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 skills add jeffreytse/grimoire-core --skill apply-graduated-entry-point-techniquegit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/jeffreytse/grimoire-core/apply-graduated-entry-point-technique)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-graduated-entry-point-technique"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-graduated-entry-point-technique/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/skills/jeffreytse/grimoire-core/apply-graduated-entry-point-technique"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-graduated-entry-point-technique.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 | $0.00100 | $0.01838 |
| Opus 5.5 | $0.00040 | $0.00735 |
| Sonnet 5.5 | $0.00020 | $0.00368 |
| Haiku 4.5 | $0.00010 | $0.00184 |
Grade A, and why
apply-graduated-entry-point-technique 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 today.
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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Graduated Entry Point Technique
When an audience isn't motivated by the actual goal but has a specific want of their own, use that existing want, unmodified, as the literal entry point to get them engaged — then execute a planned, disclosed transition to the real objective once engagement is secured.
Why This Is Best Practice
Why best: Trying to motivate an audience directly with a goal they aren't ready for or don't yet value produces the same result as warning children unmoved by an abstract danger they don't understand — no movement. Using their own already-existing, concrete want as the vehicle gets them moving on their own terms; the transition to the actual objective happens only once they're already engaged, when it's far easier to introduce.
Adopted by: Madeline Hunter's "anticipatory set" model — opening a lesson with something students already want or care about before pivoting into the actual curricular objective — became a standard component of U.S. teacher-training curricula (ITIP) after her 1982 "Mastery Teaching." Freedman & Fraser's foot-in-the-door research (1966) documented compliance-escalation from an initial small, wanted request; modern inbound-marketing methodology (documented in practices like HubSpot's) runs the same structure at scale: offer free content or a tool the audience already wants (a "lead magnet"), then transition to the actual product offer once attention is earned.
Impact: Hunter's anticipatory-set model is credited with measurably improving lesson engagement in teacher-effectiveness research by giving students a reason to attend to a lesson before its actual content is revealed, rather than requiring them to already value the curricular objective. Foot-in-the-door research shows compliance rates rise substantially once a person has already said yes to a related, wanted initial request — the same underlying mechanism of securing engagement on the audience's terms before the actual ask.
Why best (continued): This differs from the closest neighboring techniques already in this repo. apply-motivational-interviewing evokes a client's own motivation about the same target — it never substitutes an unrelated want as a bridge; this skill specifically permits the entry-point vehicle to be different from the final objective, provided the eventual transition is planned and disclosed. design-differentiated-instruction-plan explicitly states that differentiation "adjusts the pathway, not the destination" and treats interest as one transparent input to lesson design, not a vehicle later swapped for a different goal. This skill's distinguishing mechanism is exactly that graduated substitution — entry on the audience's want, transition to the practitioner's actual objective — which neither of those skills performs.
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.
- today First seen · 66 lines · 100 tokens per session scan A 9169c52d2f32
apply-graduated-entry-point-technique is a skill published in the GitHub repository jeffreytse/grimoire-core (5 stars, last pushed today), licensed MIT. It adds 100 tokens to every session and 1,838 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-17.
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