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 deciqAI/knowledge-skills --skill door-in-the-facegit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/door-in-the-face)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/door-in-the-face"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/door-in-the-face/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/deciqai/knowledge-skills/door-in-the-face"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/door-in-the-face.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00123 | $0.01916 |
| Opus 5 | $0.00062 | $0.00958 |
| Sonnet 5 | $0.00025 | $0.00383 |
| Haiku 4.5 | $0.00012 | $0.00192 |
Grade A, and why
door-in-the-face 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 11d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Door-in-the-Face Technique
Overview
Ask for something large (expect refusal), then retreat to the smaller request you actually wanted. The empirically documented result: compliance with the smaller ask is 2-3x higher than asking for it directly (Cialdini et al., 1975). The mechanism is reciprocal concession — the target perceives your retreat as a concession and feels social pressure to match it.
Three operations: recognize DITF when used on you; design it ethically as a proposer; distinguish it from pure anchoring (two requests with refusal vs. a single number). Composes with reciprocity, anchoring, signaling-games, batna-zopa.
When to Use
- Designing a negotiation opening where you want to land at a specific price/term
- Designing a fundraising ask where the target gift is moderate but commitment to the cause is high
- Designing a sales process where you want customers in a specific tier
- Recognizing that you are being run with DITF by an opposing party
- Someone says: "door-in-the-face," "reciprocal concession," "anchor high then retreat," "they conceded so I should too"
Not when: the larger request is so absurd the target reads the opening as bad faith (the technique collapses); the target has no reciprocity norm operating; you are negotiating with a counterparty who will read your retreat as DITF.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete case → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: if you ask for X (large) and get refused, then ask for Y (smaller, what you actually wanted), you get Y more often than if you asked for Y directly — because the target feels they should concede after watching you concede.
- Check fit — absurd-anchor situations → DITF collapses; point elsewhere.
- Elicit the real situation.
[WAIT — do not advance until user responds]
- One question at a time: is this technique being used on me? What's the actual smaller ask? What's my counterparty's BATNA?
[WAIT — do not advance until user responds]
- Close: the specific opening + retreat sequence, or the specific defense.
[WAIT — do not advance until user responds]
What ships with it
2 files 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.
- 11d ago First seen · 122 lines · 123 tokens per session scan A 694a4d56618b
door-in-the-face is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 9d ago), licensed MIT. It adds 123 tokens to every session and 1,916 once invoked, about $0.0006 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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