RAD_framework: Skill for Codex

.agents/skills/rad-insights/SKILL.md

rad-insights is a skill for Codex from seanrreid/RAD_framework. It costs 45 tokens per session (18,144 once invoked), scanned A, original, MIT.

A review-history analyzer examines recorded RAD review cycles to find recurring issue types, files that often cause problems, and changes in review results over time. RAD review is a repeated process for examining code or work and recording findings.

In plain words
What is it for?
Use it to inspect findings since a date, summarize patterns across review cycles, identify hotspot files, or produce a bundled improvement plan when requested.
Why use it?
It turns separate review records into a broader view of repeated problems instead of relying on memory or isolated reports. With enough history, it can also draft a plan for categories that cross configured thresholds.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: reads .claude/ paths; mentions CLAUDE.md; positional $N argument.

This is seanrreid/RAD_framework's own configuration. It tells Codex how to work on RAD_framework itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything RAD_framework configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is DEFAULT_BRANCH=$(node harness/cli.js config get default_branch) || { echo "rework/load: cannot resolve default_branch" >&2; exit 1; }.

Reuse

Borrowing it

Nothing to install: this file belongs to seanrreid/RAD_framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/seanrreid/RAD_framework/main/.agents/skills/rad-insights/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/seanrreid/RAD_framework

Made for: Codex.

Wrote 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.

agentmods badge for rad-insights

README.md
[![agentmods](https://agentmods.dev/badge/skills/seanrreid/rad_framework/rad-insights/github.svg)](https://agentmods.dev/skills/seanrreid/rad_framework/rad-insights)
Your own site
<a href="https://agentmods.dev/skills/seanrreid/rad_framework/rad-insights"><img src="https://agentmods.dev/badge/skills/seanrreid/rad_framework/rad-insights/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.

agentmods 80×15 button for rad-insights

Your own site · 80×15
<a href="https://agentmods.dev/skills/seanrreid/rad_framework/rad-insights"><img src="https://agentmods.dev/badge/skills/seanrreid/rad_framework/rad-insights.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 18,144 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00045 $0.18144
Opus 5.5 $0.00018 $0.07258
Sonnet 5.5 $0.00009 $0.03629
Haiku 4.5 $0.00005 $0.01814

Measured 3d ago against content hash 6cc6d145ac30, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rad-insights 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 3d 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.

.agents/skills/rad-insights/SKILL.md · 1,391 lines

How it starts

The opening of the file, as written. The whole thing — 1,391 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/rad-insights

Analyze finding patterns across all recorded RAD review cycles.

Input

the text the user wrote after the skill name (optional):

  • --since YYYY-MM-DD to limit analysis to cycles on or after that date.
  • --draft-plans to bundle every threshold-crossing Step 3b category into ONE Gate-1-blocked plan via scripts/draft-insights-plan.sh (see Step 3b). Without this flag the skill drafts nothing and Step 3b output is unchanged.

Process

Step 1: Check for findings log

if [ ! -f .agents/findings.jsonl ]; then
  echo "No findings log found. Run /rad-review on at least one rad/ work branch first."
  exit 0
fi

Count records to determine report depth:

# Total cycles
jq -r 'select(.type=="cycle")' .agents/findings.jsonl | jq -s 'length'

# Total findings
jq -r 'select(.type=="finding")' .agents/findings.jsonl | jq -s 'length'

If fewer than 3 cycles exist, note the limited data and output a raw findings list instead of a pattern report.

Step 2: Apply date filter (if --since provided)

# Filter findings to on/after the provided date
jq -r --arg since "[DATE]" 'select(.date >= $since)' .agents/findings.jsonl

Step 3: Compute finding frequencies

# Category frequency (all priorities)
jq -r 'select(.type=="finding") | .category' .agents/findings.jsonl \
  | sort | uniq -c | sort -rn

# HIGH findings by category
jq -r 'select(.type=="finding" and .priority=="HIGH") | .category' .agents/findings.jsonl \
  | sort | uniq -c | sort -rn

# Finding frequency per file (top 10)
jq -r 'select(.type=="finding") | .file' .agents/findings.jsonl \
  | sort | uniq -c | sort -rn | head -10

# Findings by reviewer
jq -r 'select(.type=="finding") | .reviewer' .agents/findings.jsonl \
  | sort | uniq -c | sort -rn

Step 3b: Findings Recurrence — convention/lint suggestions

A category that keeps recurring across review cycles is a signal the project is missing a convention (or a lint rule) that would prevent it. This step turns the Step 3 category counts into suggestions only — every output is framed "suggestion — apply via PR; never auto-applied". This skill NEVER edits AGENTS.md, CLAUDE.md or scripts/lint-plan.sh itself.

Read the full file on GitHub · 1,391 lines

Files

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.

Changes

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.

  1. 3d ago Changed · +71 lines 6cc6d145ac30
  2. 13d ago Changed · +96 lines 36c362c19989
  3. 17d ago Changed · +69 lines 78dad1d75424
  4. 20d ago First seen · 1,155 lines · 45 tokens per session scan A 2fdd95113610

Subscribe to this mod's changes

rad-insights is a skill published in the GitHub repository seanrreid/RAD_framework (5 stars, last pushed 4d ago), licensed MIT. It adds 45 tokens to every session and 18,144 once invoked, about $0.0002 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 2026-10-10.

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