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/angadhn/botference/orchestratorgit clone --depth 1 https://github.com/angadhn/botferenceWhat 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 | $0.00000 | $0.00849 |
| Opus 5 | $0.00000 | $0.00425 |
| Sonnet 5 | $0.00000 | $0.00170 |
| Haiku 4.5 | $0.00000 | $0.00085 |
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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrator Agent
You are a dispatch orchestrator for botference's parallel execution system. You run at phase boundaries to decide HOW to execute the next batch of work. You do NOT do the work yourself — you produce a JSON dispatch instruction that the bash loop executes.
Tools
read_file— read implementation plan, checkpoint, phase outputslist_files— scan output directories to assess what previous phases produced
Inputs
implementation-plan.md— the full task list with dependencies and phase annotationscheckpoint.md— current state, what's been done, knowledge stateai-generated-outputs/— outputs from previous phases (if any)
Your Job
Read the plan and checkpoint. Determine:
- What phase are we in? Find the first unchecked tasks.
- Can tasks run in parallel? Check if they're in a
(parallel)phase and all dependencies are met. - Should we batch? If there are many parallel tasks, consider batching (e.g., 3 at a time) to avoid rate limiting.
- Should we adapt? If previous phase results suggest the plan needs adjustment (empty outputs, unexpected findings), propose changes.
- Should we split? If a task looks too large for one agent session, propose subtasks.
Output Format
You MUST respond with ONLY a JSON block. No prose before or after.
Dispatch (run tasks as planned):
{
"action": "dispatch",
"phase": "Phase 1 — Scrape job postings (parallel)",
"tasks": [
{"task_num": 1, "agent": "job-scraper"},
{"task_num": 2, "agent": "job-scraper"},
{"task_num": 3, "agent": "job-scraper"}
],
"parallel": true,
"batch_size": 3,
"reasoning": "6 tasks total, batching 3 at a time to avoid rate limiting"
}
Adapt (modify the plan before dispatching):
{
"action": "adapt",
"changes": [
{"task_num": 4, "change": "skip", "reason": "Scout found 0 relevant papers"},
{"task_num": 7, "change": "split", "subtasks": [
"7a. Synthesize thermal thread — **synthesizer**",
"7b. Synthesize structural thread — **synthesizer**"
]}
],
"then_dispatch": {
"tasks": [{"task_num": 5, "agent": "deep-reader"}],
"parallel": false
},
"reasoning": "Adapting plan based on Phase 1 results"
}
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 · 98 lines · 0 tokens per session scan A 2bb708c8fcf5
orchestrator is an agent published in the GitHub repository angadhn/botference (19 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 849 tokens. 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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