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 anneheartrecord/charles-skill --skill doc-memory-extractorgit clone --depth 1 https://github.com/anneheartrecord/charles-skillWrote 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/anneheartrecord/charles-skill/doc-memory-extractor)<a href="https://agentmods.dev/skills/anneheartrecord/charles-skill/doc-memory-extractor"><img src="https://agentmods.dev/badge/skills/anneheartrecord/charles-skill/doc-memory-extractor/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/anneheartrecord/charles-skill/doc-memory-extractor"><img src="https://agentmods.dev/badge/skills/anneheartrecord/charles-skill/doc-memory-extractor.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 | $0.00071 | $0.01094 |
| Opus 5.5 | $0.00028 | $0.00438 |
| Sonnet 5.5 | $0.00014 | $0.00219 |
| Haiku 4.5 | $0.00007 | $0.00109 |
Grade A, and why
extract-memory 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
extract-memory
Analyze a collection of documents and extract a personal writing style profile into a Claude Code memory file.
What This Skill Does
- Reads all documents from a given directory or file
- Analyzes writing patterns, style, tone, vocabulary, themes
- Generates a structured writing style profile
- Saves the profile to a memory file (default:
~/.claude/writing-style.md)
Supported Input Formats
.md— Markdown files.txt— Plain text files.pdf— PDF documents (read via the Read tool).lakebook— Yuque exports (auto-detected, will run/convert-lakebookfirst)
Workflow
Step 1: Identify Input
Determine what the user provided:
- Directory path: Read all supported files in the directory
- Single file path: Read that one file
.lakebookfile: First convert to Markdown usingpython3 ~/Desktop/lakebook-converter/convert.py, then process the output directory
Step 2: Sample Documents
For large collections (20+ files):
- Read all files but sample strategically — pick diverse documents by length and topic
- Aim to read at least 15-20 substantial documents (>500 chars each)
- Prioritize longer, more expressive documents over short notes
For small collections (<20 files):
- Read all files completely
Step 3: Analyze Writing Style
Examine the documents and extract the following dimensions:
Structure Patterns
- How do they open articles? (Hook style, question, statement, story)
- What's the typical article structure? (Sections, numbering, flow)
- How do they close? (Summary, call-to-action, question, open-ended)
Language Characteristics
- Vocabulary level and preferences
- Use of metaphors and analogies (what domains do they draw from?)
- Sentence length patterns (short and punchy? Long and flowing?)
- Use of rhetorical devices (repetition, contrast, parallelism)
- Multilingual patterns (code-switching, foreign terms)
Tone & Voice
- Formality level
- Emotional range (analytical, passionate, humorous, serious)
- Relationship with reader (teacher, peer, friend, mentor)
- Confidence level and how they handle uncertainty
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 · 159 lines · 71 tokens per session scan A b1e598d3c344
extract-memory is a skill published in the GitHub repository anneheartrecord/charles-skill (4 stars, last pushed 2mo ago), licensed MIT. It adds 71 tokens to every session and 1,094 once invoked, about $0.0003 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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