Kaitranntt Ccs Ccs Delegation

CCS Delegation

Delegate deterministic tasks to cost-optimized models via CCS CLI.

Core Concept

Execute tasks via alternative models using:

• Initial delegation: ccs {profile} -p "task"
• Session continuation: ccs {profile}:continue -p "follow-up"

Profile Selection:

• Auto-select from ~/.ccs/config.json via task analysis
• Profiles: glm (cost-optimized), kimi (long-context/reasoning), custom profiles
• Override: --{profile} flag forces specific profile

User Invocation Patterns

Users trigger delegation naturally:

• "use ccs [task]" - Auto-select best profile
• "use ccs --glm [task]" - Force GLM profile
• "use ccs --kimi [task]" - Force Kimi profile
• "use ccs:continue [task]" - Continue last session

Examples:

• "use ccs to fix typos in README.md"
• "use ccs to analyze the entire architecture"
• "use ccs --glm to add unit tests"
• "use ccs:continue to commit the changes"

Agent Response Protocol

For /ccs [task]:

1. Parse override flag

   • Scan task for pattern: --(\w+)
   • If match: profile = match[1], remove flag from task, skip to step 5
   • If no match: continue to step 2

2. Discover profiles

   • Read ~/.ccs/config.json using Read tool
   • Extract Object.keys(config.profiles) → availableProfiles[]
   • If file missing → Error: "CCS not configured. Run: ccs doctor"
   • If empty → Error: "No profiles in config.json"

3. Analyze task requirements

   • Scan task for keywords:
     • /(think|analyze|reason|debug|investigate|evaluate)/i → needsReasoning = true
     • /(architecture|entire|all files|codebase|analyze all)/i → needsLongContext = true
     • /(typo|test|refactor|update|fix)/i → preferCostOptimized = true

4. Select profile

   • For each profile in availableProfiles: classify by name pattern (see Profile Characteristic Inference table)
   • If needsReasoning: filter profiles where reasoning=true → prefer kimi
   • Else if needsLongContext: filter profiles where context=long → prefer kimi
   • Else: filter profiles where cost=low → prefer glm
   • selectedProfile = filteredProfiles[0]
   • If filteredProfiles.length === 0: fallback to glm if exists, else first available
   • If no profiles: Error

5. Enhance prompt

   • If task mentions files: gather context using Read tool
   • Add: file paths, current implementation, expected behavior, success criteria
   • Preserve slash commands at task start (e.g., /cook, /commit)

6. Execute delegation

   • Run: ccs {selectedProfile} -p "$enhancedPrompt" via Bash tool

7. Report results

   • Log: "Selected {profile} (reason: {reasoning/long-context/cost-optimized})"
   • Report: Cost (USD), Duration (sec), Session ID, Exit code

For /ccs:continue [follow-up]:

1. Detect profile

   • Read ~/.ccs/delegation-sessions.json using Read tool
   • Find most recent session (latest timestamp)
   • Extract profile name from session data
   • If no sessions → Error: "No previous delegation. Use /ccs first"

2. Parse override flag

   • Scan follow-up for pattern: --(\w+)
   • If match: profile = match[1], remove flag from follow-up, log profile switch
   • If no match: use detected profile from step 1

3. Enhance prompt

   • Review previous work (check what was accomplished)
   • Add: previous context, incomplete tasks, validation criteria
   • Preserve slash commands at start

4. Execute continuation

   • Run: ccs {profile}:continue -p "$enhancedPrompt" via Bash tool

5. Report results

   • Report: Profile, Session #, Incremental cost, Total cost, Duration, Exit code

Decision Framework

Delegate when:

• Simple refactoring, tests, typos, documentation
• Deterministic, well-defined scope
• No discussion/decisions needed

Keep in main when:

• Architecture/design decisions
• Security-critical code
• Complex debugging requiring investigation
• Performance optimization
• Breaking changes/migrations

Profile Selection Logic

Task Analysis Keywords (scan task string with regex):

Profile Characteristic Inference (classify by name pattern):

Selection Algorithm (apply filters sequentially):

profiles = Object.keys(config.profiles)
classified = profiles.map(p => ({name: p, ...inferCharacteristics(p)}))

if (needsReasoning):
  filtered = classified.filter(p => p.reasoning === true).sort(['kimi'])
else if (needsLongContext):
  filtered = classified.filter(p => p.context === 'long').sort(['kimi'])
else:
  filtered = classified.filter(p => p.cost === 'low').sort(['glm', ...])

selected = filtered[0] || profiles.find(p => p === 'glm') || profiles[0]
if (!selected): throw Error("No profiles configured")

log("Selected {selected} (reason: {reasoning|long-context|cost-optimized})")

Override Logic:

• Parse task for /--(\w+)/. If match: profile = match[1], remove from task, skip selection

Example Delegation Tasks

Good candidates:

• "/ccs add unit tests for UserService using Jest" → Auto-selects: glm (simple task)
• "/ccs analyze entire architecture in src/" → Auto-selects: kimi (long-context)
• "/ccs think about the best database schema design" → Auto-selects: kimi (reasoning)
• "/ccs --glm refactor parseConfig to use destructuring" → Forces: glm (override)

Bad candidates (keep in main):

• "implement OAuth" (too complex, needs design)
• "improve performance" (requires profiling)
• "fix the bug" (needs investigation)

Execution

Commands:

• /ccs "task" - Intelligent delegation (auto-select profile)
• /ccs --{profile} "task" - Force specific profile
• /ccs:continue "follow-up" - Continue last session (auto-detect profile)
• /ccs:continue --{profile} "follow-up" - Continue with profile switch

Agent via Bash:

• Auto: ccs {auto-selected} -p "task"
• Continue: ccs {detected}:continue -p "follow-up"

References

Template: CLAUDE.md.template - Copy to user's CLAUDE.md for auto-delegation config Troubleshooting: references/troubleshooting.md

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