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 nabeelhyatt/coworkpowers --skill workflow-compoundgit clone --depth 1 https://github.com/nabeelhyatt/coworkpowersWrote 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/nabeelhyatt/coworkpowers/workflow-compound)<a href="https://agentmods.dev/skills/nabeelhyatt/coworkpowers/workflow-compound"><img src="https://agentmods.dev/badge/skills/nabeelhyatt/coworkpowers/workflow-compound/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/nabeelhyatt/coworkpowers/workflow-compound"><img src="https://agentmods.dev/badge/skills/nabeelhyatt/coworkpowers/workflow-compound.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 | $0.00074 | $0.01899 |
| Opus 5 | $0.00037 | $0.00949 |
| Sonnet 5 | $0.00015 | $0.00380 |
| Haiku 4.5 | $0.00007 | $0.00190 |
Grade A, and why
workflow-compound 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 9d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compound: Knowledge Compounding Workflow
You are orchestrating the Compound phase of the Compound Knowledge Work loop. Your job is to extract learnings from completed work and feed them back into the system so the next task is easier than the last.
The magic is in the compounding. Skip this phase and you're just working, not building.
Philosophy
Each documented pattern, template, and preference makes the next similar task faster and better. First attempt at a board update takes 4 hours of planning and drafting. Document the approach, and the next one takes 1 hour. That's compounding.
Process
Phase 1: Parallel Analysis
Launch these research agents in parallel:
-
Pattern Extractor - Analyze the completed work and conversation history:
- What approach was taken?
- What worked well? What didn't?
- What would you do differently next time?
- What frameworks or structures proved effective?
-
Template Assessor - Evaluate if this work could serve as a model:
- Is this output reusable as a template?
- What parts are situation-specific vs. generalizable?
- How would you parameterize this for future use?
-
Preference Detector - Capture user preferences revealed:
- What formatting or style preferences emerged?
- What level of detail does the user prefer?
- What communication style resonates?
- What frameworks does the user gravitate toward?
-
Failure Analyzer (if things didn't go well):
- What went wrong and why?
- Was it execution, planning, or assumptions?
- What early warning signs were missed?
- What would prevent this next time?
Phase 2: Produce Discrete Insights
From the analysis, produce individual, self-contained insights. Each insight should stand alone - it may be read months later in a completely different context, or surfaced by a relevance filter alongside unrelated insights.
Every task should produce at least one insight. Most will produce 2-5. Each insight gets its own type:
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.
- 9d ago First seen · 186 lines · 74 tokens per session scan A 4d215fdbefbe
workflow-compound is a skill published in the GitHub repository nabeelhyatt/coworkpowers (114 stars, last pushed 6mo ago), licensed MIT. It adds 74 tokens to every session and 1,899 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 2026-08-30.
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