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 reciprocitygit 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/reciprocity)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/reciprocity"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/reciprocity/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/reciprocity"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/reciprocity.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 | $0.00127 | $0.02097 |
| Opus 5 | $0.00063 | $0.01048 |
| Sonnet 5 | $0.00025 | $0.00419 |
| Haiku 4.5 | $0.00013 | $0.00210 |
Grade A, and why
reciprocity 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 9d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reciprocity
Overview
The rule of reciprocity — if someone gives you something, you owe them something back — recurs across virtually every documented human culture (Mauss 1925; Gouldner 1960). The rule operates below deliberation, scales asymmetrically, and fires even when the favor was unrequested or from someone you dislike. Regan (1971): a 10¢ Coke produced ~50¢ in compliance, and liking stopped predicting behavior once a favor was in play. Three operating properties: (1) asymmetric exchange — repayment routinely exceeds the favor; (2) override of liking — the obligation does the work; (3) "no obligation" disclaimers are part of the install, not an exception.
Composes with social-proof, anchoring (door-in-the-face combines reciprocity with concession-anchoring), and repeated-games-reputation.
When to Use
Apply when: accepting a gift/sample/concession where the giver has a future ask; designing sales, fundraising, or partner-development sequences; a negotiation counterpart just conceded and you feel pulled to match; evaluating cumulative small gifts in regulated relationships; weighing free AI credits / a generous free tier / a free vendor pilot where a paid contract or lock-in is the eventual ask; someone asks "why are they so generous?" or "should I concede back?"
When NOT to use: exchange is explicitly priced; gift is a normal friendship pattern with no pending ask; favor is too small to constrain any future action; bright-line legal/ethical rules apply — follow the rule, skip the analysis.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete situation → 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 what-it-is: if someone gives you something — even small, even unwanted — you'll feel a strong pull to repay, and the repayment tends to be bigger than what you got, even when you don't like the giver.
- Check fit against When to Use / When NOT to use. If it's commerce or normal friendship, point elsewhere.
- Elicit their real situation — a concrete current case. Never run the analysis on hypotheticals.
[WAIT — do not advance until user responds]
- Run The Process one step at a time — name the favor, cost to giver, eventual ask, counterfactual test.
[WAIT — do not advance until user responds]
- Close by naming the one concrete move — accept, decline, restructure, or counter — with the reason.
[WAIT — do not advance until user responds]
What ships with it
3 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.
- 9d ago First seen · 118 lines · 127 tokens per session scan A 3ba06694cc10
reciprocity is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 127 tokens to every session and 2,097 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 2026-09-03.
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