Delegation strategy
Skill forjd/mythos-delegation-skill/skills/delegation-strategy
Decision guide for choosing between inline work, one subagent, parallel subagents, and workflow orchestration. Use when planning a task that could involve searching many files, independent parallel strands, large-scale fan-out, specialized agents, or when tempted to spawn agents, so the agent picks the cheapest level that fits.From its SKILL.md
npx -y skills add forjd/mythos-delegation-skill --skill delegation-strategyAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Delegation strategy
Pick the lowest rung on this ladder that fits the task. Every step up costs latency, tokens, and context-transfer overhead: a subagent starts with zero knowledge of the conversation.
The ladder only goes as high as your harness's tools. Before climbing, check what you actually have — subagent spawning? parallel execution? a workflow/orchestration tool? — and treat your highest supported rung as the ceiling. If a rung is missing, see "Degrading gracefully" below.
The ladder
- Do it yourself (default) — you know where to look, the work is linear, or it's small.
- One subagent — the search is wide but the answer is narrow, or a specialized agent type fits.
- Parallel subagents — multiple genuinely independent strands of work.
- Workflow orchestration — structured fan-out at scale, AND the user explicitly opted in.
When a subagent earns its cost
The core trade-off is context economy vs. directness. An agent burns its own context and returns only its final message.
- Delegate wide-search / narrow-answer work. "Which of these 40 files handle auth?" — let an Explore agent read the file dumps and hand back the conclusion. Your context stays clean for the real work.
- Never delegate single-fact lookups. If you already know the file, symbol, or value, a direct Grep/Read beats spawning an agent and writing it a prompt.
- Write the prompt as if to a stranger. The agent knows nothing you haven't told it: include file paths, constraints already discovered this session, and the shape of answer you want back (a list, a verdict, a path). Most delegation failures are under-specified prompts, not wrong rungs.
- Fan out independent work. Launch unrelated investigations as multiple agents in a single message so they run concurrently. Parallelism pays when the strands are slow or numerous, not merely independent — two quick lookups can stay sequential.
- Isolate parallel writers. Agents that edit files concurrently need disjoint file sets, or per-agent worktrees if your harness offers them. Logical independence isn't enough to stop them clobbering each other's changes.
- Match specialized agent types when your harness defines them (for example Explore, Plan, docs, security, or custom agent definitions). Their tools and prompts are scoped to the job; prefer them over general-purpose.
- Don't duplicate. Once you've delegated a search, don't also run it yourself. Wait for the result.
- Continue, don't respawn. If your harness can message an existing agent, follow up with it because it keeps its context instead of starting a fresh one.
When workflow orchestration is justified
This rung means a dedicated orchestration tool that runs many agents under deterministic, code-driven control flow. Two gates, both required:
- Explicit user opt-in — a hard rule, not a judgment call. Only run workflow orchestration if the user asked for multi-agent orchestration in their own words, used an opt-in keyword your harness defines, invoked a skill that calls for one, or named a saved workflow. A task that would merely benefit from a workflow does not count. Without opt-in: describe what a workflow could do and its rough cost, and let the user choose.
- The task's shape needs deterministic orchestration — control flow that should be code, not model judgment: fan-out over a known work-list (migrations, audits), independent finders + adversarial verification of every finding, loop-until-dry discovery, judge panels over competing designs. Scout inline first to discover the work-list, then orchestrate over it.
If the work is a single investigation or a linear edit, a plain subagent (or rung 1) is correct even when workflows are available.
Degrading gracefully
When your harness lacks a rung, translate the principle, not the tool:
- No workflow tool: emulate rung 4 at rung 3 — decompose into batches of parallel subagents and iterate, with you as the orchestrator. The opt-in gate survives the translation: spawning dozens of agents is a scale decision the user must make explicitly, no matter which tool does the fan-out.
- No subagents at all: rung 1 is the whole ladder. The principle becomes context hygiene — read narrowly, summarize findings as you go instead of retaining raw file dumps, and drop intermediate material once distilled. When a task is genuinely a fan-out job your tooling can't express (a 200-file migration, an exhaustive audit), say so and propose splitting it into sessions or steps the user drives, rather than grinding through it badly.
Failure modes to avoid
- Delegating trivial lookups → pure latency for nothing.
- Doing giant multi-file sweeps inline → context pollution that degrades the rest of the session.
- Escalating to a workflow without opt-in → token surprise; the scale is the user's decision, never inferred.
What ships with it: 1 file
237 B alongside SKILL.md
agents/
- openai.yaml237 B
Gives 0 of the 12 instructions most agent orchestration skills give in ~1.1k tokens
Counted across 848 of the 1,300 authors here whose files we hold, read 2026-09-06
- Dispatch one agent per independent problem domainin 56 of 848, across 42 files
- Run full test suite after integrationin 55 of 848, across 42 files
- Verify fixes do not conflictin 40 of 848, across 32 files
- Review each summary when agents returnin 40 of 848, across 31 files
- Write a handoff document summarising the current conversationin 30 of 848, across 25 files
- Reference existing artifacts by path or URLin 26 of 848, across 24 files
- Give each agent a specific scopein 19 of 848, across 10 files
- Give each agent a clear goalin 19 of 848, across 10 files
- Include a suggested skills section in the documentin 18 of 848, across 16 files
- Tailor the doc to the user argumentsin 18 of 848, across 15 files
- Issue all subagent dispatches in the same responsein 17 of 848, across 11 files
- Use git worktrees for isolationin 17 of 848, across 8 files
Said here and by no other author read
- Pick the lowest ladder rung
- Delegate wide search narrow answer
- Write the prompt as to strangers
- Launch unrelated investigations in parallel
- Match specialized agent types
- Do not duplicate searches
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.