skill.
Merge explicit BYO candidates and dedupe by content fingerprint, source repository, or command argument.
Score by task fit, installed state, SKILL.md availability, public quality signals, popularity, and current environment fit.
Present the merged shortlist with source labels and ask the user to confirm, remove, or add candidates.

Only skip EverMind/GitHub discovery when the user explicitly asks for local-only/offline candidates, e.g. "只要本地候选", "不要联网", "local only", or "offline". Local-only mode still scans installed local Skills.

Use the local recommendation helper when task text is available:

python scripts/recommend.py --input <path_to_user_input>

If the user only gave a short task description, use:

python scripts/recommend.py --text "<task description>" --domain academic-writing

If the user explicitly asks for local-only candidates:

python scripts/recommend.py --text "<task description>" --domain academic-writing --local-only

Optional source controls:

python scripts/recommend.py --input <path_to_user_input> --no-evermind
python scripts/recommend.py --input <path_to_user_input> --no-local-skills
python scripts/recommend.py --input <path_to_user_input> --refresh-sources

Then present the recommendation in plain language:

我可以并排比较。我会先合并 curated、本机已安装、EverMind Skill Hub、GitHub 和 BYO 候选，再让你确认。

根据你的任务，我建议先跑这组：

1. baseline：原始模型输出，作为参照
2. writing-anti-ai：适合降低机器感、让中英文表达更自然
3. humanizer-zh：适合中文去 AI 痕迹
4. remove-ai-flavor-writing-skill：适合中文去模板句、假互动结尾和过度圆滑表达
5. humanizer / stop-slop / avoid-ai-writing：适合英文 anti-AI / humanizer 对比

确认按这组跑吗？你也可以删掉或加入别的 skill。

Recommendation rules:

If the user already named exact skills, respect that list and only add baseline unless they ask for suggestions.
If the user asks generally to compare skills, recommend first and do not start the run until they confirm.
If the user does not say local-only/offline, include EverMind and GitHub discovery alongside curated and installed-local candidates.
For Chinese SCI writing, default toward baseline, writing-anti-ai, humanizer-zh, remove-ai-flavor-writing-skill, research-paper-writing-skills, and paper-writer-skill.
For explicit anti-AI / humanizer writing tasks, prioritize dedicated anti-AI candidates before generic polishing: writing-anti-ai, humanizer-zh, humanizer, stop-slop, avoid-ai-writing, remove-ai-flavor-writing-skill, and academic variants when relevant.
For English/Nature-style polishing or translation, also consider BYO https://github.com/Yuan1z0825/nature-skills#skills/nature-polishing.
For reviewer response/rebuttal tasks, consider paper-writer-skill and BYO https://github.com/Yuan1z0825/nature-skills#skills/nature-response.
For PPT outline tasks, compare text plans with nature-paper2ppt, paper-writer-skill, and relevant writing skills.
For PPTX artifact tasks, run discovery first, then compare PPT generation pipelines, not writing-only skills.
For research report artifact tasks, compare research-report pipelines, not short-answer research summaries. Default candidates include baseline-research-report, source-first-research, analyst-style-report, and evidence-table-report; for specific domains, add company-research-report, user-research-cookiy-report, literature-review-report, or investment-research-report.
For webpage artifact tasks, classify the page family before shortlisting. Always include baseline-web; then choose from anthropic-frontend-design, hallmark-web, anthropic-web-artifacts, ui-ux-pro-max-web, garden-web-design-engineer, baoyu-design-web, and html-anything-prototype according to landing/dashboard/app/report fit. Use hallmark-web for landing pages and general sites where structural variety and anti-template design matter; do not treat it as a business-logic pipeline. Do not include a conditional candidate that requires unavailable Stitch, SuperDesign, gstack, or other external tooling.
For product-promo video tasks, use baseline-remotion-agent, hyperframes-product-launch, and video-shotcraft.
For motion-graphics tasks, use baseline-remotion-motion, hyperframes-motion-graphics, and remotion-bits-enhanced.
For talking-head rough cuts, use auto-editor, maxazure-video-editing, video-use-cut-only, and experimental chengfeng-cut-talking-head. Require the same source video for every candidate and forbid B-roll, music, generated scenes, visual packaging, or script rewriting in cut-only mode.

PPTX discovery:

python scripts/discover_skills.py \
  --deliverable pptx \
  --query "<task/domain, e.g. academic PPT from document>"

The discovery report must classify candidates as:

strategy: improves academic structure/style but needs a generator, e.g. academic-pptx-skill, nature-paper2ppt
generator: creates/edits PPTX, e.g. Presentations, pptx
full_pipeline: claims to produce PPTX directly, e.g. ppt-master, md-slides

Only complete pipelines should enter artifact comparison. Typical scientific PPTX shortlist:

baseline + presentations
academic-pptx-skill + presentations
nature-paper2ppt + presentations
ppt-master
md-slides

Before execution, mark GitHub/external candidates as needs_verification until clone/dependency/license/output-path checks pass.

Artifact mode execution:

Ask the user to confirm the PPT pipelines.
Generate one separate PPTX per pipeline in a clearly named output folder.
Render or capture representative previews when possible.
Create an artifact manifest JSON and render the artifact report:

python scripts/render_artifact_report.py \
  --manifest <artifact_manifest.json> \
  --output ./artifact-report.html

The artifact report should show file links/previews, candidate summaries, AI judge notes when available, and winner selection.

Step 3: Confirm skills to compare

Wait for the user to confirm, remove, or add candidates. Also support BYO: user provides a GitHub URL, local path, or repo#subdir reference such as:

https://github.com/Yuan1z0825/nature-skills#skills/nature-polishing

Step 4: Run text comparison

Inside DeepSeek Harness with the native plugin installed, call forkprobe_compare only after showing the shortlist and obtaining explicit user confirmation. Set confirmed=true, pass the original task and selected Skill IDs/paths/URLs, and normally set wait_for_verdict=true. After the user chooses a Winner and clicks Continue, use the returned selectedOutput and handoffText to continue the original task in the same Agent. If the wait expires, call forkprobe_resume with the returned logPath.

For Claude Code, Codex, or the DSH headless compatibility path, invoke:

python scripts/compare.py \
  --platform <auto|claude_code|codex|deepseek_harness> \
  --input <path_to_user_input> \
  --skill <skill_id_1> --skill <skill_id_2> ... \
  --judge \
  --output ./report.html

The script:

Detects platform (Claude Code, Codex, or DeepSeek Harness) via platform_adapter.py, unless --platform or FORKPROBE_PLATFORM overrides it
Spawns N+1 parallel subagents (one per selected skill + baseline)
- Claude Code: prefers claude-agent-sdk, then Anthropic API fallback
- Codex: prefers native codex exec so it inherits Codex Desktop auth/model config, then OpenAI API fallback
- DeepSeek Harness compatibility mode: uses the official one-shot dsh --profile headless "task" entry; resolve the executable through FORKPROBE_DSH_CLI, global dsh, or official npx @deepseek-ai/dsh
Each subagent runs the same task input through its respective system prompt
Collects outputs, tokens, latency
Optionally runs a judge subagent when --judge is present
Renders HTML via render_report.py + templates/report.html.j2

For artifact mode, do not use compare.py directly unless the artifact has first been converted into comparable text summaries. Generate artifacts per pipeline, then use render_artifact_report.py.

Scientific figure artifact mode

Use this path when the user wants finished paper figures or scientific graphics, not just figure text. Examples:

real data -> plotting code -> PNG/SVG/PDF/TIFF
paper brief -> mechanism/schematic/architecture diagram -> PNG/SVG/draw.io or SVG source
paper brief -> graphical abstract -> PNG/SVG/PDF

If the user only asks for figure storyline, caption, or plotting code draft, keep the task in text mode. If they ask for a final figure package, prepare the artifact workspace:

python scripts/figure_artifact.py \
  --input <path_to_user_input> \
  --pipeline baseline-python-figure \
  --pipeline nature-figure-python \
  --pipeline schematic-svg \
  --run \
  --judge \
  --render-report \
  --report-output ./figure-artifact-report.html

This creates:

outputs/figure-runs/<run>/
  task.md
  artifact-manifest.json
  candidates/<pipeline-id>/INSTRUCTIONS.md
  candidates/<pipeline-id>/artifacts/

With --run, forkprobe invokes the selected figure pipelines in parallel through Codex native CLI and asks each candidate to write into its own artifacts/ directory. You can omit --run to only prepare the workspace, then orchestrate each candidate manually. Expected figure package files include:

preview.png for report display
figure.svg, figure.pdf, and optionally figure.tiff
source files such as source.py, figure.svg, figure.drawio, or layout.json
caption.md
qa.md

To compare a BYO figure skill, add one or more skill sources:

python scripts/figure_artifact.py \
  --input <path_to_user_input> \
  --pipeline baseline-python-figure \
  --skill-source https://github.com/<owner>/<repo>#skills/<figure-skill> \
  --run \
  --judge \
  --render-report \
  --report-output ./figure-artifact-report.html

--skill-source accepts the same repo#subdir or local path format used by BYO text skills. forkprobe turns each source into its own figure pipeline, injects the skill instructions into that candidate run, and compares the generated artifact package in the report.

After candidate artifacts exist, run the same command again or call:

python scripts/render_artifact_report.py \
  --manifest <figure_run>/artifact-manifest.json \
  --output <figure_run>/figure-artifact-report.html

The report should compare file links/previews, candidate summaries, captions, QA notes, and winner selection.

Research report artifact mode

Use this path when the user wants a finished research report, not just a short answer, outline, interview guide, or research plan. Examples:

market research / industry analysis -> research report package
company research / competitive analysis -> report, sources, evidence table
user research -> research synthesis report, method limits, findings evidence
literature review -> structured review report and source/evidence table
investment research -> report with assumptions, risks, and non-advice limitations

If the user only asks for a research outline, question list, interview guide, or survey draft, keep the task in text mode.

If they ask for a final report, first recommend candidates and wait:

python scripts/recommend.py --input <path_to_user_input> --domain academic-writing

Present the shortlist in plain language, for example:

我建议先比较这组 research report pipeline：

1. baseline-research-report：成品基线
2. source-first-research：先整理来源和 evidence table，再生成报告
3. analyst-style-report：咨询/投研风格结构化报告
4. evidence-table-report：先建 claim-evidence 表，再写报告

确认按这组跑吗？你也可以删掉或加入 company-research、user-research-cookiy、literature-review 或 investment-research。

Only after the user confirms the shortlist, run the artifact pipelines:

python scripts/research_artifact.py \
  --input <path_to_user_input> \
  --pipeline baseline-research-report \
  --pipeline source-first-research \
  --pipeline analyst-style-report \
  --pipeline evidence-table-report \
  --confirmed \
  --run \
  --judge \
  --render-report \
  --report-output ./research-artifact-report.html

This creates:

outputs/research-runs/<run>/
  task.md
  artifact-manifest.json
  candidates/<pipeline-id>/INSTRUCTIONS.md
  candidates/<pipeline-id>/artifacts/

Expected research package files include:

candidate-report.md and preferably candidate-report.html
sources.json
evidence-table.md
claim-checks.md
limitations.md
summary.md

To compare a BYO research skill, first include it in the recommendation shortlist and ask the user to confirm. After confirmation, add one or more skill sources:

python scripts/research_artifact.py \
  --input <path_to_user_input> \
  --pipeline baseline-research-report \
  --skill-source https://github.com/<owner>/<repo>#skills/<research-skill> \
  --confirmed \
  --run \
  --judge \
  --render-report \
  --report-output ./research-artifact-report.html

--skill-source accepts the same repo#subdir or local path format used by BYO text skills. forkprobe turns each source into its own research-report pipeline, injects the skill instructions into that candidate run, and compares the generated research package in the report.

Web artifact mode

Use this path when the user wants a finished, runnable website rather than a page brief, wireframe, prompt, or isolated code suggestion. Supported families include landing pages, product sites, dashboards, web apps, report pages, and general HTML deliverables.

First recommend candidates and wait for confirmation:

python scripts/recommend.py --input <path_to_user_input> --domain academic-writing

Only after confirmation, run the selected web pipelines:

python scripts/web_artifact.py \
  --input <path_to_user_input> \
  --pipeline baseline-web \
  --pipeline anthropic-frontend-design \
  --pipeline hallmark-web \
  --pipeline baoyu-design-web \
  --confirmed \
  --run \
  --judge \
  --render-report \
  --report-output ./web-artifact-report.html

Each candidate must produce a runnable static entry at artifacts/site/index.html. ForkProbe then:

normalizes the final static site
captures desktop.png at 1440x1000
captures mobile.png at 390x844
writes qa.json for page load, viewport, responsiveness, interactions, local assets, basic accessibility, and real-browser mobile horizontal overflow when Python Playwright is available
packages editable files as source.zip
renders side-by-side previews, QA, metrics, files, and AI judge notes

Expected candidate package:

site/index.html and local assets
desktop.png
mobile.png
qa.json
source.zip
README.md and candidate summary.md

To compare a BYO web skill, add --skill-source <repo#subdir-or-local-path> after the user approves it. Never add conditional candidates whose required runtime is unavailable.

Video artifact mode

Use this path when the user wants a playable video result rather than only a script, storyboard, motion brief, or edit suggestion. Classify every request into exactly one family:

product_promo: product launch, feature announcement, website showcase, or brand promo
motion_graphics: kinetic type, data/UI animation, logo sting, chart hit, or explanatory motion
talking_head_cut: rough cut of existing talking-he

Forkprobe

Review

Why we picked it

What it does

Who it’s for

Risks & caveats

Review, then install

Repository facts

What this plugin covers

GitHub topics

Declared platforms

SKILL snapshot