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 sjarmak/coding-agent-workflows --skill distillgit clone --depth 1 https://github.com/sjarmak/coding-agent-workflowsWrote 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/sjarmak/coding-agent-workflows/distill)<a href="https://agentmods.dev/skills/sjarmak/coding-agent-workflows/distill"><img src="https://agentmods.dev/badge/skills/sjarmak/coding-agent-workflows/distill/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/sjarmak/coding-agent-workflows/distill"><img src="https://agentmods.dev/badge/skills/sjarmak/coding-agent-workflows/distill.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 | $0.00032 | $0.01322 |
| Opus 5.5 | $0.00013 | $0.00529 |
| Sonnet 5.5 | $0.00006 | $0.00264 |
| Haiku 4.5 | $0.00003 | $0.00132 |
Grade A, and why
distill 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Distill: Essence Extraction via Progressive Compression
Run a large artifact through a chain of compression steps, each compressing the previous output by ~50% while preserving the most important information. The key insight: the DROPS at each layer, what each step chose to cut, reveal the priority hierarchy. The waste product IS the signal.
Arguments
[path/to/artifact.md | inline text]
- A path (contains
/or a common extension): read the file. - Inline text: treat the whole argument as the artifact.
- No argument: ask the user what artifact to distill.
Phase 1: Ingest the Artifact
- Read the file or parse inline text
- Measure its size (word count, section count)
- If it is very short (< 500 words), tell the user it is already concise enough, distill works best on substantial artifacts. Offer to proceed anyway if they insist.
- Present a summary of what will be compressed:
- Source (file path or "inline text")
- Word count
- Number of sections/headers
- A 2-3 sentence description of the artifact's apparent purpose
- Confirm with the user before proceeding. Adjust if the user gives feedback.
Phase 2: Run Compression Chain
Run 4 sequential compression agents. Each one:
- Receives the previous agent's output (or the original artifact for agent 1)
- Must compress it to roughly 50% of its length
- Must explicitly list what it DROPPED and why
- Must preserve the most important information in its judgment
Agent prompt template for each stage:
You are a compression agent. Your job is to compress the following text to roughly 50% of its current length while preserving the most important information.
## Input ({word_count} words)
{previous_output}
## Instructions
1. Read the input carefully
2. Identify what is MOST important (load-bearing claims, decisions, data, actionable items)
3. Identify what is LEAST important (context that can be inferred, repetition, hedging, examples that illustrate already-clear points)
4. Produce a compressed version at roughly {target_word_count} words
5. List EXPLICITLY what you dropped and why
## Output Format
### Compressed ({target_word_count} words target)
[Your compressed version]
### Dropped
| What was cut | Why | Importance (1-5) |
|-------------|-----|-----------------|
| [specific content] | [reason] | [how important was it really] |
### Compression Decisions
- Hardest cut: [what was most painful to remove and why]
- Easiest cut: [what was clearly noise]
- What I'd restore first if given 25% more space: [...]
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 · 134 lines · 32 tokens per session scan A b60d0dca99bd
distill is a skill published in the GitHub repository sjarmak/coding-agent-workflows (6 stars, last pushed 17d ago), licensed MIT. It adds 32 tokens to every session and 1,322 once invoked, about $0.0001 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 2026-08-31.
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