delegate

A coordinator for splitting a larger coding plan into dependent tasks and assigning them to separate worker agents. It runs independent tasks in parallel, then checks and reports the results.

In plain words
What is it for?
Use it to execute an existing plan, delegate coding work, or run several independent tasks in parallel while respecting their dependencies.
Why use it?
It removes the need to manually break up work and manage which tasks can run at the same time. It also keeps one failed task from stopping unrelated tasks.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/mifunedev/openharness/delegate
Any agent
npx skills add mifunedev/openharness --skill delegate
Clone the repo
git clone --depth 1 https://github.com/mifunedev/openharness

Made for: Claude Code, Codex.

Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,993 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00067 $0.02993
Opus 5 $0.00034 $0.01496
Sonnet 5 $0.00013 $0.00599
Haiku 4.5 $0.00007 $0.00299

Measured 2d ago against content hash a6c952464278, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

delegate 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 2d 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.

.oh/skills/delegate/SKILL.md · 266 lines

How it starts

The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Delegate

Parallel execution coordinator. Read a plan or conversation context, decompose it into a dependency-ordered task graph, and spawn worker sub-agents in parallel waves. Each wave completes before the next begins. Results are collected, validated, and reported.

Core principle: maximize parallelism while respecting dependencies absolutely.

Worker model and thinking policy

Apply this policy to every worker:

  1. Inherit the parent/session model by default. Omit the Agent tool's model argument. Do not route routine or simple work to a weaker model tier.
  2. Set the Agent tool's thinking parameter from task complexity: simple/mechanical → low, standard → medium, complex → high, and architecture or debugging with substantial uncertainty → xhigh. Supported levels are off, minimal, low, medium, high, and xhigh; never use max.
  3. If the selected thinking level is unsupported by the inherited model/provider, use the nearest supported level. Do not switch models merely to obtain a thinking level.
  4. Override model only with an explicit task-specific reason: an operator request, an unavailable required capability/context, a strict latency or budget constraint, or local benchmark evidence. Record that reason in the task graph and pass the override only for that worker.

Decision Flow

flowchart TD
    A["Resolve input: $ARGUMENTS or conversation context"] --> B{Plan found?}
    B -->|No| FAIL["Report: no plan found"]
    FAIL --> MEM_FAIL[Memory Protocol]

    B -->|Yes| C["Step 2: Deep-think task decomposition"]
    C --> D["Step 3: Build dependency graph"]
    D --> E["Step 4: Create tasks + compute waves"]
    E --> F{--dry-run?}
    F -->|Yes| DRY["Report: task graph + wave plan"]
    DRY --> MEM_DRY[Memory Protocol]

    F -->|No| G["Step 5: Execute Wave N"]
    G --> G1["Worker A"]
    G --> G2["Worker B"]
    G --> G3["Worker C"]
    G1 & G2 & G3 --> H{All passed?}
    H -->|No| I["Mark dependents BLOCKED, continue independent"]
    I --> J{More waves?}
    H -->|Yes| J
    J -->|Yes| G
    J -->|No| K["Step 6: Validate"]
    K --> L["Step 7: Report"]
    L --> MEM_OP[Memory Protocol]

Read the full file on GitHub · 266 lines

Changes

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.

  1. 2d ago First seen · 266 lines · 67 tokens per session scan A a6c952464278

Subscribe to this mod's changes

delegate is a skill published in the GitHub repository mifunedev/openharness (36 stars, last pushed 2d ago), licensed Apache-2.0. It adds 67 tokens to every session and 2,993 once invoked, about $0.0003 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 2026-08-30.

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