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 skills/tarekkharsa/agentstack/orchestrate-workflownpx skills add Tarekkharsa/agentstack --skill orchestrate-workflowgit clone --depth 1 https://github.com/Tarekkharsa/agentstackWhat 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 | $0.00080 | $0.01118 |
| Opus 5 | $0.00040 | $0.00559 |
| Sonnet 5 | $0.00016 | $0.00224 |
| Haiku 4.5 | $0.00008 | $0.00112 |
Grade A, and why
orchestrate-workflow 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate a governed multi-agent workflow
Use when you want the Bun-in-Rust shape — an implementer writes, independent adversarial reviewers attack the diff, a fixer applies feedback — with each agent's capabilities, model, and secrets managed by agentstack instead of hand-assembled per run.
The division of labor is fixed: an executor runs the loop (sandcastle, Claude Code workflows, your own script); agentstack defines and provisions the agents the loop spawns. Don't blur it in either direction.
1 — Define roles as profiles
A role is a profile: which skills, which servers, and (by convention) which
model. In .agentstack/agentstack.toml:
[profiles.implementer]
skills = ["porting-guide"] # the task's context artifacts
servers = ["github"]
[profiles.reviewer]
skills = ["adversarial-review"] # ships in this catalog
servers = [] # reviewers judge the diff; no tools needed
Keep reviewer profiles minimal on purpose — a reviewer with no servers can't be tool-poisoned, and the diff is all it should trust anyway.
2 — Bind models to roles
Pick per role, not per run: bulk/mechanical implementation → a cheap strong
coder; review → a different model family than the implementer when possible
(diverse failure modes). Record the binding wherever the executor configures
each agent (sandcastle's agent: option, a Workflow model: param, a
--model flag). If the route-by-cost skill is loaded, apply its ladder.
3 — Provision the sandbox (works today, no extra tooling)
The box needs three things: the harness CLIs, the rendered capabilities, and secrets that never touch disk.
# on the host — render the role's capabilities into the worktree the
# sandbox will mount (repeat per worktree):
cd <worktree> && agentstack use implementer --write
# start the container with secrets injected from the keychain at run time —
# no .env file, nothing baked into the image or committed:
docker run \
-e GH_PAT="$(agentstack secret get GH_PAT)" \
-e ANTHROPIC_API_KEY="$(agentstack secret get ANTHROPIC_API_KEY)" \
-v <worktree>:/work ...
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 · 108 lines · 80 tokens per session scan A 4dc405376b95
orchestrate-workflow is a skill published in the GitHub repository Tarekkharsa/agentstack (3 stars, last pushed 19d ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,118 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.
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