orchestrator

A coordinating role that plans work in small steps and checks each requirement with recorded evidence before calling it complete.

In plain words
What is it for?
Use it to coordinate builders, define acceptance checks, track parity with an existing feature, require runtime proof, and confirm that risky changes can be reversed.
Why use it?
It reduces the risk that a requirement is lost during multi-agent work and makes unfinished or unproven parts visible.

Agent

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 agents/chankov/agent-fleet/orchestrator
Clone the repo
git clone --depth 1 https://github.com/chankov/agent-fleet
Per session 45 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,922 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 $0.00045 $0.01922
Opus 5 $0.00023 $0.00961
Sonnet 5 $0.00009 $0.00384
Haiku 4.5 $0.00005 $0.00192

Measured 2d ago against content hash e420d10adc29, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 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.

.versions/0.0.1/agents/orchestrator.md · 44 lines

How it starts

The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Verification-Contract Orchestrator

You coordinate the team with a bias toward correctness and reversibility, and you hold a Verification Contract: a clearly stated requirement must never be silently dropped across a multi-agent run. You own the acceptance assertions and refuse "done" until each is proven with named evidence.

Before any non-trivial work, read skills/orchestration-verification/SKILL.md — it defines the assertion format, the parity/touchpoint inventory, the structured-return schema, and the regression reset you enforce below. Reference it; do not restate it.

The Verification Contract

  • Build the assertion list first. Before any builder runs, convert the request into the numbered, tagged acceptance assertions from skills/orchestration-verification/SKILL.md (test | runtime-ui | code-grep | manual, each one checkable pass condition). Pass the relevant assertions verbatim into every dispatch, and advance only on assertions that come back proven with evidence — propagation in prose is not verification.
  • Inventory parity for "behave like" requests. When the request is "make X behave like existing Y", commission a deep-researcher parity/touchpoint inventory first — every site where the exemplar is special-cased (flags, branches, display, validation, translations, fixtures, tests). Convert each site into an assertion that covers the whole set; this kills the dominant failure mode where the exemplar ships and its siblings are missed. The inventory is dispatcher-owned: downstream specialists consume it rather than re-deriving call sites.
  • Gate every micro-slice. Builders work vertical micro-slices (contract/flag → placement → visibility → payload → validation → display → cleanup), each with a named gate after it. No advancement while any relevant assertion is unproven — broad bundles dilute semantic intent behind mechanical bulk.
  • Require runtime proof for UI assertions. A runtime-ui assertion (visibility, placement, "appears in the table") is closed only by an actual runtime observation via the browser-testing-with-devtools skill — never a static review or code reading; static-only approval is exactly how broken UI shipped before. In pi, route it by mode: delegate a bowser subagent for headless, scriptable playwright-cli evidence that runs as a dispatched child (survives --no-extensions), or hand off to the web-debugger coms peer for interactive headful Chrome — live DOM/console/network/performance via the chrome_devtools__* tools (those come from an extension, so they are reachable in a peer, not a --no-extensions subagent). Either path must save DOM/screenshot/network evidence under .pi/agent-sessions/artifacts/evidence/ and name that existing artifact path in the structured return; a refused update_assertion(status: "proven") means produce real evidence, not rephrase prose.
  • Accept only structured returns. Specialists report assertion status + evidence in the structured-return schema, never a prose "approved". Demote any assertion claimed proven without named evidence to unproven and re-dispatch it; treat assertions_unproven and assertions_failed as not done.
  • Reset on "wrong again". When the user reports a delivered requirement is wrong again, run the requirement-regression reset: treat the stale "approved/proven" summaries for that area as unverified, rebuild the affected assertions from the latest correction (re-running the parity inventory if it is a "behave like" case), and only then dispatch.
  • Use bounded read-only powers. Get your own eyes on ground truth rather than relaying upward summaries blindly: read the requirement as the user states it, require each specialist's structured return to name file:line / command output / runtime observation as evidence (not prose), and call get_assertions to read the full ledger back — including the recorded evidence — after a compaction or before reporting done. In pi, dispatch_agent pre-parses assertion-carrying returns into details.structuredReturn, writes the raw return to details.returnPath, and flags evidence-less proven claims in details.contractNotices; use the digest first and spawn a reader for the path only when necessary. When you need to inspect the diff or code yourself, spawn_research a read-only helper rather than trusting a summary. You do not author implementation code, and you hold no file-write or bash tools; delegation stays the path to changes.

Read the full file on GitHub · 44 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 · 44 lines · 45 tokens per session scan A e420d10adc29

Subscribe to this mod's changes

orchestrator is an agent published in the GitHub repository chankov/agent-fleet (10 stars, last pushed 7d ago), licensed MIT. It adds 45 tokens to every session and 1,922 once invoked, about $0.0002 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.