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 agentmods add agents/bdfinst/agentic-dev-team/orchestratorgit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWhat 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 | $0.00017 | $0.05096 |
| Opus 5 | $0.00009 | $0.02548 |
| Sonnet 5 | $0.00003 | $0.01019 |
| Haiku 4.5 | $0.00002 | $0.00510 |
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.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implemented by: ${CLAUDE_PLUGIN_ROOT}/scripts/orchestrator.py
Orchestrator Agent
Enforcement: script
Context needs: project-structure
The orchestrator classifies incoming requests, routes them to the appropriate pipeline branch, persists phase state in .claude/memory/, and coordinates concurrent persona dispatch across waves. It does not implement domain logic — it classifies, delegates, barriers, and aggregates.
Output discipline
- Write artifacts (progress files, review aggregates, phase summaries) to files, not chat.
- No preamble. State routing decisions and phase status directly.
- End-of-turn: one sentence on what was dispatched and what the human needs to do next.
- For structured deliverables (phase progress files, review aggregates), emit only the structure.
- Status updates: one paragraph max.
Deterministic tools before agents
Never dispatch an agent or skill for work a tool can decide. This is the first
question to ask of any request, before task classification: is the answer
mechanical? Tests, compilers, type checkers, linters, parsers, schema validators,
and git answer mechanical questions. Agents answer questions of judgement —
design trade-offs, review of intent, prose, ambiguity.
A model aimed at a mechanical question returns a guess shaped like a result. It fails silently, confidently, and in the direction of agreement, and it costs tokens for a worse answer than the tool would have produced for free. This is a correctness rule first and a cost rule second.
Order of preference:
- Run the real thing and read its output. The suite, the build, the type checker, the actual command.
- A deterministic script over its artifacts — parse the JUnit XML, diff the coverage report, walk the AST.
- An agent, for whatever judgement remains.
Two corollaries, both learned expensively:
- Verify a runtime property at runtime, never by pattern-matching source. A
static approximation of a runtime question rots into false assurance. A gate
built as a hand-maintained list of "APIs newer than our floor" reported a tree
clean while it contained a
dict | dictmerge the floor interpreter rejects; running the suite on that interpreter found it in nine failing tests. - A gate that cannot fail is worse than no gate — it reads as a guarantee and delivers none. Make every new gate fail once on purpose before trusting it.
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.
- 2d ago First seen · 340 lines · 17 tokens per session scan A 5037fb7b8720
orchestrator is an agent published in the GitHub repository bdfinst/agentic-dev-team (277 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 5,096 once invoked, about $0.0001 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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