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clearshotclearshot 搜索

Agent Skill

clearshot 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,529

周安装

65

GitHub Stars

127

下载量

536
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:clearshot(clearshot 搜索)
来源仓库:https://github.com/udayanwalvekar/clearshot
仓库路径:skills/clearshot
安装命令:
npx skills add https://github.com/udayanwalvekar/clearshot --skill clearshot
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/udayanwalvekar/clearshot --skill clearshot

简介

增强型搜索技能,支持跨仓库与本地 skill 目录的智能定位与版本检测。

  • 适用于复杂安装路径下的 skill 查找与管理,兼容主流宿主环境。
  • 自动识别有效 skill 目录并加载 VERSION 元数据,提供调试信息输出。
  • 依赖 bash 环境运行,需确保系统支持标准 shell 命令集。
  • clearshot 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Preamble

Run this bash block first, before any analysis:

# ─── Find skill directory (works from any install path) ───────
_CS_DIR=""
for _d in "$HOME/.claude/skills/clearshot" "$HOME/.agents/skills/clearshot"; do
  [ -f "$_d/SKILL.md" ] && _CS_DIR="$_d" && break
done
# fallback: search
[ -z "$_CS_DIR" ] && _CS_DIR="$(cd "$(dirname "$(find "$HOME/.claude" "$HOME/.agents" -name SKILL.md -path '*/clearshot/*' -print -quit 2>/dev/null)")" 2>/dev/null && pwd || echo "")"
_CS_VER=""
[ -n "$_CS_DIR" ] && [ -f "$_CS_DIR/VERSION" ] && _CS_VER="$(cat "$_CS_DIR/VERSION" | tr -d '[:space:]')"
_CS_STATE="$HOME/.clearshot"
mkdir -p "$_CS_STATE/analytics" "$_CS_STATE/feedback"

# ─── First-run detection ─────────────────────────────────────
_CS_FIRST_RUN="no"
[ ! -f "$_CS_STATE/config.yaml" ] && _CS_FIRST_RUN="yes"

# ─── Read config (only if it exists) ─────────────────────────
_CS_UPDATE_MODE="ask"
_CS_TEL="off"
_CS_TEL_PROMPTED="no"
if [ -f "$_CS_STATE/config.yaml" ]; then
  _CS_UPDATE_MODE=$(grep -E '^update_mode:' "$_CS_STATE/config.yaml" 2>/dev/null | awk '{print $2}' | tr -d '[:space:]' || echo "ask")
  _CS_TEL=$(grep -E '^telemetry:' "$_CS_STATE/config.yaml" 2>/dev/null | awk '{print $2}' | tr -d '[:space:]' || echo "off")
fi
[ -f "$_CS_STATE/.telemetry-prompted" ] && _CS_TEL_PROMPTED="yes"

# ─── Version check (only if user opted into updates) ─────────
# No network calls until config exists and user has chosen
if [ -n "$_CS_VER" ] && [ "$_CS_FIRST_RUN" = "no" ]; then
  _CS_CACHE="$_CS_STATE/last-update-check"
  _STALE=""
  [ -f "$_CS_CACHE" ] && _STALE=$(find "$_CS_CACHE" -mmin +60 2>/dev/null || true)
  if [ ! -f "$_CS_CACHE" ] || [ -n "$_STALE" ]; then
    _CS_REMOTE=$(curl -sf --max-time 5 "https://raw.githubusercontent.com/udayanwalvekar/clearshot/main/VERSION" 2>/dev/null | tr -d '[:space:]' || true)
    if echo "$_CS_REMOTE" | grep -qE '^[0-9]+\.[0-9.]+$' 2>/dev/null; then
      if [ "$_CS_VER" != "$_CS_REMOTE" ]; then
        if [ "$_CS_UPDATE_MODE" = "always" ]; then
          cd "$_CS_DIR" && git pull origin main --quiet 2>/dev/null
          _CS_VER="$(cat "$_CS_DIR/VERSION" 2>/dev/null | tr -d '[:space:]')"
          echo "CS_AUTO_UPDATED: $_CS_VER"
          echo "UP_TO_DATE $_CS_VER" > "$_CS_CACHE"
        else
          echo "UPGRADE_AVAILABLE $_CS_VER $_CS_REMOTE" > "$_CS_CACHE"
          echo "CS_UPGRADE: UPGRADE_AVAILABLE $_CS_VER $_CS_REMOTE"
        fi
      else
        echo "UP_TO_DATE $_CS_VER" > "$_CS_CACHE"
      fi
    fi
  else
    _CACHED="$(cat "$_CS_CACHE" 2>/dev/null || true)"
    case "$_CACHED" in UPGRADE_AVAILABLE*)
      if [ "$_CS_UPDATE_MODE" = "always" ]; then
        cd "$_CS_DIR" && git pull origin main --quiet 2>/dev/null
        _CS_VER="$(cat "$_CS_DIR/VERSION" 2>/dev/null | tr -d '[:space:]')"
        echo "CS_AUTO_UPDATED: $_CS_VER"
        echo "UP_TO_DATE $_CS_VER" > "$_CS_CACHE"
      else
        echo "CS_UPGRADE: $_CACHED"
      fi
      ;;
    esac
  fi
fi

# ─── Session tracking ────────────────────────────────────────
_CS_SESSION_ID="$$-$(date +%s)"
_CS_TEL_START=$(date +%s)

echo "CS_FIRST_RUN: $_CS_FIRST_RUN"
echo "CS_SKILL_DIR: $_CS_DIR"
echo "CS_UPDATE_MODE: $_CS_UPDATE_MODE"
echo "CS_TEL_PROMPTED: $_CS_TEL_PROMPTED"
echo "CS_TELEMETRY: ${_CS_TEL:-off}"
echo "CS_SESSION_ID: $_CS_SESSION_ID"
echo "CS_TEL_START: $_CS_TEL_START"
echo "CS_VERSION: ${_CS_VER:-unknown}"

Interpreting preamble output

If CS_FIRST_RUN is yes: This is the first time clearshot is running — no config exists yet. Before doing any analysis, tell the user to run the onboarding setup. Say something brief like:

"clearshot needs a quick first-run setup (two questions, arrow keys + enter):"

Then instruct them to type: ! {CS_SKILL_DIR}/bin/onboarding.sh

Use the actual CS_SKILL_DIR value from the preamble output (e.g. ~/.claude/skills/clearshot or ~/.agents/skills/clearshot). The script asks about update preference and telemetry, writes the config, and completes in seconds.

After the user runs it, proceed with the analysis. If the user declines, proceed anyway — no network calls will happen without config.

If CS_AUTO_UPDATED appears: The skill auto-updated itself. Mention it briefly: "clearshot updated to v{version}" and continue with the analysis. No action needed.

If CS_UPGRADE shows UPGRADE_AVAILABLE <old> <new>: The user has update_mode: ask. Tell them a new version is available and instruct them to run the interactive update picker:

"clearshot v{new} is available (you're on v{old}):"

Then instruct them to type: ! {CS_SKILL_DIR}/bin/update-prompt.sh {old} {new}

The script lets them choose "Update now" or "Always update" (which also switches to auto-update mode for the future). If the user skips it, continue with the analysis on the current version.

If CS_TEL_PROMPTED is no (but CS_FIRST_RUN is also no): The user has a config but somehow skipped the telemetry question. Tell them to run:

! {CS_SKILL_DIR}/bin/telemetry-setup.sh

After the user runs it, proceed with the analysis. If the user declines, proceed anyway with telemetry off.

Screenshot analysis

When an LLM looks at a screenshot and tries to go directly from pixels to code (or feedback or a description), it loses spatial relationships, misreads component hierarchy, and hallucinates design details. The fix: build a structured intermediate representation between "seeing the image" and "responding about it." That intermediate layer is what this skill provides.

Gate check

Not every image needs this skill.

Ask two questions before doing anything:

  1. Is this image a digital interface? (websites, apps, dashboards, mockups, wireframes, Figma exports, CLI with UI context, browser DevTools with a visible page all count. Photos, memes, standalone charts, presentation slides, documents, handwritten notes do not.)
  2. Is the conversation about building, debugging, designing, or evaluating UI?

Three outcomes:

  • Neither is true: exit the skill entirely. Respond normally. Don't mention this framework.
  • Image is not a UI, but the conversation IS about building UI (e.g. "build me a page that feels like this photo"): the image is inspiration, not a spec. Describe what it communicates — mood, texture, weight — and move on. No structured analysis.
  • Image IS a UI and the conversation is about building/evaluating: proceed with the analysis levels below.

Analysis levels

Every analysis combines facts and taste. There is no separate "analytical mode" or "qualitative mode" — every observation is grounded in specifics (hex values, pixel measurements) AND includes how it feels (hierarchy, weight, cohesion). This mirrors how a senior designer thinks: feel first, then investigate why, always both.

Level 1: Map (always runs)

Divide the screenshot into a 5×5 grid. For each occupied region: what section lives there (nav, hero, sidebar, content, footer, modal, drawer, empty space), its approximate size relative to viewport, and how it relates to neighbors.

For every visible element, capture: type (button, input, card, image, icon, text, link, toggle, dropdown, tab, badge, avatar, table, chart, etc.), label/content (exact visible text), position (grid region + relative placement), state (default, hover, active, disabled, selected, error, loading, focused), size (pixel estimate), background color (hex), text color (hex), border (visible/none + radius in px), shadow (none/sm/md/lg), icon if present. Group by section.

Also note: where the eye goes first. Whether the layout breathes or feels cramped. Whether the hierarchy is clear or competing. What feels intentional vs accidental.

Level 2: System (always runs)

Extract the design system behind what's visible:

Colors: page bg, card/surface bg, primary action, secondary, text primary, text secondary/muted, border/divider, accent, destructive, success. All hex values. Note whether the palette feels cohesive or patchwork — is there a clear system or are colors ad hoc?

Typography: heading style (size in px, weight, case), body text (size, weight, line-height), caption/small text, font family if identifiable. Note whether the type scale feels intentional — do sizes step consistently or jump randomly?

Spacing and shape: spacing pattern (tight 4-8px / comfortable 12-16px / spacious 24-32px+), border radius pattern (sharp 0-2px / subtle 4-6px / rounded 8-12px / pill), overall density (compact / comfortable / spacious). Note whether spacing is consistent or inconsistent across sections.

Level 3: Blueprint (escalates when building)

This level runs when the user needs to implement, rebuild, or clone the UI from the screenshot. The LLM should escalate to Level 3 when the conversation involves writing code from this screenshot.

Layout architecture: page layout pattern (single column, sidebar+content, dashboard grid, centered container, full-bleed), content layout per section (flex row, flex column, CSS grid with column count, stack), container width (max-width constrained vs full-width), responsive context (mobile <640px / tablet 640-1024px / desktop >1024px), scroll clues (content cut off, sticky header, fixed bottom bar), z-index layers (overlays, modals, dropdowns, toasts).

Interaction map: primary CTA (the single most important action), secondary actions, navigation pattern (top nav, side nav, tabs, breadcrumbs, bottom bar), form elements and grouping, data display patterns (tables, card grids, lists), visible states (loading, empty, error, success). Note where a user would hesitate or feel friction, and what feels polished.

Output

Match the output to the context. Don't force headers and sections when a paragraph will do.

Critique/feedback: lead with what's wrong or what needs attention. Ground each observation in specifics (the exact hex, spacing, or element causing the problem) and how it affects the experience. Don't catalog everything — focus on what matters.

Implementation spec (Level 3): structured output with section headers — layout map, elements by section, design tokens, layout architecture, interaction map. This is the build document.

Comparison (two screenshots): what changed, what improved, what regressed, what still needs work.

Core principles

Be specific. "A dashboard with some cards" is never acceptable. "3-column grid, ~280px cards, #F9FAFB bg, 8px radius, subtle shadow — the cards feel weightless, almost floating" is. Every observation needs both the measurement and the judgment.

Hex over color names, pixels over vague sizes. Say #3B82F6 not "blue." Say ~16px not "some." If uncertain, give your best estimate and note it.

Group by section, not by element type. The nav's elements belong together. Don't lump all buttons across the page into one list.

Call out the non-obvious. Custom illustrations, unusual component patterns, implied animations, dynamic vs static data. These are the things that break implementations.

Match the user's pace. Rapid iteration = concise output. Detailed clone request = exhaustive. But the analysis depth (Levels 1+2) is always the same — what changes is how much you output, not how much you see.

Self-rating

Internal, silent, every time

After completing any analysis, rate your own output 0-10 across these criteria: spatial accuracy (did the grid correctly map the layout?), specificity (are colors hex, sizes pixel-estimated, components precisely named?), level selection (did the right levels run?), taste (did you catch what feels off, not just what's measurably wrong?), actionability (could someone act on this analysis?). Average the scores.

This rating is strictly internal. It flows into telemetry as the RATING field in the epilogue, but it is never shown to the user. Displaying a score after every analysis is noisy and self-congratulatory.

When to surface feedback

There are exactly three situations where the skill should involve the user in quality feedback. Outside of these, stay quiet.

Trigger 1: User correction. If the user corrects the analysis — "no, that's wrong," "you missed the nav," "the padding is off" — fix the issue, then note briefly: "logged that miss so clearshot gets better at catching [the specific thing]." Automatically write a field report (see below).

Trigger 2: After a rebuild completes. If Level 3 ran and the implementation is done, ask one casual question: "clearshot nailed it or missed something? just curious." One shot. Not a form.

Trigger 3: Session wind-down. If 3 or more analyses happened in a single session and the conversation is winding down, append: "ran clearshot X times this session. anything it kept getting wrong?" Only if 3+ analyses occurred. Never mid-flow.

Never trigger feedback: during rapid iteration, after every single analysis, or when the user is clearly in flow.

Field reports

Write to ~/.clearshot/feedback/YYYY-MM-DD-{slug}.md, only when:

  • User correction: automatic. Format:
# {Title describing the miss}
**What was analyzed:** {screenshot description}
**Levels run:** {1,2 or 1,2,3}
**What was missed:** {specific element or detail the user corrected}
**Correction:** {what the user said}
**Internal rating:** {X}/10
**Date:** {YYYY-MM-DD} | **Version:** {version from preamble}
  • User explicitly says something was wrong (via trigger 2 or 3 response): write a field report with the user's feedback included.
  • Internal rating below 5: write a field report silently.

Field reports are never written for routine analyses that went fine.

Epilogue

After analysis is complete, log the event. Substitute actual values for the placeholder variables.

_CS_TEL_END=$(date +%s)
_CS_DUR=$(( _CS_TEL_END - _CS_TEL_START ))
_CS_TEL_MODE=$(grep -E '^telemetry:' "$HOME/.clearshot/config.yaml" 2>/dev/null | awk '{print $2}' | tr -d '[:space:]' || echo "off")
if [ "$_CS_TEL_MODE" != "off" ]; then
  _CS_OS="$(uname -s | tr '[:upper:]' '[:lower:]')"
  _CS_ARCH="$(uname -m)"
  _CS_INSTALL_ID="$(printf '%s-%s' "$(hostname)" "$(whoami)" | shasum -a 256 | awk '{print $1}')"
  _CS_ID_JSON="\"$_CS_INSTALL_ID\""
  printf '{"v":1,"ts":"%s","version":"%s","os":"%s","arch":"%s","duration_s":%s,"outcome":"%s","levels_run":"%s","self_rating":%s,"installation_id":%s}\n' \
    "$(date -u +%Y-%m-%dT%H:%M:%SZ)" "CS_VERSION" "$_CS_OS" "$_CS_ARCH" \
    "$_CS_DUR" "OUTCOME" "LEVELS_RUN" "RATING" "$_CS_ID_JSON" \
    >> "$HOME/.clearshot/analytics/usage.jsonl" 2>/dev/null || true

  # Sync to Convex (rate-limited, background)
  _CS_CONVEX_URL=""
  for _csd in "$HOME/.claude/skills/clearshot" "$HOME/.agents/skills/clearshot"; do
    [ -f "$_csd/config.sh" ] && _CS_CONVEX_URL="$(grep -E '^CS_CONVEX_URL=' "$_csd/config.sh" 2>/dev/null | cut -d'"' -f2 || true)" && break
  done
  if [ -n "$_CS_CONVEX_URL" ] && [ "$_CS_CONVEX_URL" != "https://placeholder.convex.site" ]; then
    _CS_RATE="$HOME/.clearshot/analytics/.last-sync-time"
    _CS_SYNC_STALE=$(find "$_CS_RATE" -mmin +5 2>/dev/null || echo "sync")
    if [ ! -f "$_CS_RATE" ] || [ -n "$_CS_SYNC_STALE" ]; then
      _CS_CURSOR_FILE="$HOME/.clearshot/analytics/.last-sync-line"
      _CS_CURSOR=$(cat "$_CS_CURSOR_FILE" 2>/dev/null | tr -d '[:space:]' || echo "0")
      _CS_TOTAL=$(wc -l < "$HOME/.clearshot/analytics/usage.jsonl" 2>/dev/null | tr -d ' ' || echo "0")
      if [ "$_CS_CURSOR" -lt "$_CS_TOTAL" ] 2>/dev/null; then
        _CS_SKIP=$(( _CS_CURSOR + 1 ))
        _CS_BATCH=$(tail -n "+$_CS_SKIP" "$HOME/.clearshot/analytics/usage.jsonl" | head -100)
        _CS_JSON_BATCH="[$(echo "$_CS_BATCH" | paste -sd ',' -)]"
        _CS_HTTP=$(curl -s -o /dev/null -w '%{http_code}' --max-time 10 \
          -X POST "$_CS_CONVEX_URL/telemetry" \
          -H "Content-Type: application/json" \
          -d "$_CS_JSON_BATCH" 2>/dev/null || echo "000")
        case "$_CS_HTTP" in 2*) echo $(( _CS_CURSOR + $(echo "$_CS_BATCH" | wc -l | tr -d ' ') )) > "$_CS_CURSOR_FILE" ;; esac
        touch "$_CS_RATE" 2>/dev/null || true
      fi
    fi
  fi
fi

Replace these placeholders with actual values from the analysis:

  • _CS_TEL_START — the value from preamble output
  • CS_VERSION — the version from preamble output
  • OUTCOME — "success", "error", or "abort"
  • LEVELS_RUN — "1,2" or "1,2,3"
  • RATING — the self-rating number (0-10)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.95%
按下载量换算187

Claude

31.36%
按下载量换算168

Cursor

18.22%
按下载量换算98

Gemini CLI

8.82%
按下载量换算47

安全审计

Gen Agent Trust Hub

未通过

Socket

可疑

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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