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/radiantlogicinc/fastworkflow/design-context-modelsnpx skills add radiantlogicinc/fastworkflow --skill design-context-modelsgit clone --depth 1 https://github.com/radiantlogicinc/fastworkflowWrote 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/radiantlogicinc/fastworkflow/design-context-models)<a href="https://agentmods.dev/skills/radiantlogicinc/fastworkflow/design-context-models"><img src="https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/design-context-models.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 | $0.00131 | $0.02602 |
| Opus 5 | $0.00066 | $0.01301 |
| Sonnet 5 | $0.00026 | $0.00520 |
| Haiku 4.5 | $0.00013 | $0.00260 |
Grade A, and why
design-context-models 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 5d 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Designing context models for routing
The mechanism being optimized
Two files, two unrelated effects. Confusing them makes every later decision guesswork.
| File | Key | Effect |
|---|---|---|
_commands/context_inheritance_model.json |
base |
Adds the base context's commands to the child's callable surface. Controls reachability. |
context_hierarchy_model.json |
parent |
No runtime navigation effect at all. Read at training time only. |
A context's classifier does not choose among its surface. Its label set is:
own commands
+ inherited commands (transitive over `base`)
+ framework core commands + `wildcard`
+ every ancestor's surface, trained as `wildcard` <- this comes from the `parent` axis
go_up and escalation both walk the live object's parent — the Context.get_parent callback
returning obj.parent — never the JSON. The parent axis exists to teach a context which
utterances are not its own, so it answers wildcard, which makes the runtime climb the live chain
and re-predict there.
Three consequences drive everything below:
- A
parentedge no command can produce is pure cost. It can never be walked at runtime, and it still enlarges the wildcard class. - A missing
parentedge that is produced at runtime is worse. The child was never taught to reject that ancestor's utterances, so it confidently misroutes instead of escalating. - Only a lone confident
wildcardescalates.wildcardreturned beside real candidates raises an ambiguity prompt instead, so "escalation" and "routing" trade against each other and must be scored separately.
Rule 1 — derive the hierarchy from code, never hand-maintain it
The true parent graph is whatever each command constructs and assigns as the current context. Scan
for the assignment to current_command_context and record the class being entered; that set is
the containment graph. Diff it against the declared JSON and fix both directions: drop edges nothing
produces, add edges something produces.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 216 lines · 131 tokens per session scan A d2ce8843ce64
design-context-models is a skill published in the GitHub repository radiantlogicinc/fastworkflow (52 stars, last pushed yesterday), licensed Apache-2.0. It adds 131 tokens to every session and 2,602 once invoked, about $0.0007 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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