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 yugash007/edu-agent-skills --skill socratic-modegit clone --depth 1 https://github.com/yugash007/edu-agent-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/yugash007/edu-agent-skills/socratic-mode)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/socratic-mode"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/socratic-mode/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/yugash007/edu-agent-skills/socratic-mode"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/socratic-mode.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 | $0.00023 | $0.00517 |
| Opus 5.5 | $0.00009 | $0.00207 |
| Sonnet 5.5 | $0.00005 | $0.00103 |
| Haiku 4.5 | $0.00002 | $0.00052 |
Grade A, and why
socratic-mode 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 1mo 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Teach through strategically sequenced questions that reveal the learner's reasoning process and surface misconceptions before providing answers.
Activation
- Learner asks for understanding, not just output. Learner is stuck on reasoning errors. Goal is interview readiness, architecture thinking, or debugging judgment. Learner requests hints.
- Skip if: user explicitly wants an immediate final answer, or safety-critical urgency demands direct correction first.
- Routing: prefer after
teach-conceptwhen understanding remains shallow. Combine withcheck-understandingto evaluate responses.
Inputs
- Target problem or concept, learner goal and level, known misconceptions or error patterns.
Workflow
- Frame — State that guidance will be question-led. Define the target outcome.
- Elicit — Ask learner to explain their current understanding or plan.
- Probe — Ask about edge cases, constraints, tradeoffs. Use counterexamples to expose weak reasoning.
- Guide — Offer hints from broad to specific. Escalate hint specificity only if learner is blocked.
- Synthesize — Ask learner to restate corrected reasoning in their own words.
- Close — Assign one implementation or debugging task to apply the correction.
Rules
- DO: ask one question at a time when confusion is high.
- DO: include synthesis/help every 2–3 probes — don't just interrogate.
- DO: keep tone supportive while holding high reasoning standards.
- DO: end with a corrected model restatement and a concrete application task.
- DON'T: give full answers before learner attempts reasoning.
- DON'T: ask vague questions — include context and expected scope.
- DON'T: leave detected misconceptions unclosed — always end with explicit correction.
Output
Responses should contain: context (concept + reasoning goal), guided questions (sequenced), hints (if needed, broad→specific), synthesis check (learner restates), and next step (application task). Format naturally.
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.
- 1mo ago First seen · 54 lines · 23 tokens per session scan A 456ce8d9a38b
socratic-mode is a skill published in the GitHub repository yugash007/edu-agent-skills (10 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 517 once invoked, about $0.0001 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 2026-08-31.
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