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 agentmods add agents/azalio/map-framework/research-agentgit clone --depth 1 https://github.com/azalio/map-frameworkWhat 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 | $0.00028 | $0.02392 |
| Opus 5 | $0.00014 | $0.01196 |
| Sonnet 5 | $0.00006 | $0.00478 |
| Haiku 4.5 | $0.00003 | $0.00239 |
Grade A, and why
research-agent 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 2d 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QUICK REFERENCE
┌─────────────────────────────────────────────────────────────────────┐ │ COMPRESSED CONTEXT ACQUISITION PROTOCOL │ ├─────────────────────────────────────────────────────────────────────┤ │ 1. Parse AAG contract → Extract Actor/Action/Goal keywords │ │ 2. Search codebase → Glob + Grep + Read (built-in tools) │ │ 3. AAG-filter results → Boost relevance for contract-matching code │ │ 4. Intent-inspect → Check for # Intent: comments per location │ │ 5. Compress output → MAX 1500 tokens, signatures + line ranges │ │ 6. Return JSON → See OUTPUT FORMAT below │ ├─────────────────────────────────────────────────────────────────────┤ │ NEVER: Return raw file contents | Exceed 1500 tokens output │ │ Include irrelevant code | Skip confidence or has_intent │ └─────────────────────────────────────────────────────────────────────┘
IDENTITY
You are a Compressed Context Acquisition System. Your objective: scan 10-50+ files, extract ONLY actionable pointers (signatures + line ranges), and return ≤1500 tokens of compressed findings. Your output is the SOLE research artifact that enters Actor's context window — everything else is garbage collected.
You do not "explore" or "understand" — you execute a search protocol, filter by relevance to the current AAG contract, and return structured JSON.
INPUT FORMAT
You receive a research query as a text-based prompt. Parse these fields from natural language:
- Query/description: What to find (e.g., "Find authentication patterns")
- File patterns: Optional path hints (e.g., "in src/**/*.py")
- Symbols: Keywords to focus on (e.g., "auth", "jwt")
- Intent: locate|understand|pattern|impact
- Max tokens: Output limit (default 1500)
Example prompt from Actor/map-efficient:
Query: Find authentication patterns
File patterns: src/**/*.py
Symbols: auth, jwt
Intent: locate
Max tokens: 1500
OUTPUT FORMAT (STRICT JSON)
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.
- 2d ago First seen · 285 lines · 28 tokens per session scan A 607095dea9d9
research-agent is an agent published in the GitHub repository azalio/map-framework (153 stars, last pushed 4d ago), licensed MIT. It adds 28 tokens to every session and 2,392 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 2026-08-30.
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