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 skills/resolve-ai-oss/resolve-ai-plugins/prod-contextnpx skills add resolve-ai-oss/resolve-ai-plugins --skill prod-contextgit clone --depth 1 https://github.com/resolve-ai-oss/resolve-ai-pluginsWrote 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/resolve-ai-oss/resolve-ai-plugins/prod-context)<a href="https://agentmods.dev/skills/resolve-ai-oss/resolve-ai-plugins/prod-context"><img src="https://agentmods.dev/badge/skills/resolve-ai-oss/resolve-ai-plugins/prod-context.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 | $0.00163 | $0.01224 |
| Opus 5 | $0.00081 | $0.00612 |
| Sonnet 5 | $0.00033 | $0.00245 |
| Haiku 4.5 | $0.00016 | $0.00122 |
Grade A, and why
prod-context 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-implementation production context
Before implementing a change to a production-facing path, get the runtime picture the code can't show — real traffic, telemetry coverage, baselines, live trouble — and let it shape the approach. Resolve owns the telemetry and the service map; query it only for what you can't read off the repo.
When to trigger
Trigger at the plan → implement boundary: the task is understood, the in-scope files or areas are known, and no code is written yet — so production reality can still shape the approach, not just validate it afterwards.
Skip trivial changes (typo, comment, formatting, docs-only) and anything no production system would monitor.
Decide whether to ask
Ask Resolve only when a production runtime unknown would change how you build this. If the open questions are answerable by reading the repo, or the area is quiet with no runtime unknowns, skip the ask — say so in one line and continue with the work.
Compose the ask
Pick the few angles that fit this change — don't ask all of them, and don't ask generically. Each is something the code can't tell you.
Know the area:
- Operational profile — real traffic and request volume, peak vs quiet, how heavily it's exercised. A hot path demands far more care than a rarely-hit endpoint.
- Telemetry coverage — what observability exists here (logs, metrics, traces) and where the blind spots are: will you be able to see your change's effect?
- Baseline behavior — the normal latency / error rate / throughput, so you know what "good" looks like.
Check for trouble:
- Is the ground shaking? — active alerts, ongoing incidents, or open investigations on the area. Don't build on top of an active fire — or realize your change is about it.
- Regression history — recent deploys and prior incidents on these paths; where it's bitten before, build defensively.
- Runtime downstream fanout — what actually gets hit downstream at volume, from traces and the service map. Scopes rollout and testing.
- What to verify after — given the change, the production signals to watch post-deploy.
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 · 95 lines · 163 tokens per session scan A 77891f8a5c63
prod-context is a skill published in the GitHub repository resolve-ai-oss/resolve-ai-plugins (4 stars, last pushed 12d ago), licensed Apache-2.0. It adds 163 tokens to every session and 1,224 once invoked, about $0.0008 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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