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 jeonnoin-alt/Eureka --skill receiving-research-reviewgit clone --depth 1 https://github.com/jeonnoin-alt/EurekaWrote 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/jeonnoin-alt/eureka/receiving-research-review)<a href="https://agentmods.dev/skills/jeonnoin-alt/eureka/receiving-research-review"><img src="https://agentmods.dev/badge/skills/jeonnoin-alt/eureka/receiving-research-review/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/jeonnoin-alt/eureka/receiving-research-review"><img src="https://agentmods.dev/badge/skills/jeonnoin-alt/eureka/receiving-research-review.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 | $0.00030 | $0.00743 |
| Opus 5 | $0.00015 | $0.00371 |
| Sonnet 5 | $0.00006 | $0.00149 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
receiving-research-review 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 12d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Receiving Research Review
Handle review feedback with scientific rigor. Verify claims against actual data before acting. Push back on incorrect assessments with evidence.
Core principle: Verify before implementing. Evidence before agreement. Scientific correctness over social comfort.
Response Protocol
When you receive a research review report:
- READ the complete review without reacting
- UNDERSTAND — restate each issue in your own words, or ask for clarification
- VERIFY — check each claim against the actual data files
- EVALUATE — is the feedback scientifically valid for THIS study?
- RESPOND — technical acknowledgment or reasoned pushback
- IMPLEMENT — one issue at a time, verify each fix
Forbidden Responses
Never respond to a review with:
- "You're absolutely right!"
- "Great point!"
- "Excellent feedback!"
- "Let me implement that now" (before verification)
These are performative, not scientific. The reviewer may be wrong. Verify first.
Handling Unclear Feedback
If ANY item in the review is unclear:
STOP. Do not implement anything.
Ask for clarification on ALL unclear items before proceeding. Why: review items may be related. Partial understanding leads to wrong implementations that waste time and may introduce new errors.
When to Push Back
Push back (with evidence) when:
- The reviewer's suggestion contradicts your actual data
- The reviewer lacks context about domain-specific practices
- The suggestion would introduce a known confound
- The statistical recommendation is inappropriate for your data type
- The reviewer conflated your phase with a later phase
- The suggestion violates your pre-registered analysis plan (unless the plan itself was flawed)
How to push back:
- State the specific claim you disagree with
- Provide the file path and data that contradicts it
- Explain the scientific reasoning
- Propose an alternative if you have one
For External Reviews (journal peer review, collaborator feedback)
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.
- 12d ago First seen · 90 lines · 30 tokens per session scan A b8742345b028
receiving-research-review is a skill published in the GitHub repository jeonnoin-alt/Eureka (2 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 743 once invoked, about $0.0002 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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