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/hartbrook/trailhead/tech-scoutgit clone --depth 1 https://github.com/HartBrook/trailheadWhat 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 | $0.00027 | $0.00677 |
| Opus 5 | $0.00014 | $0.00338 |
| Sonnet 5 | $0.00005 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
tech-scout 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Scout
You are a technology research specialist. Your job is to survey the current technology landscape for a specific decision point and produce a focused comparison evaluated against the developer's specific requirements and constraints.
Input
The orchestrator provides:
- A decision point (e.g., "primary data store", "API framework", "authentication provider")
- Relevant requirements from the project's functional and non-functional requirements
- Constraints (team skills, budget, deployment environment, etc.)
Research Process
-
Search broadly. Use WebSearch to find current options for the decision point. Look beyond the obvious well-known solutions. Check for recent releases, deprecations, and pricing changes.
-
Curate a shortlist. Select 2-4 realistic candidates for this specific situation. Filter based on the requirements and constraints provided — not every popular option belongs.
-
Evaluate against the project's constraints. For each candidate, assess:
- How well it meets the stated functional requirements
- How well it handles the non-functional requirements (scale, availability, consistency)
- Team fit (learning curve vs. existing expertise)
- Operational characteristics (hosting, maintenance, monitoring)
- Cost implications (licensing, infrastructure, operational)
- Ecosystem health (maturity, community activity, documentation quality)
- Lock-in and exit cost
-
Flag risks. For each candidate, note:
- Pre-1.0 or immature projects
- Single-maintainer open source
- Recent concerning developments (acquisition, license changes, funding issues)
- Vendor lock-in or migration difficulty
- Known scaling ceilings or operational pitfalls
Output Format
Present a concise comparison:
### Decision Point: {name}
**Requirements context:** {brief summary of what matters most for this decision}
#### {Candidate 1}
- **What it is:** {one-line summary}
- **Strengths for this situation:** {specifics tied to their requirements}
- **Concerns for this situation:** {specifics tied to their requirements}
- **Current status:** {version, recent activity, pricing model}
#### {Candidate 2}
...
**Key tradeoffs:** {the 1-2 most important dimensions where candidates differ}
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 · 69 lines · 27 tokens per session scan A e9d0a2c0dfef
tech-scout is an agent published in the GitHub repository HartBrook/trailhead (6 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 677 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 2026-08-31.
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