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.
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/commands/adriannoes/awesome-agentic-ai/intel)<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/intel"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/intel.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.00440 |
| Opus 5 | $0.00016 | $0.00220 |
| Sonnet 5 | $0.00006 | $0.00088 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
intel 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 3d 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.
This is a copy
100% identical to intel — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/intel
Fetch actionable intelligence for a target.
What This Does
- Runs
learn.pyfor CVEs and advisories matching the target's tech stack - Fetches HackerOne Hacktivity for the target (via HackerOne MCP if available)
- Cross-references with hunt memory — flags untested CVEs and new endpoints
- Outputs prioritized intel with hunt recommendations
Usage
/intel target.com
Output
INTEL: target.com
═══════════════════════════════════════
ALERTS:
[CRITICAL] CVE-2026-XXXX — Next.js middleware bypass (CVSS 9.1)
target.com runs Next.js 14.2.3 (vulnerable). Patch: 14.2.4.
→ You haven't tested this endpoint yet. Hunt candidate.
[HIGH] New feature detected: /api/v3/billing/invoices
Not in your tested_endpoints list. 3 new paths.
→ New = unreviewed. Priority hunt target.
[INFO] 2 new disclosed reports on HackerOne for target.com
→ Read for methodology insights before hunting.
MEMORY CONTEXT:
Last hunted: 2026-03-24 (2 days ago)
Tech stack: Next.js 14.2.3, GraphQL, PostgreSQL
Untested CVEs: 1 critical, 0 high
Data Sources
| Source | What | Auth required? |
|---|---|---|
learn.py — NVD |
CVEs matching tech stack | No |
learn.py — GitHub Advisory |
Security advisories | No |
learn.py — HackerOne Hacktivity |
Disclosed reports | No |
| HackerOne MCP (if connected) | Program stats, policy | No (public) |
| Hunt memory | Previously tested endpoints | Local files |
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.
- 3d ago First seen · 55 lines · 0 tokens per session scan A d8dfa2a1ceaf
intel is a command published in the GitHub repository adriannoes/awesome-agentic-ai (55 stars, last pushed 8d ago), licensed MIT. It adds 32 tokens to every session and 440 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to intel, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
design-onboarding
Design a first-run experience end to end — activation path, progressive disclosure, and time to first value.
assistant-auto
Orchestrator in automatic mode. Choose the workflow that semantically fits based on the request + the injected repo context, then execute immediately via Skill.
amby.clarify
Resolve the open [NEEDS CLARIFICATION] markers in a feature spec.
aw-upgrade
Upgrade gh-aw extension, recompile and validate all workflows, and open a PR with changes.
code-review
Code review — local uncommitted changes or GitHub PR (pass PR number/URL for PR mode).
review
Conduct a five-axis code review — correctness, readability, architecture, security, performance.