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/linkpranay-ai/context-engineering-protocolWrote 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/linkpranay-ai/context-engineering-protocol/ult-context-generate)<a href="https://agentmods.dev/commands/linkpranay-ai/context-engineering-protocol/ult-context-generate"><img src="https://agentmods.dev/badge/commands/linkpranay-ai/context-engineering-protocol/ult-context-generate/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/commands/linkpranay-ai/context-engineering-protocol/ult-context-generate"><img src="https://agentmods.dev/badge/commands/linkpranay-ai/context-engineering-protocol/ult-context-generate.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 | $0.00039 | $0.00428 |
| Opus 5 | $0.00019 | $0.00214 |
| Sonnet 5 | $0.00008 | $0.00086 |
| Haiku 4.5 | $0.00004 | $0.00043 |
Grade A, and why
context-generate 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 yesterday.
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.
What it actually says
Read and follow the skill at .github/skills/ult-context-generate/SKILL.md.
When invoked directly by an engineer:
- Read
context-config.yamlat the project root for layer paths and budget settings. If it does not exist, copystarter_kits/context_engineering/context-config.yaml.templateto the project root ascontext-config.yamland adjust the layer paths, or proceed with the documented defaults. - Answer the Step 1 scope-clarification questions (feature/subsystem, task type, new vs. existing, scope boundary, known gaps) — all five are required.
- The skill queries the code graph via
ult-codegraph(graphify-out/graph.json— run/ult-codegraphfirst if it doesn't exist yet), readsdocs/requirements/for What-L2, and — ifstarter_kit/project_guidelines/COMPILED-GUIDELINES.mdexists — loads it as the Constraints layer (D11). - Resolve every open question and domain-enrichment suggestion one at a time; don't batch them.
- Review the assembled product context package and org convention package, and say APPROVE for both — nothing downstream proceeds without explicit approval.
- Once approved, hand off to the artifact-generation skill for the chosen task type
(whatever skill in your setup handles that
task_type).
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.
- yesterday First seen · 36 lines · 39 tokens per session scan A 806091596a49
context-generate is a command published in the GitHub repository linkpranay-ai/context-engineering-protocol (8 stars, last pushed yesterday), licensed Apache-2.0. It adds 39 tokens to every session and 428 once invoked, about $0.0002 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 2026-09-08.
Other commands, from other repositories
plan
Execute the implementation planning workflow using the plan template to generate design artifacts.
implement
Execute implementation by processing atomic task files one at a time with Context Pinning (Atomic Traceability Model).
clarify
Pre-plan interview — resolves spec ambiguity (v0.1 contract), pins the architectural lurkers from .specify/knowledge/architectural-lurkers.yaml, fires trigger-driven probes from .specify/knowledge/triggers.yaml, walks compliance scope, and writes decisions to specs/defaults/registry.yaml with provenance tagging.
cleanup
Detect and remove orphaned code, unused components, dead routes, and stale database artifacts.
specify
Create or update the feature specification from a natural language feature description.
analyze-competitors
Analyze competitors following Station 03 discovery procedures. Creates structured competitive intelligence for use in downstream planning.