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 ralfyishere/rules-with-receipts --skill code-reconnaissancegit clone --depth 1 https://github.com/ralfyishere/rules-with-receiptsWrote 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/ralfyishere/rules-with-receipts/code-reconnaissance)<a href="https://agentmods.dev/skills/ralfyishere/rules-with-receipts/code-reconnaissance"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/code-reconnaissance.svg" alt="Measured on agentmods" 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 | $0.00101 | $0.01322 |
| Opus 5 | $0.00051 | $0.00661 |
| Sonnet 5 | $0.00020 | $0.00264 |
| Haiku 4.5 | $0.00010 | $0.00132 |
Grade A, and why
Code Reconnaissance 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 6d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reconnaissance
Purpose
Code written without reconnaissance is foreign tissue: it duplicates helpers that already exist, violates conventions the rest of the codebase follows, and breaks callers the author never saw. Fifteen minutes of directed reading turns a plausible change into a native one. This is the comprehension strategy that runs before any plan for changing existing code — live-state-truth says "read before you edit"; this skill says what to read and what to extract from it.
When to use this skill
- Before implementing anything in a codebase not written in this session.
- Before choosing where new code should live ("where does this belong?" is a recon question, not a taste question).
- Before editing any function/module whose callers you can't name.
- When the task says "do it the way the rest of the app does it" — explicitly or implicitly (it's always implicit).
When NOT to use this skill
- Greenfield code with no surrounding conventions to honor.
- Code you wrote or fully mapped earlier in this session and haven't been away from (see
memory-hygienefor when that expires). - Don't let recon become stalling: the checklist below is minutes of reading, not a full codebase audit. Depth follows
effort-calibration.
Operating procedure
1 — Locate the territory. Search by the feature's vocabulary (user-facing terms, route names, domain nouns) to find where this concern already lives. Entry points first: routes, CLI commands, main modules, exported APIs.
2 — Find an exemplar. Locate one existing implementation of the same kind of thing you're about to build (another endpoint, another validator, another migration). This is the single highest-value recon artifact — it answers naming, structure, error handling, and test placement in one read.
3 — Check for prior art before writing anything new. The helper you're about to write probably exists. Search for it by behavior, not just name (utils, shared libs, an existing dependency that does it). Writing a second formatDate is how codebases rot.
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.
- 6d ago First seen · 70 lines · 0 tokens per session scan A a3840889a65d
Code Reconnaissance is a skill published in the GitHub repository ralfyishere/rules-with-receipts (2 stars, last pushed 1mo ago), licensed MIT. It adds 101 tokens to every session and 1,322 once invoked, about $0.0005 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.
Other skills, from other repositories
happiness-skill
A Chinese-language guide to happiness based on reducing unmet wants, focusing on the present, and treating happiness as a trainable skill.
short-drama-storyboard
A workflow for turning a Chinese short-drama script and its visual facts into a shot-by-shot storyboard with frozen starting-frame prompts.
setup-matt-pocock-skills
A setup skill that configures engineering skills for a repository, including its issue tracker, labels, and documentation layout. A repository is the project folder managed by version control.
frontend-design
A design guide for building polished web interfaces such as pages, dashboards, forms, navigation, and reusable UI components. It covers HTML, CSS, JavaScript, and common frontend frameworks.
alterlab-cobrapy
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…
alterlab-depmap
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use when identifying cancer-specific genetic vulnerabilities, finding synthetic lethal interactions, checking whether a gene is essential in given cell lines, or…