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.
npx skills add dailyaiagents-cpu/dailyai-os --skill delegation-orchestratorgit clone --depth 1 https://github.com/dailyaiagents-cpu/dailyai-osWrote 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.
[](https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/delegation-orchestrator)<a href="https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/delegation-orchestrator"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/delegation-orchestrator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/delegation-orchestrator"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/delegation-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00088 | $0.01243 |
| Opus 5 | $0.00044 | $0.00622 |
| Sonnet 5 | $0.00018 | $0.00249 |
| Haiku 4.5 | $0.00009 | $0.00124 |
Grade A, and why
delegation-orchestrator 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
delegation-orchestrator
Why this exists
When the outreach-paced specialist hits "linkedin session expired," it shouldn't loop trying to fix it itself. It should write a delegation: "ops, please run session-keeper for linkedin." The ops agent runs the helper, opens the right gate, the user resolves it, outreach resumes. No specialist is stuck indefinitely; no Cooper-time wasted on routing.
This file is the convention + fault tree specialists read to know who to
delegate to. The actual dispatch happens in tools/delegation_tick.py
(daemon-tick, every 60s, permitted helper).
The convention
A specialist in a blocked state writes JSON to
data/delegations/pending/<unix-timestamp>-<source>.json:
{
"source_agent": "outreach-paced",
"next_action": "ops",
"task": "run session-keeper for linkedin target",
"context_diff": "session-keeper reports linkedin status=expired since 2026-05-02T...",
"priority": "high",
"max_attempts": 2
}
Fields:
source_agent— who hit the block (informational)next_action— the openclaw agent id who should help (e.g.,ops,accountant,research)task— single-message instruction; daemon-tick passes this verbatim toopenclaw agent --agent <next_action> -m "<task>"context_diff— optional, appended to task if present (keeps requests small)priority—low|medium|high(informational; daemon-tick doesn't reorder)max_attempts— int, default 2
The daemon-tick (tools/delegation_tick.py):
- Polls
data/delegations/pending/every 60s - For each request: attempts++ check vs max_attempts
- Calls
openclaw agent --agent <next_action> -m "<task>" --timeout 120 --json - On rc=0: moves to
data/delegations/done/ - On rc≠0 with attempts < max: writes request back, retries next tick
- On attempts ≥ max: moves to
data/delegations/failed/withfinal_status: max-attempts-exhausted
Fault tree (specialists read this to know who to delegate to)
| Source agent | Trigger | next_action | task |
|---|---|---|---|
| outreach-paced | linkedin session expired | ops | "run session-keeper for linkedin target" |
| hermes-inbox-router | reddit poll returns 401 | ops | "rotate reddit credentials; verify session-keeper for reddit" |
| research | paywall hit on <publisher> |
accountant | "do we have a subscription to <publisher>?" |
| sales | LinkedIn DM rate-limit | ops | "rotate session or switch surface for linkedin" |
| sales | Reddit account suspension | ops | "check reddit account state" |
| content | image-gen quota exceeded | ops | "check API key state for <provider>" |
| ops | ollama model unreachable | ops | "run model-warmth-keeper recovery" |
| accountant | Stripe webhook failed signature | ops | "check STRIPE_WEBHOOK_SECRET freshness" |
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.
- 12d ago First seen · 111 lines · 88 tokens per session scan A 042062c61d3c
delegation-orchestrator is a skill published in the GitHub repository dailyaiagents-cpu/dailyai-os (0 stars, last pushed 4mo ago), licensed MIT. It adds 88 tokens to every session and 1,243 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…