Borrowing it
Nothing to install: this file belongs to Daegyu519/portfolio-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Daegyu519/portfolio-agent/main/.claude/commands/retrospect.mdgit clone --depth 1 https://github.com/Daegyu519/portfolio-agentWrote 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/commands/daegyu519/portfolio-agent/retrospect)<a href="https://agentmods.dev/commands/daegyu519/portfolio-agent/retrospect"><img src="https://agentmods.dev/badge/commands/daegyu519/portfolio-agent/retrospect/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/commands/daegyu519/portfolio-agent/retrospect"><img src="https://agentmods.dev/badge/commands/daegyu519/portfolio-agent/retrospect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00029 | $0.00186 |
| Opus 5 | $0.00015 | $0.00093 |
| Sonnet 5 | $0.00006 | $0.00037 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
retrospect 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 8d 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.
What it actually says
python -m src.cli.brief retrospect 를 Bash 로 실행하라 (인자 그대로 전달 — 예: --days 30).
리포트 마크다운을 그대로 보여주고 저장 위치(stderr 의 [저장: …] 라인)를 알려라. 리포트에 없는 인과 해석·교훈·판단을 추가로 생성하지 마라 — 이 시스템의 회고는 관찰 전용이고 해석은 사람이 한다 (CLAUDE.md 과신 금지 규율).
"다음 액션" 에 결과 미기록 id 가 있으면 brief outcome <id> --outcome "..." 로
채우라는 안내만 덧붙여라.
$ARGUMENTS
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.
- 8d ago First seen · 15 lines · 29 tokens per session scan A 18a85c98a2e9
retrospect is a command published in the GitHub repository Daegyu519/portfolio-agent (0 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 186 once invoked, about $0.0001 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.
Other commands, from other repositories
portfolio
View TastyTrade portfolio summary (positions and balances).
security-audit
Full security pass - verify, validate and harden this repo against its real threat model.
sages
Summon the Market Sages council to analyze a stock (e.g. /sages AAPL, /sages NVDA --value).
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.