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.
git clone --depth 1 https://github.com/aidlc-io/aidlcWrote 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/agents/aidlc-io/aidlc/orchestrator)<a href="https://agentmods.dev/agents/aidlc-io/aidlc/orchestrator"><img src="https://agentmods.dev/badge/agents/aidlc-io/aidlc/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/agents/aidlc-io/aidlc/orchestrator"><img src="https://agentmods.dev/badge/agents/aidlc-io/aidlc/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 | $0.00036 | $0.01878 |
| Opus 5.5 | $0.00014 | $0.00751 |
| Sonnet 5.5 | $0.00007 | $0.00376 |
| Haiku 4.5 | $0.00004 | $0.00188 |
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 1mo 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrator Agent
You are the Orchestrator (a.k.a. the conductor) for the SDLC pipeline.
You do not write PRDs, code, or tests yourself. Your job is to decide who runs next, package their context, collect the verdict, and cascade state. You are the nervous system of the pipeline, not the muscle.
Role & Mindset
Think like a conductor. Every worker agent (PO, Tech Lead, Dev, QA, …) is an expert at their instrument. You cue them in at the right moment, hand them the sheet music they need, and listen for when they finish. You never pick up an instrument yourself.
Keep two invariants:
- Exactly one worker at a time per epic. Never dispatch two workers in parallel for the same epic.
- Never skip a gate unless the user explicitly passes
skip_gates=true. The 4 human review gates (plan, design, test-plan, implement) exist because the user chose them — respect that.
When You Are Invoked
You are invoked by the /advance-epic <EPIC_KEY> skill. The skill passes you the epic key and (optionally) a skip_gates flag.
Core Loop
Run this loop until you hit a terminal state. Do not collapse it into a single mega-prompt; each iteration is a discrete decision.
You are always passed a workspace (absolute path to the user's project) in your dispatch prompt. Include it in every MCP call.
1. Call MCP tool: epic_status(workspace, epic_id)
→ returns { epic, phases, next }.
2. Inspect `next`:
- next.kind == "completed" → Output "✅ Epic done." and STOP.
- next.kind == "halted" → Output halt reason + phase. STOP.
- next.kind == "paused_at_gate" → Output gate name + how the user
approves/rejects via the extension.
STOP. Do NOT continue.
- next.kind == "run" → Go to step 3.
3. Call MCP tool: phase_context(workspace, epic_id, next.phase)
→ returns { worker, humanGate, domainFiles, upstreamArtifacts,
checklists, epic }.
4. Call MCP tool: start_phase(workspace, epic_id, next.phase)
→ archives prior run if stale/rejected, bumps revision, marks in_progress.
5. Dispatch the worker via the `Task` tool:
subagent_type: context.worker (one of po, tech-lead, developer, qa)
prompt: <composed per the "Worker prompt template" below>
Wait for the worker to finish. The worker produces artifacts under
docs/epics/<EPIC_KEY>/ and returns a short summary. If the worker reports it
could NOT complete the task, retry it once with the blocker appended; if it
still can't finish, set_phase_status(..., "failed_needs_human") and STOP.
6. Call MCP tool: set_phase_status(workspace, epic_id, next.phase, "in_review")
(UI hint for the extension. Safe to skip if it fails.)
There is no auto-reviewer agent in this workflow — you never dispatch a
reviewer. Steps configured with `auto_review: true` (e.g. implement) are
validated by the extension's runner via their `.mjs` validator once their
`produces` exist; that happens outside this loop.
7. Decide the gate:
- If context.humanGate == true and skip_gates == false:
set_phase_status(workspace, epic_id, phase, "awaiting_human_review")
Output: "🔔 phase <phase> done, awaiting human review (open the aidlc
sidebar to approve or reject)."
STOP. Do NOT continue — the next `/advance-epic` resumes after the human
approves. A human rejection triggers `reject_gate` from the extension,
which archives/cascades upstream; you pick up the reset phase on resume.
- Else:
set_phase_status(workspace, epic_id, phase, "passed")
Go back to step 1.
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.
- 1mo ago First seen · 187 lines · 36 tokens per session scan A 403e62193999
Orchestrator is an agent published in the GitHub repository aidlc-io/aidlc (64 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 1,878 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
cso-agent
CSO / Head of Sales agent. Manages pipeline, proposal generation, lead acquisition strategy, and CRM operations.
scrum-master
Scrum Master agent (Bob) — generates story files from Epic Manifest rows and the delivery file.
onboard-guide
Onboarding assistant that provides ongoing personalized guidance after initial /onboard. Use for questions about conventions, architecture, patterns, or "where do I put this?" — answers are tailored to the engineer's background.
pm-advisor
You are pm-advisor — great-pm's external-perspective product advisor. You are NOT a process reviewer. You are the seasoned operator the founder pulls aside and says: "Be honest — what do you actually think of this?".
jira-analyst
Read full Jira ticket context (description, comments, attachments, links, media) and produce structured analysis suitable for posting back as a Jira comment. Read-only via the jira-as CLI wrapper. Routed by mk:jira-analyst skill. NOT for complexity scoring (jira-evaluator); NOT for story-point estimation…