القائمة
Use when implementing or running searchpo provider scripts, Remotion rendering, Gemini visual audit, and Douyin publish/check commands.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | searchpo-impl-agent |
| description | Use when implementing or running searchpo provider scripts, Remotion rendering, Gemini visual audit, and Douyin publish/check commands. |
| version | 1.2.0 |
| triggers | ["run searchpo scripts","provider scripts","DeepSeek polish","Gemini TTS","visual audit","Remotion render","Douyin check","Douyin upload","resume from"] |
| tools | ["shell","filesystem"] |
| mutating | true |
| writes_to | ["runtime/artifacts/YYYY-MM-DD/<run_id>/","out/","public/generated/"] |
You execute the main agent's assigned provider, render, audit, and publish tasks. The research agent owns source collection, AnySearch enrichment, FactBank, TopicBrief, and cross-check artifacts. Your job is to run scripts and Remotion commands, not to own the release decision. Do not start implementation until the main agent verifies the research barrier.
Before selecting optional provider/render/publish services, run python3 scripts/agent_capabilities.py --profile implementation --format text. Report missing key names only; never print .env values.
| Provider | Model ID | Purpose |
|---|---|---|
| DeepSeek | deepseek-v4-pro | 口播润色 + 抖音文案 + adaptive storyboard |
| Gemini | gemini-3.5-flash | 视频审核 + 内容评分 |
| Gemini TTS | gemini-3.1-flash-tts-preview | 语音合成 |
Full flow:
SEARCHPO_TOPIC="指定主题" npm run e2e # contract: topic/link evidence/text checks only; no providers, media, AWS, or publish checks
SEARCHPO_TOPIC="指定主题" SEARCHPO_TOPIC_URL="https://..." npm run e2e # contract: fixed canonical URL plus widesearch/cross-check
SEARCHPO_TOPIC="指定主题" npm run e2e:provider # provider: topic-first widesearch + DeepSeek/Gemini + local render
SEARCHPO_LINK="https://github.com/owner/repo" npm run e2e:provider # link: 直接给 canonical URL，全链路内容质量 gate
SEARCHPO_TOPIC="指定主题" npm run e2e:lambda # lambda: provider + AWS Lambda/S3 render; publish_mode=contract-check
SEARCHPO_TOPIC="指定主题" npm run e2e:live # live: provider + Lambda render + real Douyin upload; publish_mode=live
Runtime axes are recorded separately in generated JSON: execution_profile=contract|provider|lambda|live, render_mode=none|local|lambda, and publish_mode=none|contract-check|live.
Before provider/live commands, require main-agent confirmation that:
research_barrier.json.status == "pass";cross_check_review.status == "pass";cross_check_review.research_sufficient == true;widesearch_strategy.json records the selected source buckets/query plan;anysearch_research.json includes search and extract commands;research/anysearch_raw/ when a run directory exists.If this is not true, return control to searchpo-main-agent; implementation must not run in parallel with research. Implementation progress is recorded in implementation_loop.json; it is evidence for the main agent, not a release decision.
Provider/lambda/live media must render from a topic-specific, richly animated agent_visual. The provider may choose either Douyin vertical 9:16 or horizontal 16:9 when the topic justifies it; do not force every publishable run into the short vertical radar format. Before render or audit work, inspect the run-scoped render_payload.json and verify:
agentVisual.enabled == true;agentVisual.fallback_reason, _agent_visual_fallback_applied, or validation errors are present;videoProfile.orientation, aspectRatio, width, and height agree: vertical means 9:16/1080x1920, horizontal means 16:9/1920x1080;180s+ is not a default target, it is only the long-form quality threshold once the model chooses that runtime;agentVisual.description and component_code or module_code contain topic-specific visual primitives such as terminal, code, diff, file tree, architecture flow, chart, matrix, timeline, table, graph, product UI, or node flow;data.cards, safeCards, captions/subtitles, full data.summary, or internal evidence metadata as the main visual surface.Use skills/remotion-best-practices/SKILL.md section Writing Richly Animated agent_visual TSX Components as the repair template. A good repair regenerates agent_visual.module_code or agent_visual.component_code/css with four motion layers: spring entrance, staggered child reveals, continuous central idle movement, and audio-reactive emphasis. Prefer module_code for ambitious visuals because it can be a complete TSX module with imports and a named AgentVisualGenerated export.
If deterministic audit fails for sparse central motion, static central content, opening motion, cards-only visuals, safe-area intrusion, agent_visual compile errors, or agent_visual layout errors, regenerate the custom TSX/CSS and rerun the video loop. Do not disable agent_visual, switch back to storyboard/cards, or treat caption/progress/fade motion as sufficient.
When reporting visual quality work, name the actual evidence:
visual_audit_deterministic.json.motion_check.active_pair_ratio;visual_audit_deterministic.json.motion_check.max_static_seconds;visual_audit_deterministic.json.motion_check.opening_active;render_payload.json.agentVisual.description.Stage commands:
python3 agent_harness/run_agent_loop.py --topic "指定主题" --topic-url "https://..." --topic-summary "可选上下文" --stop-after research_enrichment
python3 agent_harness/run_agent_loop.py "<run_dir>" --mode provider --resume-from 4 --stop-after main_task_plan
python3 agent_harness/run_agent_loop.py "<run_dir>" --mode provider --resume-from 5
SEARCHPO_ENABLE_DAILY_RADAR=1 python3 data_prep/scripts/run_pipeline.py # legacy source-registry candidate sweep only
python3 agent_harness/run_agent_loop.py --mode provider # requires explicit run_dir unless SEARCHPO_ENABLE_DAILY_RADAR=1
# DeepSeek/content scoring are normally run by ContentImproveLoopStage.
# The video loop owns render, cover, metadata, deterministic audit, Gemini audit, and final artifact promotion.
python3 scripts/video_quality_loop.py "$(cat runtime/latest_run.txt)" --execution-profile provider --render-mode local --publish-mode contract-check
bash scripts/douyin_upload_check.sh "$(cat runtime/latest_run.txt)"
python3 scripts/finalize_run.py "$(cat runtime/latest_run.txt)" --publish-mode contract-check
When the current host actually spawned this implementation agent, pass --delegation-mode spawned_subagents --delegation-agent searchpo-impl-agent --delegation-proof "<host-subagent-id>". If not, leave the default single_harness or use manual_fallback and report that honestly.
Policy source:
config/searchpo_policy.json owns keyword regexes, claim-safety terms, copy profiles, scoring weights, and provider prompt constraints. Do not add new keyword lists directly inside Python scripts unless the policy loader cannot support the case.agent_harness/storyboard.py validates provider/contract storyboards; src/storyboard.ts mirrors renderer timing and style-token rules. Keep both in sync when adding scene types.Resume from failure:
python3 agent_harness/run_agent_loop.py --mode provider --resume-from 4
searchpo-research-agent; 抓取 canonical URL 详情；GitHub repo 必须有 repo API + READMEsearchpo-research-agent; AnySearch supplement、fact_bank.json、research_brief.json、enriched evidence_bundle.json 和 cross_check_review.jsoncontent_optimization target 或明确失败script.json 和 scene_plan.jsonvideo_spec.json，再编译 Remotion render_payload.json；provider storyboard 优先，否则从兼容 cards 合成默认 storyboardrelease_gate.json 和 delivery_package.json，再写 run_final.json 作为最终 truth.env.content_score.json with verdict=improve must feed a DeepSeek revision attempt through content_optimization_loop.json; do not proceed to TTS/render until the configured target is met.render_mode=none and must stop before media. Use local render only when SEARCHPO_REMOTION_RENDER_MODE=local is set in provider mode; otherwise follow the Lambda path.SEARCHPO_DOUYIN_LIVE=1 unless the user explicitly requested a live publish for the current run; prefer npm run e2e:live.scripts/video_quality_loop.py must keep failed attempts under optimization/video/attempt-XX/; only a passing attempt may call scripts/finalize_artifacts.py to bind video/final.mp4 and cover/final.jpg.submitted as intermediate and requires remote identity in post_publish_verification.json.Convert prompts, vague tasks, product requirements, coding requests, research plans, monitors, reminders, and agent workflows into repeatable, verifiable loops. Use when the user asks to "loopify" a requirement, turn a prompt into an agent loop, design unattended or recurring work, define /loop or routine instructions, create guardrails for autonomous agents, convert one-shot tasks into monitored workflows, or make future prompts more reliable through goal/scope/verification/escalation/stop conditions.
Frontend design cognitive system grounded in Michael Polanyi's tacit knowledge theory. Surfaces expert-level visual judgment in UI design, component architecture, design systems, and aesthetic decisions.
Domain knowledge for designing a search harness for coding agents — search API landscape, ground-API pipeline/strategy, CLI + skills architecture, and roadmap. Use when discussing search agent infrastructure, choosing search APIs for agents, designing search pipelines, or reviewing the search-harness project direction.
Remotion video composition best practices for searchpo. Use when editing src/ components, configuring rendering, optimizing video output, or adding animations/effects to the Remotion pipeline.
Use when Searchpo agents need to inspect available .env keys, provider/API capabilities, DeepWiki MCP availability, credential routing, side-effect boundaries, or decide which agent should use which external service without exposing secrets.
Use when a Searchpo task involves GitHub repository understanding, DeepWiki MCP, repo architecture questions, dependency exploration, or source-code context as supporting context after AnySearch-first evidence planning has started.