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Get Started Free →Delegates tasks to Gemini or Qwen with quota tracking and error handling. Use when tasks exceed context window or need cheaper processing.
.claude/skills/athola-delegation-core/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 0% | 0% |
delegation-core:task-assessed)delegation-core:delegation-suitability)delegation-core:handoff-planned)delegation-core:results-integrated)A method for deciding when and how to delegate tasks to external LLM services. Core principle: delegate execution, retain high-level reasoning.
Delegate execution, retain reasoning. Claude handles architecture, strategy, design, and review. External LLMs perform data processing, pattern extraction, bulk operations, and summarization.
| Complexity | Context | Recommendation | |------------|---------|----------------| | High | Any | Keep local | | Low | Large | Delegate | | Low | Small | Either |
High Complexity: Architecture, design decisions, trade-offs, creative problem solving.
Low Complexity: Pattern counting, bulk extraction, boilerplate generation, summarization.
delegation-core:task-assessed)Classify the task:
modules/task-assessment.md for classification criteria.Exit Criteria: Task classified with complexity level, context size, and delegation recommendation.
delegation-core:delegation-suitability)Verify prerequisites:
modules/handoff-patterns.md for checklist.modules/cost-estimation.md.Exit Criteria: Service authenticated, quotas verified, cost justified.
delegation-core:handoff-planned)Create a delegation plan:
modules/handoff-patterns.md for request template.Exit Criteria: Delegation plan documented.
delegation-core:results-integrated)Execute and validate results:
Exit Criteria: Results validated and integrated, usage logged.
For MCP servers that don't support Dynamic Client Registration (e.g., Slack), pre-configured OAuth client credentials can be provided:
bashclaude mcp add <server-name> --client-id <id> --client-secret <secret>
This enables delegation workflows through MCP servers that require pre-configured OAuth, expanding the range of external services available for task delegation.
As an alternative to manual OAuth setup, users can configure MCP servers directly in claude.ai at claude.ai/settings/connectors. These connectors are automatically available in Claude Code when logged in with a claude.ai account: no claude mcp add or credential management required. This provides a browser-based auth flow that may be simpler for services with complex OAuth requirements.
When delegating tasks that modify files to subagents, use isolation: worktree in the agent frontmatter to run each agent in a temporary git worktree. This prevents file conflicts when multiple delegated agents operate in parallel on overlapping paths. The worktree is auto-cleaned if no changes are made; preserved with commits if the agent produces changes.
yaml# Agent frontmatter for isolated delegation isolation: worktree
Conjure uses leyline infrastructure:
| Leyline Skill | Used For | |---------------|----------| | quota-management | Track service quotas and thresholds. | | usage-logging | Session-aware audit trails. | | service-registry | Unified service configuration. | | error-patterns | Consistent error handling. | | authentication-patterns | Auth verification. |
See modules/cost-estimation.md for leyline integration examples.
For detailed service workflows:
Skill(conjure:gemini-delegation): Gemini CLI specifics.Skill(conjure:qwen-delegation): Qwen MCP specifics.Skill(conjure:minimax-delegation): MiniMax CLI specifics.When delegating to multiple agents, choose the appropriate execution mode:
| Mode | When to Use | How It Works | |------|-------------|--------------| | single-session | Sequential tasks, same-file edits | Claude works through tasks in order | | subagents | Parallel independent tasks | Agents work independently, report back | | agent-team | Parallel coordinated tasks | Agents can communicate with each other |
See references/execution-modes.md for the selection decision matrix, mode compatibility notes, and anti-patterns to avoid.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | 21,257 | 18,355 | -14% | 1 | 1 | 0% | 3,063 | 4,122 | +35% | 0 | 0 | — |
case-01 | fail→pass | 16,553 | 12,090 | -27% | 1 | 1 | 0% | 2,511 | 3,652 | +45% | 0 | 0 | — |
case-02 | fail→pass | 20,772 | 13,956 | -33% | 1 | 1 | 0% | 3,170 | 3,852 | +22% | 0 | 0 | — |
case-04 | pass→pass | 19,672 | 16,055 | -18% | 1 | 1 | 0% | 2,928 | 3,946 | +35% | 0 | 0 | — |
case-05 | pass→pass | 21,612 | 12,326 | -43% | 1 | 1 | 0% | 3,294 | 3,377 | +3% | 0 | 0 | — |
case-06 | fail→fail | 11,578 | 9,781 | -16% | 1 | 1 | 0% | 1,857 | 3,069 | +65% | 0 | 0 | — |
case-07 | fail→pass | 11,139 | 3,628 | -67% | 1 | 1 | 0% | 1,632 | 2,081 | +28% | 0 | 0 | — |
case-08 | fail→pass | 10,957 | 3,238 | -70% | 1 | 1 | 0% | 1,745 | 2,057 | +18% | 0 | 0 | — |
case-09 | pass→pass | 11,052 | 3,258 | -71% | 1 | 1 | 0% | 1,652 | 2,038 | +23% | 0 | 0 | — |
case-10 | fail→pass | 11,342 | 1,969 | -83% | 1 | 1 | 0% | 1,875 | 1,883 | +0% | 0 | 0 | — |
case-11 | fail→pass | 11,247 | 3,387 | -70% | 1 | 1 | 0% | 1,695 | 2,153 | +27% | 0 | 0 | — |
case-12 | pass→pass | 5,828 | 1,886 | -68% | 1 | 1 | 0% | 861 | 1,855 | +115% | 0 | 0 | — |
case-13 | pass→pass | 10,508 | 3,387 | -68% | 1 | 1 | 0% | 1,677 | 2,071 | +23% | 0 | 0 | — |
case-14 | pass→pass | 8,896 | 1,889 | -79% | 1 | 1 | 0% | 1,325 | 1,855 | +40% | 0 | 0 | — |
case-15 | fail→pass | 8,963 | 1,911 | -79% | 1 | 1 | 0% | 1,293 | 1,887 | +46% | 0 | 0 | — |
case-16 | fail→pass | 12,924 | 2,096 | -84% | 1 | 1 | 0% | 2,015 | 1,885 | -6% | 0 | 0 | — |
case-17 | fail→pass | 10,463 | 1,442 | -86% | 1 | 1 | 0% | 1,577 | 1,751 | +11% | 0 | 0 | — |
case-18 | fail→pass | 10,659 | 1,958 | -82% | 1 | 1 | 0% | 1,697 | 1,916 | +13% | 0 | 0 | — |
case-19 | fail→pass | 9,758 | 1,992 | -80% | 1 | 1 | 0% | 1,570 | 1,900 | +21% | 0 | 0 | — |
case-20 | fail→pass | 9,277 | 2,349 | -75% | 1 | 1 | 0% | 1,432 | 1,904 | +33% | 0 | 0 | — |
case-21 | fail→pass | 16,595 | 2,736 | -84% | 1 | 1 | 0% | 2,611 | 2,021 | -23% | 0 | 0 | — |
case-22 | fail→pass | 9,009 | 2,543 | -72% | 1 | 1 | 0% | 1,405 | 1,988 | +41% | 0 | 0 | — |
case-23 | pass→pass | 9,069 | 1,910 | -79% | 1 | 1 | 0% | 1,445 | 1,865 | +29% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +61 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.