simon-boost-capture

simon-boost-capture is a skill for Claude Code, Codex from SW-in-beta/simon-skills. It costs 183 tokens per session (1,578 once invoked), scanned A, original, MIT.

A background capture tool for recording ideas about improving a coding-agent skill. It takes a short snapshot of the current problem and sends detailed analysis to a later review process.

In plain words
What is it for?
Use it when a skill behaves inefficiently, needs manual correction, or exposes a repeated issue. It records the affected skill, workflow stage, observation, and suggested direction.
Why use it?
It lets work continue while a recurring problem or improvement idea is documented for later. This avoids stopping the current task just to investigate how the skill should change.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for gstack. Also seen: model in frontmatter; reads .claude/ paths; mentions subagents.

Good fit Use it when a skill behaves inefficiently, needs manual correction, or exposes a repeated issue. It records the affected skill, workflow stage, observation, and suggested direction.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sw-in-beta/simon-skills/simon-boost-capture
Install

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.

Any agent
npx skills add SW-in-beta/simon-skills --skill simon-boost-capture
Clone the repo
git clone --depth 1 https://github.com/SW-in-beta/simon-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for simon-boost-capture

README.md
[![agentmods](https://agentmods.dev/badge/skills/sw-in-beta/simon-skills/simon-boost-capture/github.svg)](https://agentmods.dev/skills/sw-in-beta/simon-skills/simon-boost-capture)
Your own site
<a href="https://agentmods.dev/skills/sw-in-beta/simon-skills/simon-boost-capture"><img src="https://agentmods.dev/badge/skills/sw-in-beta/simon-skills/simon-boost-capture/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.

agentmods 80×15 button for simon-boost-capture

Your own site · 80×15
<a href="https://agentmods.dev/skills/sw-in-beta/simon-skills/simon-boost-capture"><img src="https://agentmods.dev/badge/skills/sw-in-beta/simon-skills/simon-boost-capture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 183 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,578 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00183 $0.01578
Opus 5 $0.00092 $0.00789
Sonnet 5 $0.00037 $0.00316
Haiku 4.5 $0.00018 $0.00158

Measured 10d ago against content hash eccc2dc89338, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

simon-boost-capture 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 10d 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.

skills/simon-boost-capture/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

simon-boost-capture

작업 중 발견한 스킬 개선점을 백그라운드로 분석·기록하는 경량 스킬.

세션 컨텍스트 오염을 최소화하기 위해 포그라운드 작업은 스냅샷 확인까지만 수행하고, 상세 분석은 백그라운드 Agent에 위임한다. 축적된 인사이트는 simon-boost의 Review 모드에서 일괄 처리한다.

Step 1: Context Snapshot

현재 세션의 대화 흐름에서 개선 관련 컨텍스트를 자동 추출한다.

추출 대상:

  • 실행 중인 스킬과 현재 단계 (Step)
  • 문제·비효율이 발생한 지점
  • 에러 메시지 (있다면)
  • 사용자가 수동 보정하거나 지적한 부분

스냅샷 제시:

캡처 스냅샷:
- 대상 스킬: {skill_name}
- 대상 섹션: {step/section}
- 관찰: {1-2줄 요약}
- 개선 방향: {1줄 제안}

이 내용으로 캡처할까요? (수정할 부분이 있으면 말씀해주세요)
  • 확인 → Step 2
  • 수정 요청 → 사용자 입력으로 대체 → Step 2

Step 2: Background Dispatch

확정된 스냅샷으로 백그라운드 Agent를 spawn하고, 현재 작업으로 즉시 복귀한다.

Agent spawn:

Agent(
    description="boost insight 리포트 작성",
    run_in_background=true,
    prompt=아래 템플릿
)

Agent Prompt 템플릿:

## 임무
스킬 개선 인사이트를 분석하고 리포트를 작성하라.

## 관찰 내용
{확정된 스냅샷 전체}

## 작업
1. 대상 스킬 파일 Read:
   - ~/.claude/skills/{target_skill}/SKILL.md
   - 스냅샷에서 언급된 섹션의 reference 파일 (있다면)

2. 관찰과 현재 스킬 코드를 대조 분석:
   - 문제의 근본 원인이 스킬의 어느 부분에 있는지
   - 구체적으로 어떤 코드/지시문을 어떻게 바꿔야 하는지
   - 변경이 다른 스킬에 전파되는지

3. ~/.claude/boost/insights/ 디렉토리에 리포트 저장 (없으면 mkdir -p로 생성)

## 리포트 형식
파일명: {YYYY-MM-DD}-{HHmm}-{slug}.md

내용:
---
status: pending
severity: CRITICAL | HIGH | MEDIUM | LOW  # 판정 기준: _shared/expert-panel-boost.md의 Proposal Severity Rubric
target_skill: {skill_name}
target_section: {step/section}
captured_at: {ISO datetime}
---

# {제목}

## 관찰
(세션에서 구체적으로 무슨 일이 있었는지)

## 현재 스킬 코드 분석
(해당 부분의 현재 코드, 왜 문제인지)
(파일 경로와 해당 섹션 명시)

## 개선 제안
(무엇을 어떻게 바꾸면 좋을지 — before/after 포함)

## 기대 효과
(변경 시 어떤 개선이 예상되는지)

## 영향 범위
(이 변경이 다른 스킬이나 워크플로에 미치는 영향)

사용자 안내 후 즉시 복귀:

백그라운드에서 분석 중입니다. 완료 시 ~/.claude/boost/insights/에 저장됩니다.

이 메시지 이후 캡처 관련 추가 출력을 하지 않는다. 사용자의 원래 작업으로 복귀한다.

Auto-Capture Mode

사용자 개입 없이 워크플로 비효율을 자동 감지하여 캡처한다. 세션당 최대 3건.

자동 감지 패턴:

  • 같은 Step에서 3회 이상 재시도 (동일 에러 반복)
  • 예상 소요 시간의 2배 이상 경과
  • 동일 파일을 3회 이상 반복 읽기 (필요한 정보를 찾지 못하는 징후)
  • subagent가 동일 실패를 2회 이상 보고

캡처 조건: 위 패턴 중 하나라도 감지되면, 현재 작업을 중단하지 않고 백그라운드로 인사이트 리포트를 .claude/boost/auto-captures/에 기록한다.

Read the full file on GitHub · 135 lines

Changes

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.

  1. 10d ago First seen · 135 lines · 183 tokens per session scan A eccc2dc89338

Subscribe to this mod's changes

simon-boost-capture is a skill published in the GitHub repository SW-in-beta/simon-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 183 tokens to every session and 1,578 once invoked, about $0.0009 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens