Tonic System Deploy

Software deployment workflow for systems with separate UAT and PROD environments. Use when: planning a bug fix deployment, choosing between Flow 1 (UAT-first...

Security Scan

The skill reads like a deployment runbook (benign intent) but the instructions refer to automated actions and external integrations (Telegram, deploy/merge steps) without declaring required credentials, endpoints, or tooling — that mismatch is concerning.

Purpose & Capability

The name and description claim a deployment workflow for UAT/PROD with automation and Telegram notifications. However, the skill declares no required binaries, no environment variables, and no install steps. For a workflow that claims to perform automated deploys and send Telegram messages, we'd expect declared integration credentials (e.g., CI/CD API tokens, SSH keys, Telegram bot token/chat id) and/or required tooling. The absence of those required artifacts is an incoherence: either the skill is only a human-facing playbook (OK) or it intends to execute actions but fails to declare necessary capabilities/credentials.

Instruction Scope

SKILL.md instructs an agent to 'AI analyses root cause + records fix plan', 'Deploys fix to UAT/PROD', and 'Telegram: "Fix deployed to UAT"' — i.e., networked actions and side effects. The instructions are vague about how/where to run deploys, where plans are recorded, and which Telegram endpoints to use. They grant broad discretion to an agent (e.g., 'AI analyses root cause') without bounded constraints, and they reference sending data externally but provide no destination or authorization details.

Install Mechanism

There is no install spec and no code files; the skill is instruction-only. This is the lowest install risk because nothing is written to disk by the skill itself.

Credentials

The instructions imply need for secrets (CI/CD credentials, deploy keys, Telegram bot token, possibly cloud provider credentials) but the skill declares none. That mismatch is disproportionate: the documented behavior would normally require multiple secrets and scoped access, yet none are requested or documented.

Persistence & Privilege

The skill does not request 'always: true' and has no install-time persistence. Model invocation is allowed (platform default) but there is no evidence the skill modifies other skills or system-wide settings. This dimension is not a concern by itself.

What to consider before installing

This appears to be a deployment playbook rather than executable automation, but it mentions sending Telegram notifications and performing deploy/merge operations without listing any required credentials, endpoints, or tools. Before installing or enabling this skill for autonomous use: 1) Confirm whether it is intended as a human-facing checklist only — if so, treat it as documentation and keep autonomous invocation disabled. 2) If you plan to let the agent perform deploys or send Telegram messages, require explicit, minimal credentials (scoped CI/CD service token, Telegram bot token + chat id, deploy keys) and restrict them to least privilege. 3) Ask the publisher for concrete integration details (what CI/CD, how deploys are executed, where 'records' are stored, rollback commands). 4) Avoid pasting high-privilege secrets into the agent without reviewing and limiting their scope; prefer manual gates for PROD actions. If you cannot obtain those clarifications, do not grant this skill network or credential access and use it only as a manual runbook.

Software Deployment Workflow — Dual-Environment (UAT + PROD)

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Background & Design Rationale

This skill was designed for systems where:

• Two live environments co-exist: UAT (testing/staging) and PROD (production)
• Versions can diverge: UAT may be ahead of PROD by several releases
• Deployments are nightly: automated pipelines run at scheduled times
• Human approval is mandatory: no code goes to PROD without explicit admin sign-off
• Bugs require structured triage: severity, origin environment, and version state all affect the deploy path

The key insight: choosing the wrong deploy flow when versions are mismatched can introduce regressions. Flow 1 assumes parity; Flow 2 handles divergence safely.

---

Prerequisites — Before Choosing a Flow

Step 0: Version Check (always do this first)

Question	Answer →
Are UAT and PROD on the same version?	→ Flow 1
Is UAT ahead of PROD by any version?	→ Flow 2
Is this a critical/high severity bug?	→ Emergency Hotfix (bypass pipeline)
Do you need to undo a bad deploy?	→ Rollback

Version Mismatch Decision Tree

Bug found
    │
    ├─ severity = critical/high?
    │       └─ YES → Emergency Hotfix (skip pipeline)
    │
    ├─ UAT version == PROD version?
    │       └─ YES → Flow 1
    │
    └─ UAT version > PROD version?
            └─ YES → Flow 2

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Flow 1 — UAT-First (Versions Aligned)

Scenario: Bug found in UAT or PROD when both environments run the same version. Goal: Fix → validate in UAT → promote to PROD. Result: UAT and PROD converge to same patched version.

Timeline

Bug reported
    │
    │ 🧑 HUMAN: Admin reviews + confirms bug (status: confirmed)
    ▼
[confirmed] — severity: low/medium only
    │
    │ 🤖 SYSTEM: Scheduled deploy time T1 (e.g. 20:00)
    │   - AI analyses root cause + records fix plan
    │   - Deploys fix to UAT environment
    │   - Status → deployed_uat
    ▼
[deployed_uat]
    │ 📲 Telegram: "Fix deployed to UAT. Please validate."
    │
    │ 🧑 HUMAN: Admin logs into UAT, validates fix
    │   - Runs through affected workflows
    │   - Confirms no regression
    │   - Clicks "Approve PROD Deploy" → status: pending_prod
    ▼
[pending_prod]
    │ 📲 Telegram: "Queued for PROD at T2."
    │
    │ 🤖 SYSTEM: Scheduled deploy time T2 (e.g. 22:00)
    │   - Deploys fix to PROD environment
    │   - Status → deployed_prod
    ▼
[deployed_prod] ✅ Flow 1 Complete
    │ 📲 Telegram: "Deployed to PROD. Flow 1 complete."

Human Checkpoints (Flow 1)

Checkpoint	Who	Action	Gate Condition
Confirm bug	Admin/Manager	Mark as confirmed	Bug is reproducible and valid
UAT validation	Admin/Manager	Click "Approve PROD Deploy"	Fix works, no regression in UAT

Automation Nodes (Flow 1)

Time	Node	Input Status	Output Status	Action
T1	Phase 1	confirmed/planned	deployed_uat	AI analysis + UAT deploy
T2	Phase 2	pending_prod	deployed_prod	PROD deploy

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Flow 2 — PROD-First (Versions Misaligned)

Scenario: Bug found in PROD when UAT is ahead by one or more versions. Why not Flow 1? Validating a PROD fix in a newer UAT environment risks false confidence — the fix may behave differently on the older PROD codebase. Goal: Fix PROD directly → validate in PROD → cherry-pick back to UAT. Result: PROD gets the fix immediately; UAT gets it merged back after PROD validation.

Timeline

Bug found in PROD (UAT is ahead)
    │
    │ 🧑 HUMAN: Admin reviews + confirms bug
    │   - Selects: found_in_env = prod, fix_flow = flow2
    │   - Status → confirmed
    ▼
[confirmed]
    │
    │ 🤖 SYSTEM: Scheduled deploy time T1 (e.g. 20:00)
    │   - AI analyses root cause + records fix plan
    │   - Skips UAT entirely
    │   - Queues for PROD deploy → status: pending_prod
    ▼
[pending_prod]
    │ 📲 Telegram: "PROD deploy queued for T2 (Flow 2)."
    │
    │ 🤖 SYSTEM: Scheduled deploy time T2 (e.g. 22:00)
    │   - Deploys fix to PROD
    │   - Status → deployed_prod
    ▼
[deployed_prod]
    │ 📲 Telegram: "Deployed to PROD. Please validate PROD. Approve UAT merge when ready."
    │
    │ 🧑 HUMAN: Admin validates fix in PROD
    │   - Confirms fix works on production data/config
    │   - No regression in PROD workflows
    │   - Clicks "Approve Merge UAT" → status: pending_uat_merge
    ▼
[pending_uat_merge]
    │ 📲 Telegram: "UAT merge queued for T2 tonight."
    │
    │ 🤖 SYSTEM: Next T2 cycle (22:00)
    │   - Deploys/merges fix into UAT environment
    │   - Status → uat_merged
    ▼
[uat_merged] ✅ Flow 2 Complete
    │ 📲 Telegram: "Merged to UAT. Flow 2 complete."

Human Checkpoints (Flow 2)

Checkpoint	Who	Action	Gate Condition
Confirm bug	Admin/Manager	Mark as confirmed + select flow2	Bug confirmed in PROD, version mismatch verified
PROD validation	Admin/Manager	Click "Approve Merge UAT"	Fix verified in PROD, no regression

Automation Nodes (Flow 2)

Time	Node	Input Status	Output Status	Action
T1	Phase 1	confirmed/planned	pending_prod	AI analysis (skip UAT)
T2	Phase 2a	pending_prod	deployed_prod	PROD deploy
T2 (next)	Phase 2b	pending_uat_merge	uat_merged	UAT deploy/merge

Flow 2 Important Note

T2 deadline matters. If admin approves UAT merge before T2 on the same day, the merge runs that night. If approved after T2, it runs the following night's T2. Always communicate the cutoff time to the team.

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Status Reference

Status	Flow	Colour	Meaning	Next Action
submitted	Both	Grey	Bug reported, awaiting review	Admin confirms/rejects
confirmed	Both	Blue	Valid bug, enters pipeline	T1 auto-process
analyzing	Both	Purple	AI analysis running (transient)	Auto → planned
planned	Both	Indigo	AI fix plan recorded	T1 auto-deploy
deployed_uat	Flow 1	Cyan	UAT deployed, awaiting human validation	Admin approves PROD
pending_prod	Both	Yellow	Queued for PROD at next T2	T2 auto-deploy
deployed_prod	Both	Green	PROD deployed	Flow1: done; Flow2: admin approves UAT merge
pending_uat_merge	Flow 2	Purple	Queued for UAT merge at next T2	T2 auto-merge
uat_merged	Flow 2	Teal	UAT updated with PROD fix	Flow 2 complete ✅
closed	Both	Emerald	Manually closed	—
rejected	Both	Red	Not a valid bug	—

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Severity Rules

Severity	Pipeline Eligible?	Notes
low	✅ Yes	Both flows
medium	✅ Yes	Both flows
high	❌ No	Emergency Hotfix only
critical	❌ No	Emergency Hotfix, immediate escalation

Never let high/critical bugs wait for a scheduled pipeline. Treat them as emergency hotfixes.

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Emergency Hotfix (Bypass Pipeline)

Scenario: Critical or high severity bug in PROD. Cannot wait for scheduled T1/T2.

Process

Critical bug found in PROD
    │
    │ 🧑 HUMAN: Admin confirms severity = critical/high
    │   - Does NOT enter pipeline (no confirmed status)
    │   - Opens direct hotfix branch
    ▼
Fix developed (manually or with AI assist)
    │
    │ 🧑 HUMAN: Admin deploys directly to PROD
    │   - Updates bug status to deployed_prod manually
    │   - Records fix details in ai_fix_diff field
    ▼
[deployed_prod] (manual)
    │ 📲 Telegram: "Emergency hotfix deployed to PROD. [Bug title]"
    │
    │ 🧑 HUMAN: Validates PROD immediately
    │
    └─ If UAT is ahead → manually cherry-pick to UAT branch
       If UAT is same version → update UAT as well
    ▼
✅ Emergency resolved

Checklist for Emergency Hotfix

• Severity confirmed as critical/high before bypassing pipeline
• At least one other team member notified before deploy
• Fix deployed and validated within agreed SLA (e.g. P1: 1 hour, P2: 4 hours)
• Post-deploy smoke test completed (login, core workflow, affected feature)
• Bug status updated manually in system
• Telegram/Slack notification sent to stakeholders
• Post-incident note added to bug record (root cause, fix summary)
• UAT updated (cherry-pick or re-sync if needed)
• Incident review scheduled (within 48h for P1)

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Rollback Procedure

Scenario: A deploy (T1 or T2) introduces a regression or new failure.

Decision: When to Rollback

Issue detected after deploy
    │
    ├─ Severity: minor UX glitch → Monitor, schedule fix in next pipeline
    │
    ├─ Severity: functional regression → Rollback immediately
    │
    └─ Severity: data corruption risk → Rollback + escalate + engage DBA

Rollback Process

Regression detected post-deploy
    │
    │ 🧑 HUMAN: Admin confirms rollback needed
    │   - Record: what was deployed, when, what broke
    ▼
    │ 🤖 SYSTEM or 🧑 HUMAN: Execute rollback
    │   - Docker: docker compose down && git checkout <prev_tag> && docker compose up
    │   - DB migration: apply down migration if schema changed
    │   - Status of affected bugs → revert to previous status
    ▼
    │ 📲 Telegram: "Rollback executed for [version]. Monitoring."
    │
    │ 🧑 HUMAN: Post-rollback validation (5–10 min smoke test)
    │
    └─ Stable → document in HISTORY + schedule proper fix
       Unstable → escalate

Rollback Checklist

• Previous working commit/tag identified (git log)
• Rollback scope defined (frontend / backend / both / DB)
• Affected bug statuses reverted in system
• Smoke test completed after rollback
• Root cause of regression documented
• Team + stakeholders notified
• Fix plan for the reverted change recorded

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Scheduled Deploy Times (Reference Only)

⚠️ These times are project-specific. Adapt per project SLA and business hours.

Slot	Name	Phase	Typical Window
T1	UAT/PROD-queue Deploy	Phase 1	Off-peak evening (e.g. 20:00)
T2	PROD/UAT-merge Deploy	Phase 2	Late evening (e.g. 22:00)

Principles for choosing T1/T2:

• T1 and T2 must have enough gap for human validation (minimum 1–2 hours)
• Both should be outside business hours unless urgency demands otherwise
• For 24/7 systems: choose lowest traffic window (check metrics)
• Emergency hotfix: no scheduled time — deploy ASAP after approval

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Telegram Notification Templates

Use these as the standard message format for each pipeline node.

T1 Complete — Flow 1 (UAT deployed)

🔧 Bug Fix Pipeline — Flow 1 已部署 UAT

• #<id> [<severity>] <title>
• ...

📦 Release: <version>

✅ 請驗收 UAT：<UAT_URL>
確認後請點「批准推 PROD」。
下次 T2 時間：<T2_time>

T2 Complete — Flow 1 (PROD deployed)

🚀 Bug Fix Pipeline — Flow 1 已部署 PROD

• #<id> [<severity>] <title>

✅ Flow 1 完成。UAT 與 PROD 版本已對齊。

T1 Complete — Flow 2 (PROD queued)

🔧 Bug Fix Pipeline — Flow 2 已排隊部署 PROD

• #<id> [<severity>] <title>

⏳ 今晚 T2（<T2_time>）自動部署至 PROD。
部署後請驗收，確認後點「批准 Merge UAT」。

T2 Complete — Flow 2 (PROD deployed, UAT pending)

🚀 Bug Fix Pipeline — Flow 2 已部署 PROD

• #<id> [<severity>] <title>

⚠️ 請登入 PROD 驗收。
確認正常後點「批准 Merge UAT」。
下次 T2 時間：<T2_time>

T2 Complete — Flow 2 (UAT merged)

✅ Bug Fix Pipeline — Flow 2 完成

• #<id> [<severity>] <title>

🎉 Fix 已同步至 UAT。Flow 2 全流程完成。

Emergency Hotfix

🚨 Emergency Hotfix — <PROD/UAT>

Bug: #<id> [<severity>] <title>
時間：<datetime>
部署人：<admin>

狀態：已部署，請立即驗收
需要跟進：<yes/no — UAT cherry-pick needed>

Rollback

⚠️ Rollback 執行 — <environment>

原因：<brief reason>
回滾至：<version/commit>
時間：<datetime>
執行人：<admin>

狀態：已回滾，正在監控
下一步：<scheduled fix / investigation>

---

Adapting This Workflow to a New Project

When setting up a new project with this workflow:

1. Define T1 and T2 — pick times based on traffic patterns and SLA
2. Set severity policy — confirm which severities enter pipeline vs emergency hotfix
3. Configure Telegram/notification channels — who receives which notifications
4. Add DB columns — fix_flow, found_in_env, and status enum (see Status Reference)
5. Implement Phase 1 + Phase 2 cron jobs — schedule at T1 and T2
6. Add approval endpoints — approve-prod, approve-uat-merge, batch variants
7. Add status badges + action buttons — frontend must reflect all statuses clearly
8. Test the full cycle in UAT first — simulate a bug through both flows before going live
9. Document rollback steps — specific to the project's tech stack and DB

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Pre-Deploy Checklist (T1 / T2)

Run before every scheduled deploy window.

• DB backup confirmed — last backup < 24h, or trigger manual backup now
• Monitoring alerts active — error rate, response time, queue depth dashboards open
• On-call admin reachable — someone available to respond within 15 min post-deploy
• Change freeze check — not within a freeze window (see Change Freeze Policy)
• Rollback path clear — previous working commit/tag identified and noted
• Dependent services healthy — upstream/downstream APIs, DBs, message queues OK
• Disk + memory OK — server has headroom (>20% free disk, <80% memory)

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Post-Deploy Monitoring

After each T1 or T2 deploy, monitor for a minimum of 10 minutes before standing down.

Metrics to Watch

Metric	Healthy Threshold	Action if Breached
HTTP 5xx error rate	< 0.5%	Investigate immediately, consider rollback
API response time (p95)	< baseline + 20%	Check DB queries, cache hit rate
Memory usage	< 85%	Check for memory leaks in new code
CPU usage	< 80% sustained	Check for infinite loops or expensive queries
Login / auth success rate	> 99%	Auth regression — rollback candidate
Key business flow (e.g. task create)	Working end-to-end	Smoke test immediately post-deploy

Smoke Test Sequence (2–3 min)

1. Login with admin account
2. Navigate to the affected feature
3. Perform the action that triggered the bug
4. Confirm fix is working
5. Check 2–3 adjacent features for regression
6. Check system logs for new errors

If any smoke test step fails → rollback immediately, do not wait.

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Multi-Service Deploy (Cross-Service Fixes)

Scenario: A bug fix requires changes to more than one service (e.g. backend API + frontend, or service A + service B).

Deploy Order Principle

Always deploy in dependency order:
  Backend (API) first → Frontend second
  Shared library → Dependent services
  DB migration → Application code

Never deploy in reverse order — it risks breaking in-flight requests.

Coordination Steps

1. Map dependencies — list all services affected and their dependency order
2. Stage the deploys — do not deploy all services simultaneously
3. Validate between services — after each service deploy, quick health check before next
4. Single rollback plan — define the exact reverse order and what to check at each step
5. Lock window — communicate to team that a multi-service deploy is in progress (no other deploys)

Status Tracking for Multi-Service

Tag the bug with affected services. Use release notes to list which service each fix applies to:

[backend] Fix: null pointer in task update handler
[frontend] Fix: error boundary not catching API timeout

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Change Freeze Policy

Certain periods should have no scheduled pipeline deploys (T1/T2 suspended). Emergency hotfixes may still be approved by escalation.

Recommended Freeze Windows

Period	Recommended Action
Public holidays	Suspend T1/T2. Emergency hotfix requires 2-person approval.
Lunar New Year (3 days)	Full freeze. P1 only with CTO sign-off.
Major client go-live week	Freeze for that client's system. Other systems normal.
End-of-month financial close	Freeze financial modules. Other modules normal.
Planned system maintenance	Coordinate freeze window in advance, notify stakeholders.

Declaring a Freeze

1. Update HEARTBEAT.md or project config with freeze start/end dates
2. Notify team via Telegram/channel
3. Pipeline cron jobs remain scheduled but agent checks freeze flag before executing
4. Emergency hotfix during freeze: requires explicit approval from admin + one other senior (two-person rule)

Freeze Flag (implementation)

In pipeline config or environment variable:

DEPLOY_FREEZE=true              # hard freeze, all deploys blocked
DEPLOY_FREEZE_MODULES=financial # module-specific freeze
DEPLOY_FREEZE_UNTIL=2026-02-05  # auto-lift date

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Quick Reference Card

FLOW SELECTION:
  Same version?  → Flow 1
  UAT ahead?     → Flow 2
  Critical/High? → Emergency Hotfix
  Freeze window? → Block (escalate for emergency)

FLOW 1: confirmed → [T1] deployed_uat → [human] pending_prod → [T2] deployed_prod ✅
FLOW 2: confirmed → [T1] pending_prod → [T2] deployed_prod → [human] pending_uat_merge → [T2] uat_merged ✅

ROLLBACK: detect → confirm → execute → validate → document

SEVERITY:
  low/medium  → pipeline eligible
  high/critical → emergency hotfix only

MULTI-SERVICE: backend first → validate → frontend → validate

PRE-DEPLOY: backup ✓ monitoring ✓ on-call ✓ freeze-check ✓ rollback-path ✓
POST-DEPLOY: monitor 10min → smoke test → stand down