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 skills add daffy0208/ai-dev-standards --skill release-managergit clone --depth 1 https://github.com/daffy0208/ai-dev-standardsWrote 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/daffy0208/ai-dev-standards/release-manager)<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/release-manager"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/release-manager/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/daffy0208/ai-dev-standards/release-manager"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/release-manager.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 | $0.00030 | $0.00885 |
| Opus 5 | $0.00015 | $0.00443 |
| Sonnet 5 | $0.00006 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
release-manager 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.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Release Manager
Ship features safely with progressive rollouts.
Progressive Rollout Strategy
Phase 1 - Internal (Day 1):
- 100% to internal team
- Test thoroughly
- Fix critical bugs
Phase 2 - Beta (Day 2-3):
- 5% to beta users
- Monitor errors/performance
- Collect feedback
Phase 3 - Gradual (Day 4-7):
- 25% of users
- Watch metrics closely
- 50% of users if good
- 100% if still good
Phase 4 - Full Release:
- 100% of users
- Remove feature flag
- Announce publicly
Feature Flags
// Feature flag implementation
const featureFlags = {
newDashboard: {
enabled: true,
rollout: 0.25, // 25% of users
userGroups: ['beta-testers'], // Always on for beta
}
}
function isFeatureEnabled(feature, user) {
const flag = featureFlags[feature]
// Check user group
if (user.groups.some(g => flag.userGroups.includes(g))) {
return true
}
// Check rollout percentage
const hash = hashUserId(user.id)
return (hash % 100) < (flag.rollout * 100)
}
// Usage
{isFeatureEnabled('newDashboard', user) ? (
<NewDashboard />
) : (
<OldDashboard />
)}
Deployment Strategies
Blue-Green Deployment
Process: 1. Deploy to "green" environment
2. Test green thoroughly
3. Switch traffic to green
4. Keep blue as rollback
Pros: Instant rollback
Cons: 2x infrastructure cost
Canary Deployment
Process: 1. Deploy to 5% of servers
2. Monitor for 1 hour
3. If good, deploy to 25%
4. Monitor for 1 hour
5. If good, deploy to 100%
Pros: Gradual, safe
Cons: Slower rollout
Rollback Plan
Criteria for Rollback:
- Error rate > 1%
- Performance degradation > 20%
- Critical bug discovered
- Negative user feedback
Rollback Process: 1. Disable feature flag immediately
2. Notify team
3. Investigate issue
4. Fix and redeploy
Release Checklist
Pre-Release
- Code reviewed
- Tests passing
- Staging tested
- Feature flag configured
- Rollback plan ready
- Monitoring alerts set
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 175 lines · 30 tokens per session scan A 8d6664e312d3
release-manager is a skill published in the GitHub repository daffy0208/ai-dev-standards (36 stars, last pushed 8mo ago), licensed MIT. It adds 30 tokens to every session and 885 once invoked, about $0.0002 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 2026-09-03.
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