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 markdown.summarizegit 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/markdown.summarize)<a href="https://agentmods.dev/skills/causify-ai/helpers/markdown.summarize"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/markdown.summarize/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/markdown.summarize"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/markdown.summarize.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 | $0.00012 | $0.00915 |
| Opus 5.5 | $0.00005 | $0.00366 |
| Sonnet 5.5 | $0.00002 | $0.00183 |
| Haiku 4.5 | $0.00001 | $0.00092 |
Grade A, and why
markdown.summarize 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interface Specification
Inputs
| Parameter | Type | Required | Description |
|---|---|---|---|
<INPUT> |
file path or text | Yes | Markdown file or text chunk to summarize |
<NUM_WORDS> |
integer | No* | Target output word count |
<FRACTION> |
float (0-1) | No* | Alternative to <NUM_WORDS>: fraction of original size |
<MAX_HEADER_LEV> |
integer | No | Max header level to preserve (default: preserve all levels) |
<TAG> |
string | No | Tag for output filename (default: derived from input) |
- Exactly one of
<NUM_WORDS>or<FRACTION>must be specified.
Outputs
| Item | Format | Description |
|---|---|---|
| Console output | Text | Header structure preview + summary + statistics |
| Output file | Markdown | explanation.<tag>.md (overwrites if exists) |
| Word count | Integer | Original and target word counts printed to console |
Workflow
Parse Input
- Read file from
<INPUT>path - Extract headers using markdown syntax (# ## ### etc.)
Determine Header Strategy
Case A: Input has header structure
- Extract all headers from the document
- Print structure to console
Case B: Input is plain text (no headers)
- Skip header extraction
- Proceed directly to summarization
Apply Header Level Filter
If <MAX_HEADER_LEV> not specified:
- Preserve all header levels from original
- Prefix headers with chapter numbers (e.g.,
# 1. Title,## 1.1. Subtitle)
If <MAX_HEADER_LEV> specified:
- Keep only headers with level <=
<MAX_HEADER_LEV> - Summarize/collapse all deeper sections into bullet points
- Example:
<MAX_HEADER_LEV>= 1 results in only H1 headers kept, all H2+ become bullets
Calculate Target Length
- Count original word count as
<ORIG_NUM_WORDS> - If
<FRACTION>given:<TARGET_WORDS>=<ORIG_NUM_WORDS>x<FRACTION> - If
<NUM_WORDS>given:<TARGET_WORDS>=<NUM_WORDS>
Summarize Content
- Convert to nested bullet points
- Follow rules from:
.claude/skills/markdown.rules.md.claude/skills/text.rules.md
- Constraints:
- Convert all mathematical formulas to LaTeX format
- Wrap text at 80 columns
- Target
<TARGET_WORDS>word count (within 10% tolerance) - Preserve key concepts and important details
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 · +1 lines · -1 tokens per session 8959bce4ed05
- 1mo ago First seen · 117 lines · 13 tokens per session scan A 5eae1b0d20f7
markdown.summarize is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 12 tokens to every session and 915 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
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…