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 causify-ai/helpers --skill slides.reduce_textgit clone --depth 1 https://github.com/causify-ai/helpersWrote 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/causify-ai/helpers/slides.reduce_text)<a href="https://agentmods.dev/skills/causify-ai/helpers/slides.reduce_text"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/slides.reduce_text/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/causify-ai/helpers/slides.reduce_text"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/slides.reduce_text.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00010 | $0.00814 |
| Opus 5.5 | $0.00004 | $0.00326 |
| Sonnet 5.5 | $0.00002 | $0.00163 |
| Haiku 4.5 | $0.00001 | $0.00081 |
Grade A, and why
slides.reduce_text 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal
- Your goal is to reduce the text in the slide text without changing content or structure
Workflow
Role
- Your role is specified in
.claude/skills/role.ai_researcher.md
Follow Conventions
- Follow the conventions in
.claude/skills/slides.rules.md- E.g., if a line starts with an asterisk
*, it's the slide title and leave it unchanged
- E.g., if a line starts with an asterisk
Keep the Structure
- Maintain the sequence of slides, the comments, and the headers
- Maintain the structure of the text in terms of bullet and sub-bullet points
- Keep all the figures
- Leave bold lines untouched
- Never collapse a sub-point into a colon-joined clause on the parent bullet:
shorten the words in each bullet, don't merge bullets together
- Bad
- **Collections of data**: organized datasets for analysis - E.g., customer purchase histories in CRM - Good
- **Collections of data** - Organized datasets for analysis - E.g., customer purchase histories in CRM
- Bad
Reduce Text
- Reduce text keeping the structure of the bullets untouched
- Write directly to the audience using "you"
- Be concise: remove filler words (e.g., "the", "that", "very")
- Use active voice (e.g., "Improve accuracy," not "Accuracy can be improved")
- Prefer short phrases over full sentences
- Delete a whole bullet point outright when it is unclear or adds no
information beyond a nearby bullet, rather than trying to trim it
- E.g., for a slide in input like:
the output is:* Slide title - This is a very long bullet point that is not clear and should be removed - This is a clear bullet point that should be kept* Slide title - This is a clear bullet point that should be kept
- E.g., for a slide in input like:
Example
- Input
- **Collections of data** - Aggregated, organized data sets for analysis - E.g., customer purchase histories in a CRM system - **Descriptive statistics** - Summary metrics: mean, median, mode, standard deviation - E.g., average sales per quarter to understand trends - **Historical reports** - Examination of _past performance_ - E.g., monthly sales reports for past fiscal year - **Dashboards** - Visual displays of key metrics for insights - E.g., dashboard showing quarterly revenue, expenses - **Models** - Statistical representations to _forecast, explain phenomena_ - E.g., model to anticipate customer churn based on behavioral data
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 · +6 lines e981fd23afbe
- 1mo ago First seen · 105 lines · 10 tokens per session scan A 6e2ebad84377
slides.reduce_text is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 10 tokens to every session and 814 once invoked, about $0.0000 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 2026-09-03.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
miniapp
Build a tiny interactive HTML playground only when someone asks to see, play with, or step through a mechanism.
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.