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 jpoutrin/product-forge --skill chunking-strategiesgit clone --depth 1 https://github.com/jpoutrin/product-forgeWrote 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/jpoutrin/product-forge/chunking-strategies)<a href="https://agentmods.dev/skills/jpoutrin/product-forge/chunking-strategies"><img src="https://agentmods.dev/badge/skills/jpoutrin/product-forge/chunking-strategies/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/jpoutrin/product-forge/chunking-strategies"><img src="https://agentmods.dev/badge/skills/jpoutrin/product-forge/chunking-strategies.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 | $0.00035 | $0.00650 |
| Opus 5.5 | $0.00014 | $0.00260 |
| Sonnet 5.5 | $0.00007 | $0.00130 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
chunking-strategies 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chunking Strategies Skill
This skill provides chunking strategies for RAG document processing.
Chunking Methods
1. Fixed-Size Chunking
def fixed_size_chunk(text: str, chunk_size: int = 500, overlap: int = 50):
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
chunks.append(text[start:end])
start = end - overlap
return chunks
2. Semantic Chunking
Split on natural boundaries (sentences, paragraphs).
def semantic_chunk(text: str, max_tokens: int = 500):
paragraphs = text.split("\n\n")
chunks = []
current_chunk = []
current_tokens = 0
for para in paragraphs:
para_tokens = count_tokens(para)
if current_tokens + para_tokens > max_tokens:
chunks.append("\n\n".join(current_chunk))
current_chunk = [para]
current_tokens = para_tokens
else:
current_chunk.append(para)
current_tokens += para_tokens
if current_chunk:
chunks.append("\n\n".join(current_chunk))
return chunks
3. Recursive Chunking
Hierarchical splitting on multiple separators.
SEPARATORS = ["\n\n", "\n", ". ", " "]
def recursive_chunk(text: str, max_size: int, separators: list[str]):
if len(text) <= max_size:
return [text]
sep = separators[0] if separators else ""
chunks = []
parts = text.split(sep)
for part in parts:
if len(part) <= max_size:
chunks.append(part)
elif len(separators) > 1:
chunks.extend(recursive_chunk(part, max_size, separators[1:]))
else:
chunks.append(part[:max_size])
return chunks
Chunking by Document Type
| Document Type | Recommended Strategy | Chunk Size |
|---|---|---|
| Technical docs | Semantic (headers) | 500-1000 tokens |
| Legal documents | Semantic (sections) | 1000-2000 tokens |
| Code | Function/class based | 200-500 tokens |
| Conversations | Message boundaries | 100-300 tokens |
| General text | Recursive | 300-500 tokens |
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 · 104 lines · 35 tokens per session scan A 59992b7886b6
chunking-strategies is a skill published in the GitHub repository jpoutrin/product-forge (16 stars, last pushed 7mo ago), licensed MIT. It adds 35 tokens to every session and 650 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 2026-09-03.
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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
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