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 existential-birds/beagle --skill review-feedback-schemagit clone --depth 1 https://github.com/existential-birds/beagleWrote 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/existential-birds/beagle/review-feedback-schema)<a href="https://agentmods.dev/skills/existential-birds/beagle/review-feedback-schema"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/review-feedback-schema/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/existential-birds/beagle/review-feedback-schema"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/review-feedback-schema.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 186 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00027 | $0.02356 |
| Opus 5.5 | $0.00011 | $0.00942 |
| Sonnet 5.5 | $0.00005 | $0.00471 |
| Haiku 4.5 | $0.00003 | $0.00236 |
Grade A, and why
review-feedback-schema 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Feedback Schema
Purpose
Structured format for logging code review outcomes. This data enables:
- Identifying rules that produce false positives
- Tracking skill accuracy over time
- Automated skill improvement via pattern analysis
Schema
date,file,line,rule_source,category,severity,issue,verdict,rationale
| Field | Type | Description | Example Values |
|---|---|---|---|
date |
ISO date | When review occurred | 2025-12-23 |
file |
path | Relative file path | amelia/agents/developer.py |
line |
string | Line number(s) | 128, 190-191 |
rule_source |
string | Skill and rule that triggered issue | python-code-review/common-mistakes:unused-variables, pydantic-ai-common-pitfalls:tool-decorator |
category |
enum | Issue taxonomy | type-safety, async, error-handling, style, patterns, testing, security |
severity |
enum | As flagged by reviewer | critical, major, minor |
issue |
string | Brief description | Return type list[Any] loses type safety |
verdict |
enum | Human decision | ACCEPT, REJECT, DEFER, ACKNOWLEDGE |
rationale |
string | Why verdict was chosen | pydantic-ai docs explicitly support this pattern |
Gates (feedback log rows)
Run in order before appending a row. Do not skip ahead while a gate fails.
-
Evidence bound to code
- Pass when:
fileis a repo-relative path that exists (or existed at review time), andlineidentifies line number(s) you actually opened—not only a paraphrased summary.
- Pass when:
-
Rule source attributable
- Pass when:
rule_sourcematchesskill-name[/section]:rule-id(see Rule Source Format). If the trigger is unknown, set a best-effort source and state the gap inrationaleinstead of inventing a rule id.
- Pass when:
-
Verdict backed by artifact
- Pass when: For
REJECT,rationalecites something checkable (command + output, doc URL, or quoted code). ForACCEPT, it states the fix or points to the change. ForDEFER/ACKNOWLEDGE, it names a tracker, timeline, or documented intent per Verdict Types.
- Pass when: For
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 · 199 lines · 27 tokens per session scan A 8a1118246212
review-feedback-schema is a skill published in the GitHub repository existential-birds/beagle (84 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 2,356 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 2026-08-30.
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