Borrowing it
Nothing to install: this file belongs to Nam-Cheol/namba-ai. 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/Nam-Cheol/namba-ai/main/.agents/skills/namba-release/SKILL.mdgit clone --depth 1 https://github.com/Nam-Cheol/namba-aiWrote 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/skills/nam-cheol/namba-ai/namba-release)<a href="https://agentmods.dev/skills/nam-cheol/namba-ai/namba-release"><img src="https://agentmods.dev/badge/skills/nam-cheol/namba-ai/namba-release/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/skills/nam-cheol/namba-ai/namba-release"><img src="https://agentmods.dev/badge/skills/nam-cheol/namba-ai/namba-release.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 | $0.00021 | $0.00672 |
| Opus 5.5 | $0.00008 | $0.00269 |
| Sonnet 5.5 | $0.00004 | $0.00134 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
namba-release 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 2mo 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
State effect: mutating workflow entry point. Use help/probe paths read-only, and otherwise expect repository state or GitHub state to change.
Generated instruction contract for this command skill:
- Purpose: keep the role or command scope explicit, bounded, and testable.
- Boundary: honor read-only versus mutating state effects, configured sandbox mode, and assigned file or workflow ownership.
- Required output: report concrete actions, changed paths or artifacts, validation evidence, and pass/fail status or blockers.
- Pass/fail criteria: claim success only when acceptance criteria and configured validation are satisfied; otherwise name the exact blocker and impact.
- Evidence expectations: cite source artifacts such as SPEC files,
.namba/configs, diffs, test output, PR/check links, or generated manifests instead of relying on unsupported assertions. - Security responsibilities: never expose or commit secrets; treat auth, privacy, destructive commands, permission changes, and external network or credential use as security-sensitive.
- Destructive command and escalation policy: do not run destructive commands unless explicitly requested; request approval for privileged, networked, or sandbox-blocked actions only when the active approval mode allows it, and otherwise report the blocker or use a safe non-escalating path.
- Fallback implementer boundary: if a specialist path is unavailable and the main/default implementer takes over, stay within the assigned scope and preserve the same evidence and validation duties.
- Portability: keep durable guidance non-project-specific unless the current repository config or SPEC explicitly provides the project detail.
Use this skill when the user explicitly says $namba-release, namba release in a Codex workflow context, or Korean wording such as 릴리즈 진행해.
Behavior:
- Treat this as NambaAI-specific release orchestration, not a generic release helper.
- Start from
mainand require a clean working tree before the final tagging step. - If generated templates or docs changed during release prep, run
namba regenand/ornamba sync, validate, and commit those changes before tagging. - Determine the target version from explicit input or the next semver bump.
- Collect commits since the previous semver tag, ignoring merge noise and excluding any release-note prep commit when the notes artifact is committed separately.
- Draft release notes from that commit range and group them into user-visible changes, fixes, docs/workflow, and internal maintenance while preserving SPEC IDs, PR numbers, and short commit hashes when useful.
- Release notes must describe actual changes and include evidence sources such as SPEC IDs, PR numbers, short commit hashes, source artifacts, and validation results when available.
- Write the notes to a durable per-version artifact such as
.namba/releases/<version>.md, then use that file as the handoff for the guardednamba release --version <version> --pushpath. - Write release-facing prose in the explicit user-requested language when one is given, such as
--language en; otherwise use the init or project-configured language. - Do not tag until the notes exist and validation has passed.
- Make sure the GitHub Release body uses the generated notes rather than an empty or generic body.
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.
- 2mo ago First seen · 33 lines · 21 tokens per session scan A 4770f8ad4e98
namba-release is a skill published in the GitHub repository Nam-Cheol/namba-ai (11 stars, last pushed 12d ago), licensed MIT. It adds 21 tokens to every session and 672 once invoked, about $0.0001 per session on Opus 5.5. 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.
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