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 thapaliyabikendra/ai-artifacts --skill content-retrievalgit clone --depth 1 https://github.com/thapaliyabikendra/ai-artifactsWrote 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/thapaliyabikendra/ai-artifacts/content-retrieval)<a href="https://agentmods.dev/skills/thapaliyabikendra/ai-artifacts/content-retrieval"><img src="https://agentmods.dev/badge/skills/thapaliyabikendra/ai-artifacts/content-retrieval/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/thapaliyabikendra/ai-artifacts/content-retrieval"><img src="https://agentmods.dev/badge/skills/thapaliyabikendra/ai-artifacts/content-retrieval.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 | $0.00028 | $0.02293 |
| Opus 5 | $0.00014 | $0.01146 |
| Sonnet 5 | $0.00006 | $0.00459 |
| Haiku 4.5 | $0.00003 | $0.00229 |
Grade A, and why
content-retrieval 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 6d 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 — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Retrieval Protocol
Token-efficient retrieval system using markdown-native section detection.
Summary
Core Principle: Never read a full file when partial content suffices.
Choose retrieval depth by need:
- Exists? → Grep (files_with_matches)
- Count? → Grep (count)
- Lookup? → Grep (-C:2)
- Overview? → Read (limit:40)
- Section? → Grep heading → Read (offset, limit)
- Full? → Read (justify first)
Quick Reference
Depth Levels
| Level | Need | Method | Lines |
|---|---|---|---|
| L0 | Exists? | Grep(output: files_with_matches) |
~1 |
| L1 | Count? | Grep(output: count) |
~1 |
| L2 | Lookup value | Grep(pattern, -C:2) |
~5 |
| L3 | Overview | Read(limit: 40) |
~40 |
| L4 | Section | Grep heading → Read(offset, limit) | ~50 |
| L5 | Full | Read() - justify first |
All |
Decision Tree
What do I need?
├─ Does X exist? ──────────► L0: Grep files_with_matches
├─ How many X? ────────────► L1: Grep count
├─ What is X's value? ─────► L2: Grep with context
├─ What does X do? ────────► L3: Read limit:40
├─ How to use X for Y? ────► L4: Section extraction
└─ Implement X fully? ─────► L5: Full read (target only)
Tool Selection
| Scenario | Tool | Parameters |
|---|---|---|
| Check skill exists | Grep | output: files_with_matches |
| Count matches | Grep | output: count |
| Get specific row | Grep | pattern, -C: 0-2 |
| Read frontmatter | Read | limit: 25 |
| Read frontmatter+summary | Read | limit: 40 |
| Extract section | Read | offset: N, limit: 50 |
| Full understanding | Read | (no limit) |
Section Extraction
How Sections Work (Markdown-Native)
Sections are defined by headings and horizontal rules:
## Section A
Content...
--- ← Section A ends here (horizontal rule)
## Section B ← Or section ends at next same-level heading
Content...
No custom markers needed. Standard markdown structure.
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.
- 6d ago First seen · 357 lines · 28 tokens per session scan A 862d2c5815b2
content-retrieval is a skill published in the GitHub repository thapaliyabikendra/ai-artifacts (24 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 2,293 once invoked, about $0.0001 per session on Opus 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
install
This repository has colgrep installed - a semantic code search CLI.
context-budget
Audita o peso de contexto carregado na sessão — CLAUDE.md, agents, MCP descriptions, rules ativas, skills invocadas e histórico acumulado. Estima tokens por componente, reporta headroom disponível e emite alertas de overflow. Distinto do cost-tracker (skill 30) que rastreia tokens gastos em completions runtime.…
Workspace Search
Keeps local retrieval, file search, and nearby context gathering available in-session.
colgrep
This repository has colgrep installed - a semantic code search CLI.
frg-search
Fast indexed code search using frg (5x faster than ripgrep).
context-budget
Use when you need to check the current session's context usage and get recommendations for compaction or continuation.