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/thewolffish/wolffish-app/workflownpx skills add thewolffish/wolffish-app --skill workflowgit clone --depth 1 https://github.com/thewolffish/wolffish-appWrote 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/thewolffish/wolffish-app/workflow)<a href="https://agentmods.dev/skills/thewolffish/wolffish-app/workflow"><img src="https://agentmods.dev/badge/skills/thewolffish/wolffish-app/workflow.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.01519 |
| Opus 5 | $0.00024 | $0.00759 |
| Sonnet 5 | $0.00010 | $0.00304 |
| Haiku 4.5 | $0.00005 | $0.00152 |
Grade A, and why
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 5d 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.
What it actually says
Workflow — design and drive a model-led run
These tools exist so YOU can architect the execution of a task: break it into phases, fan independent slices out to parallel agents on models you choose, collect each result the moment it lands, pit agents against each other for adversarial verification, and synthesize one answer in your own voice.
The workflow is never fixed. You design it per task — a quick question needs no agents at all; a hard deliverable might get analysis agents, builder agents, skeptic agents, and a verify pass. The user sees a live workflow card built from the harness's own telemetry (statuses, tokens, time, tool calls) — your job is the work, not the reporting.
The loop
workflow_planthe phases when the task is big enough to phase — it commits you to spawning agents into them; solo work takes no plan and no card.agent_spawnthe independent slices — several at once for parallelism, each with a complete, self-contained task and a model matched to its difficulty.agents_await— it returns on the FIRST landing; read the report, decide, spawn/steer/cancel, await again. Never stall on the slowest agent.- Verify: spawn skeptics/checkers against produced work before trusting it.
- Synthesize the final answer yourself, in one voice.
The full playbook lives in your workflow-mode system prompt.
What ships with it
1 file 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.
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.
- 5d ago First seen · 98 lines · 48 tokens per session scan A 1a3448fbaf43
workflow is a skill published in the GitHub repository thewolffish/wolffish-app (5 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 1,519 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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