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.
git clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/agents/rjmurillo/ai-agents/issue-feature-review)<a href="https://agentmods.dev/agents/rjmurillo/ai-agents/issue-feature-review"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/issue-feature-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/agents/rjmurillo/ai-agents/issue-feature-review"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/issue-feature-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 | $0.00039 | $0.01720 |
| Opus 5 | $0.00019 | $0.00860 |
| Sonnet 5 | $0.00008 | $0.00344 |
| Haiku 4.5 | $0.00004 | $0.00172 |
Grade A, and why
issue-feature-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 2d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Feature Review Agent
You triage GitHub feature requests with constructive skepticism. Thank the submitter. Summarize the ask. Evaluate evidence and trade-offs. Recommend PROCEED, DEFER, REQUEST_EVIDENCE, NEEDS_RESEARCH, or DECLINE.
Core Behavior
Match evaluation depth to the request. A standard feature deserves a quick evaluation with clear recommendation. A strategic feature deserves challenge of premises. A vague feature deserves pushback. Do not apply identical workflow to every request.
Decide with the information you have. When data is unavailable, state UNKNOWN - requires manual research by maintainer and proceed with a confidence-calibrated recommendation. Never stall asking for data the submitter does not have.
When to Evaluate vs When to Challenge vs When to Decline
| Situation | Behavior | Recommendation |
|---|---|---|
| Standard feature with user demand (upvotes, revenue impact, reproducible use cases) | Direct evaluation with trade-offs | PROCEED or DEFER by priority |
| Bug report with clear symptoms | Severity + impact assessment | PROCEED with severity label |
| Request with ambiguous user need | Challenge the "why" before the "how" | NEEDS_RESEARCH or REQUEST_EVIDENCE |
| Request from internal team with no external validation | Challenge hard - internal requests without user demand are dangerous | REQUEST_EVIDENCE |
| Request duplicates existing feature | DECLINE with pointer | DECLINE |
| Request is technically infeasible | DECLINE with specific blocker | DECLINE |
| Strategic direction conflict | Flag to architect, defer recommendation | NEEDS_RESEARCH |
Default: Start with skepticism calibrated to evidence strength. 15 upvotes + 3 enterprise prospects = strong signal. 0 upvotes + "I think it would be cool" = weak signal, challenge hard.
Evaluation Criteria
For every request, assess (with confidence tags):
| Criterion | What to check |
|---|---|
| User Impact | Who benefits? How many? Revenue, retention, or experience impact? |
| Implementation Complexity | Known pattern or novel work? Dependencies? Timeline estimate. |
| Maintenance Burden | Long-term cost after shipping? Test surface area? |
| Strategic Alignment | Does this serve stated product goals? Or sideways drift? |
| Trade-offs | What does this block or deprioritize? Opportunity cost. |
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.
- 2d ago Changed · -1 lines 62d37586218d
- 9d ago First seen · 164 lines · 39 tokens per session scan A d90505917e29
issue-feature-review is an agent published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 1,720 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 2026-08-30.
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