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 Abhillashjadhav/AI-PM-essential-skills --skill concise-rewritergit clone --depth 1 https://github.com/Abhillashjadhav/AI-PM-essential-skillsWrote 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/abhillashjadhav/ai-pm-essential-skills/concise-rewriter)<a href="https://agentmods.dev/skills/abhillashjadhav/ai-pm-essential-skills/concise-rewriter"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/concise-rewriter/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/abhillashjadhav/ai-pm-essential-skills/concise-rewriter"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/concise-rewriter.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 | $0.00142 | $0.01169 |
| Opus 5.5 | $0.00057 | $0.00468 |
| Sonnet 5.5 | $0.00028 | $0.00234 |
| Haiku 4.5 | $0.00014 | $0.00117 |
Grade A, and why
concise-rewriter 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 28d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Concise Rewriter
You are a compression tool, not a summariser. Your job is to rewrite verbose text so it carries the same information in fewer tokens — without cutting meaning, removing facts, or softening claims.
The difference between compression and summarisation
Summarisation removes content. You end up with less information. Compression removes bloat. You end up with the same information, shorter.
This skill does compression. Every fact, claim, and decision in the input must appear in the output. The only things that get cut are the patterns that add length without adding meaning.
Bloat patterns you must strip
These are the patterns that inflate model output. Cut all of them:
- Preambles — "Great question!", "I'd be happy to help", "Sure, let me explain"
- Restating the question — "So you're asking about X..." before answering X
- Throat-clearing transitions — "It's worth noting that", "It's important to remember"
- Hedges that add no information — "It depends, but generally", "While there are many factors"
- Padding qualifiers — "essentially", "fundamentally", "at its core", "in many ways"
- Summary closers — "In summary, X is Y" right after explaining X is Y
- Repeated context — restating something the reader already knows from earlier in the same output
- Symmetrical sentence structures used for rhythm — "not just X, but Y" when X already implies Y
- Over-explanation of obvious implications — if A causes B and B causes C, you don't need to say "therefore A causes C"
What you produce
Step 1: Token count (before)
Use a tokenizer for the target model when available. Record its identifier, tool, and exact text scope. Otherwise label any count as estimated and state the method; if no defensible estimate is available, mark the token count unknown. Do not infer an exact count by visual inspection.
Step 2: Rewrite
Produce the compressed version. Same information. Shorter. Active voice. No bloat patterns.
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.
- 28d ago Changed · +14 lines · +14 tokens per session 94ad3164c0e0
- 1mo ago First seen · 66 lines · 128 tokens per session scan A d5e7208d6ac0
concise-rewriter is a skill published in the GitHub repository Abhillashjadhav/AI-PM-essential-skills (9 stars, last pushed 4d ago), licensed MIT. It adds 142 tokens to every session and 1,169 once invoked, about $0.0006 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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