delegation

delegation is a skill for Claude Code from ai-ecoverse/slicc. It costs 101 tokens per session (5,770 once invoked), scanned A, original, Apache-2.0.

A guide for deciding when to delegate work to short-lived helper agents and how to coordinate them. It covers splitting independent tasks, running them in parallel, choosing models, and combining their results.

In plain words
What is it for?
Use it to divide research or implementation into independent assignments, start helper agents, wait for them, and synthesize their findings.
Why use it?
It helps avoid doing every task in one context and gives a consistent way to balance delegation against the overhead of starting another agent.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents.

Good fit Use it to divide research or implementation into independent assignments, start helper agents, wait for them, and synthesize their findings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ai-ecoverse/slicc/delegation
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.

Any agent
npx skills add ai-ecoverse/slicc --skill delegation
Clone the repo
git clone --depth 1 https://github.com/ai-ecoverse/slicc

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for delegation

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai-ecoverse/slicc/delegation.svg)](https://agentmods.dev/skills/ai-ecoverse/slicc/delegation)
Your own site
<a href="https://agentmods.dev/skills/ai-ecoverse/slicc/delegation"><img src="https://agentmods.dev/badge/skills/ai-ecoverse/slicc/delegation.svg" alt="Measured on agentmods" height="20"></a>
Per session 101 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,770 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 6 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Excessive Agency · line 108
    Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.
    Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
  • medium Prompt Injection · line 43
    Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.
    Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
  • medium Agent Snooping · line 111
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
  • medium Agent Snooping · line 111
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
  • medium Prompt Injection · line 119
    Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.
    Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
  • medium Agent Snooping · line 298
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
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 5.1 $0.00101 $0.05770
Opus 5 $0.00051 $0.02885
Sonnet 5 $0.00020 $0.01154
Haiku 4.5 $0.00010 $0.00577

Measured 2d ago against content hash 4b2695190888, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

delegation scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

agent "$TMPDIR" "curl,jq" "Fetch https://$url/api, return the top-level title field." >> "$TMPDIR/titles.txt" &
packages/vfs-root/workspace/skills/delegation/SKILL.md · 341 lines

How it starts

The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Delegation

Scoops do the heavy lifting; the cone orchestrates and synthesizes. This skill is about choosing how to delegate, not about administering scoops.

When to delegate

Default to delegation. Parallel scoops almost always finish faster, and the cone's job is synthesis.

Delegate when:

  • Multiple independent sources (scraping 3 sites = 3 scoops).
  • Time-consuming work that doesn't need direct oversight.
  • Work expressible as a clear, self-contained brief.

Do it yourself when:

  • Single quick lookup (one page, one API call).
  • Real-time adaptation needed (navigating broken URLs).
  • Overhead of spawning exceeds benefit.

Brief for authority, not for execution

The most common delegation failure is the cone doing too much pre-work before delegating. The cone researches the topic, makes the design decisions, picks the approach, and then hands the scoop a pre-cooked plan to type out. This is bad on three axes:

  • Pollutes the scoop's context. The brief is bloated with conclusions the scoop now has to re-derive an opinion on, instead of facts it can act on.
  • Strips the scoop of autonomy. A scoop that's been told what to think can't push back on a bad call or notice a better path mid-task. You get a typist, not a collaborator.
  • Wastes the cone's tokens. The cone's strength is orchestration — picking the right scoops, synthesizing their outputs. Doing the research itself burns the cone's context on work that's parallelizable.

Heuristic: if the cone reads files, runs commands, or makes decisions before delegating, that should have been part of the scoop's brief. Hand the scoop the question, the constraints, and the access — let it decide.

Bad (cone over-prepares) Good (scoop decides)
Cone reads 5 files, picks an approach, tells scoop "implement approach X in file Y." Scoop is told "the user wants Z; the relevant code is under /workspace/src/. Pick an approach and implement it."
Cone scrapes 3 docs, summarizes, then asks scoop to "write a comparison based on this summary." Scoop is told "compare libraries A, B, C for this use case. Their docs are at . Decide and write the comparison."
Cone debugs a failure, isolates the bug, then asks scoop to "fix the off-by-one in line 42." Scoop is told "this command fails with <output>. Find and fix the bug." (Even better: a one-shot agent for cheap, deterministic bug-fixes.)

Read the full file on GitHub · 341 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 · 341 lines · 101 tokens per session scan A 4b2695190888

Subscribe to this mod's changes

delegation is a skill published in the GitHub repository ai-ecoverse/slicc (30 stars, last pushed today), licensed Apache-2.0. It adds 101 tokens to every session and 5,770 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.