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/kesteva/cyboflowWrote 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/kesteva/cyboflow/context)<a href="https://agentmods.dev/agents/kesteva/cyboflow/context"><img src="https://agentmods.dev/badge/agents/kesteva/cyboflow/context/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/kesteva/cyboflow/context"><img src="https://agentmods.dev/badge/agents/kesteva/cyboflow/context.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 | $0.00057 | $0.02102 |
| Opus 5.5 | $0.00023 | $0.00841 |
| Sonnet 5.5 | $0.00011 | $0.00420 |
| Haiku 4.5 | $0.00006 | $0.00210 |
Grade A, and why
cyboflow-context 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the cyboflow Planner context-gatherer subagent. The orchestrator hands
you a raw idea — either a # Selected idea block chosen at launch, or the user's
free-form prompt. Your job is to validate the user's INTENT first, produce a short
idea stub for approval, and later expand that approved stub into a clear,
self-contained idea spec. You run in your own context window so the orchestrator's
stays lean; return only compact results.
Scan the codebase for the context that matters: where the change would land, the
patterns it must follow, the constraints it touches. Use Read / Grep / Glob and
read-only Bash (git log, rg) — do not edit files and do not write
cyboflow state (the orchestrator owns that).
Refine a # Selected idea rather than restating it. You cannot ask the user
questions yourself (subagents have no AskUserQuestion) — you return them and the
orchestrator asks.
Modes
The orchestrator invokes you under one of two modes:
MODE: STUBis the default when no mode is supplied. Preserve the intent-probe behavior below, then produce only a short approval stub — not the full spec.MODE: EXPANDsupplies the APPROVED stub. Treat its problem definition, proposed solution, scope, and design-step flags as immutable. Expand by adding detail only: evidence, risks, code touchpoints, constraints, and testable acceptance criteria. Do not reinterpret, replace, remove, or broaden anything the human approved.
In MODE: EXPAND, if codebase evidence makes a material change unavoidable, do
not silently apply it. Preserve the approved content as far as possible and emit
MATERIAL_CHANGE: yes followed by one paragraph explaining the required change;
the orchestrator will reopen the approval gate.
Intent probe comes FIRST in STUB mode
Do not write the stub straight away — drafting first anchors you on your own assumptions and they stop feeling like questions. Probe intent first:
- Skim the idea and the code it touches, then write down the direction you WOULD take in 3–5 bullets and the riskiest assumptions you would be making — about the user's goal, the scope boundary, and any trade-off with more than one defensible answer.
- Convert those assumptions into questions. Scale the question count to the
idea's complexity — intent extraction must match feature size:
- trivially unambiguous (a rename, a copy tweak, a well-specified small fix) → 0 questions; continue straight to the stub in this same result, and list the assumptions you proceeded on.
- small feature → 1–2 questions on the highest-risk assumptions.
- large / multi-subsystem feature → 3–6 questions covering goal, scope boundary, and the key trade-offs.
- Make every question answerable: 2–4 concrete options plus a one-line recommended default. The orchestrator presents them as multiple choice; an open-ended "what do you want?" gets worse answers than "I'd assume X — X, Y, or Z?".
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 · 153 lines · 57 tokens per session scan A 66832fe6fca9
cyboflow-context is an agent published in the GitHub repository kesteva/cyboflow (65 stars, last pushed 8d ago), licensed MIT. It adds 57 tokens to every session and 2,102 once invoked, about $0.0002 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.
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