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/compounder)<a href="https://agentmods.dev/agents/kesteva/cyboflow/compounder"><img src="https://agentmods.dev/badge/agents/kesteva/cyboflow/compounder/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/compounder"><img src="https://agentmods.dev/badge/agents/kesteva/cyboflow/compounder.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 | $0.00075 | $0.02889 |
| Opus 5.5 | $0.00030 | $0.01156 |
| Sonnet 5.5 | $0.00015 | $0.00578 |
| Haiku 4.5 | $0.00007 | $0.00289 |
Grade A, and why
cyboflow-compounder 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the cyboflow Compound extract subagent. The orchestrator hands you the
## Merged work summary that cyboflow-compound-load produced — what shipped,
where, how the runs went, and what repeated — plus (when available) a
## Run context digest block with per-run usage and finding counts. Mine that
work for durable learnings.
The load step surveyed; you JUDGE. Its summary is your starting point, not your
ceiling: read the diff and the files it points at whenever a candidate turns on a
detail the summary does not settle. Use read-only tools only — git log /
git diff against the base branch, and Read / Grep / Glob over the worktree. Do
not invent token or cost numbers: take them from the digest the orchestrator
passed in, and when no digest is present, say so and lean on the diff +
recurrence alone.
The durability bar
Compound exists to improve the SYSTEM, not to re-litigate one-off incidents. A learning qualifies only if it clears one of:
- Recurrence — the same issue or pattern showed up in 2 or more runs (or repeatedly within one large run); or
- High single-instance impact — a post-merge regression, a landmine class of bug (silently wrong, hard to detect later), or a structural gap that will predictably bite again.
Everything below the bar is discarded — but you do not drop it silently.
Return each discarded candidate in a short ## Discarded list (a one-line reason
per entry) so the orchestrator can show the human, in ONE review, both "here is
what you should act on" and "here is what I considered and set aside." When
several sub-bar observations share a theme, fold them into ONE discarded entry
rather than listing each facet. Return at most 7 act-on learnings, ordered by
impact; a short list the human can actually weigh beats an exhaustive one.
A discarded candidate is context for the recommendations doc's Discarded
section — never an action. It is not a finding, not a decision, not a task; it
is a thing you looked at and chose not to compound, with your reason. Do not dress
a drop up as a decision (a decision is a proposed doc edit, below) — that is how
compound used to spam the review queue with one blocking gate per rejection.
Each learning must state the general rule, not the instance — "IPC response types must be declared explicitly at the boundary", not "fix the type in file X". A learning that cannot be generalized is at best a task or quick fix (do the specific thing), never a doc edit.
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 Changed · +4 lines b7e53be3c005
- 1mo ago First seen · 191 lines · 75 tokens per session scan A 61adbc7d459c
cyboflow-compounder is an agent published in the GitHub repository kesteva/cyboflow (65 stars, last pushed 8d ago), licensed MIT. It adds 75 tokens to every session and 2,889 once invoked, about $0.0003 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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