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 skills add granoflow/granoflow-mcp-server --skill granoflow-task-orchestratorgit clone --depth 1 https://github.com/granoflow/granoflow-mcp-serverWrote 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/skills/granoflow/granoflow-mcp-server/granoflow-task-orchestrator)<a href="https://agentmods.dev/skills/granoflow/granoflow-mcp-server/granoflow-task-orchestrator"><img src="https://agentmods.dev/badge/skills/granoflow/granoflow-mcp-server/granoflow-task-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/skills/granoflow/granoflow-mcp-server/granoflow-task-orchestrator"><img src="https://agentmods.dev/badge/skills/granoflow/granoflow-mcp-server/granoflow-task-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 | $0.00038 | $0.03385 |
| Opus 5 | $0.00019 | $0.01692 |
| Sonnet 5 | $0.00008 | $0.00677 |
| Haiku 4.5 | $0.00004 | $0.00338 |
Grade A, and why
granoflow-task-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 11d 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 — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Granoflow Task Orchestrator
Use this bundled MCP skill as the single upper-layer task entrypoint. It decides whether a request is an incidental capture, a context-rich task, an analysis or planning request, an end-to-end execution request, or an already-completed delivery audit, then delegates to existing Granoflow workflow owners without duplicating their contracts.
Keyword
#gf#gf-capture#gf-analyze#gf-plan#gf-run#gf-finish
When to use
- Use whenever the user asks to record, analyze, plan, execute, finish, or otherwise manage work in Granoflow.
- Use when a new task idea appears during another active task and the agent must preserve it without derailing current work.
- Use when natural-language context may already be sufficient to enter Analysis, Planning, or safe local execution.
- Use Chinese shortcuts gf记, gf析, gf规, gf做, gf完 or ASCII aliases gf+, gf?, gf>, gf!, gf. only as explicit route overrides; plain gf uses automatic routing.
Example requests
- 刚想到以后要优化首次同步，先记一下。
- gf析 把我们刚才讨论的导入兼容问题整理成可决策的分析。
- gf做 把刚才确认的两个任务建档、分析、计划、实现、验证并在 GF 完成。
- 请用无人值守模式根据 docs 下的产品文档和用户故事生成 granoflow 项目并完成和交付它。长跑维护 Project E2E SoT，并在宿主支持时按 SoT next_step 定时唤醒续跑。
Workflow
Before routing a project-bound request, call
granoflow_agent_preferences_get(projectId). Apply the resolved explanation and
execution defaults without asking repeated setup questions. Preferences may
select a normal path, but they never weaken Task Work, test, Delivery,
authorization, acceptance, Git-checkpoint, or external-action gates.
After every project-bound analyze / plan / run / finish stop (and whenever the
user asks for status or next steps), load
granoflow-agent-workflow/project-lifecycle-progress-board and end the turn
with a rendered progress board + recommended next action. Interactive mode keeps
phase confirmations; unattended mode shows the same board as display-only and
does not ask for board acknowledgement.
1. Classify lifecycle intent from context
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 126 B
- references/authoring-contract.md 1.7 KB
- references/end-to-end-orchestration.md 6.0 KB
- references/intent-and-maturity-routing.md 4.0 KB
- references/short-command-contract.md 4.6 KB
- references/task-depth-placement-and-dates.md 2.9 KB
- scripts/route_task_intent.py 9.1 KB runs code
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.
- 11d ago First seen · 262 lines · 38 tokens per session scan A c7788e47d65c
granoflow-task-orchestrator is a skill published in the GitHub repository granoflow/granoflow-mcp-server (0 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 3,385 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.
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