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 agentmods add rules/secondlifes/code-intel/analysis-outputgit clone --depth 1 https://github.com/SecondLifes/code-intelWhat 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 | $0.01449 | $0.01449 |
| Opus 5 | $0.00724 | $0.00724 |
| Sonnet 5 | $0.00290 | $0.00290 |
| Haiku 4.5 | $0.00145 | $0.00145 |
Grade A, and why
analysis-output 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 yesterday.
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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analysis/Evaluation/Edit Output Convention
The shared input-resolution and output-naming rule for the bundled
rad-prompt-studio skill's three master prompts
(analysis-base-prompt.md, evaluation-base-prompt.md,
edit-base-prompt.md). None of those three files restate this — they all
point here. Without this file present, none of them can resolve where a
report goes or what it is named.
Where input comes from
- A specific path is named in the request (a file or a folder) → analyze/evaluate/edit that path directly, in place. Nothing gets copied anywhere first.
- No path named, request is "review this project/repo" or
equivalent → bulk traversal mode (
analysis-base-prompt.md's "Determining the target" section). The map is this repo's ownAGENTS.md+docs/proje-haritasi.md, not a staged folder. - Request is "system analizi"/"system analysis" or equivalent →
System Analysis mode (
analysis-base-prompt.md's own section). In a kit, the system layer is.agents/skills/,.agents/rules/,.agents/commands/and the four AI-primary identity files (AGENTS.md,.claude/CLAUDE.md,.github/copilot-instructions.md,.gemini/rules/project-rules.md) — never the kit's own subject-matter code undersrc/orexamples/. Drop everything on the exclusion list (any*.tr-TR.mdfile, and any bundled third-party skill this kit did not author), then present the rest as a numbered pick-list and wait for the user to choose before analyzing anything. - No path named and no bulk-review/system-analysis phrasing either →
scan
src/for.mdfiles and skill-shaped folders (a folder containing its ownSKILL.md). Present every match as a numbered pick-list and ask the user which one(s) to analyze — never a bare open-ended "what would you like me to analyze?" when there's something concrete to offer. Only fall back to the bare open-ended question if that scan comes back empty. Never guess, and never treat "nothing specified" as "nothing to do."
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.
- yesterday First seen · 114 lines · 1,449 tokens per session scan A d61297700501
analysis-output is a cursor rule published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 21d ago), licensed Apache-2.0. It adds 1,449 tokens to every session, about $0.0072 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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