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.
npx agentmods add skills/sehoon787/my-codex/gstack-sprintnpx skills add sehoon787/my-codex --skill gstack-sprintgit clone --depth 1 https://github.com/sehoon787/my-codexWhat 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 | $0.00022 | $0.02228 |
| Opus 5 | $0.00011 | $0.01114 |
| Sonnet 5 | $0.00004 | $0.00446 |
| Haiku 4.5 | $0.00002 | $0.00223 |
Grade A, and why
gstack-sprint 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gstack-sprint — 91% identical, 30 lines differ
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Use_When>
- End-to-end feature implementation ("build this feature", "design it and implement it")
- Build or Mid-sized intent type with implementation phase included
- User says "sprint", "end-to-end", "e2e implementation" </Use_When>
<Do_Not_Use_When>
- Pure design/planning/idea review without implementation → use /office-hours or /plan-ceo-review directly
- Architecture intent type (design only, no build)
- "just design it", "just plan it", "review my idea" — these route to /office-hours or /plan-ceo-review
- Single-purpose requests: code review → /review, QA → /qa, deploy → /ship
- Trivial fixes, research, documentation </Do_Not_Use_When>
<Why_This_Exists> End-to-end feature work fails silently in three predictable ways:
- Implementation starts before design is aligned — wrong thing built
- Execution proceeds without structured iteration — partial implementations declared done
- Review skips comparison against the design doc — drift goes unnoticed
gstack-sprint enforces the three-phase contract: user-confirmed design → automated execution via ralph → user-confirmed review against the design doc. Each phase boundary requires explicit user confirmation before the next phase begins. </Why_This_Exists>
Phase 1: Design (interactive — user decisions)
-
Determine scale from the user's request:
- Large (new feature, cross-system architecture, significant refactor) → invoke /plan-ceo-review first, then proceed to step 2
- Medium (scoped feature, single-service change) → skip /plan-ceo-review, proceed directly to step 2
-
Invoke /plan-eng-review (mandatory for all scales) to produce a structured engineering design document
-
Surface all key decisions using AskUserQuestion for each ambiguity:
- Technology choices with tradeoffs
- API contract decisions
- Data model decisions
- Scope boundary decisions
- Do not batch decisions silently — each decision that affects implementation requires explicit user input
-
Wait for user to confirm "design complete" before transitioning to Phase 2. Do not proceed to Phase 2 on your own judgment.
-
Skip condition: If the user's original message already confirms design is done ("design is done, just build it") → skip Phase 1 entirely and proceed to Phase 2 with the provided design context
-
Fallback (gstack not installed): Use the OMC planner agent (opus) to produce a structured plan. Present the plan to the user and wait for their confirmation before proceeding.
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.
- 3d ago First seen · 186 lines · 22 tokens per session scan A 33c50cd98508
gstack-sprint is a skill published in the GitHub repository sehoon787/my-codex (25 stars, last pushed 5d ago), licensed MIT. It adds 22 tokens to every session and 2,228 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 2026-08-30.
Other skills, from other repositories
loop
Use only when user explicitly invokes $loop.
agent-orchestrator-v2
Agent Orchestrator workflow skill. Use this skill when the user needs Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management and the operator should preserve the upstream workflow, copied support files, and…
agent-orchestration-multi-agent-optimize-v2
Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow…
compact-kb
Use only when user explicitly invokes $compact-kb.
manage-skills
세션 변경사항을 분석하여 검증 스킬 누락을 탐지합니다. 기존 스킬을 동적으로 탐색하고, 새 스킬을 생성하거나 기존 스킬을 업데이트한 뒤 CLAUDE.md를 관리합니다.
release
Bump package.json version and create a git tag for npm release. Accepts semver bump type (patch/minor/major) or explicit version.