Borrowing it
Nothing to install: this file belongs to anthonywang-sg/Personal-Travel-Agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/anthonywang-sg/Personal-Travel-Agent/main/.agents/skills/sanitizing-repo-for-open-source/SKILL.mdgit clone --depth 1 https://github.com/anthonywang-sg/Personal-Travel-AgentWrote 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/anthonywang-sg/personal-travel-agent/sanitizing-repo-for-open-source)<a href="https://agentmods.dev/skills/anthonywang-sg/personal-travel-agent/sanitizing-repo-for-open-source"><img src="https://agentmods.dev/badge/skills/anthonywang-sg/personal-travel-agent/sanitizing-repo-for-open-source/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/anthonywang-sg/personal-travel-agent/sanitizing-repo-for-open-source"><img src="https://agentmods.dev/badge/skills/anthonywang-sg/personal-travel-agent/sanitizing-repo-for-open-source.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 | $0.00054 | $0.02458 |
| Opus 5 | $0.00027 | $0.01229 |
| Sonnet 5 | $0.00011 | $0.00492 |
| Haiku 4.5 | $0.00005 | $0.00246 |
Grade A, and why
sanitizing-repo-for-open-source 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 11d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sanitizing Repo for Open Source
Overview
Repository sanitation is the defense-in-depth process of identifying, scrubbing, and preventing the leakage of secrets, private credentials, internal paths, and proprietary configurations before making a codebase public.
A single leaked API key, database connection string, or internal project credential in git history can compromise cloud accounts and downstream users. Deleting a secret in a subsequent commit does NOT remove it from git history.
When to Use
- Preparing a private repository for public open-source release on GitHub/GitLab.
- Auditing an existing codebase for hardcoded API keys (
GEMINI_API_KEY, OpenAI, AWS, GCP service account JSONs, database passwords). - Setting up or updating
.gitignoreand.env.exampletemplates. - Reviewing file and directory taxonomy to determine what should vs. should not be published.
- Verifying git commit history for legacy secret commits before publishing.
When NOT to Use
- Routine day-to-day feature development when not changing environment configs or prepping for release.
- For application runtime authentication logic (use specific cloud auth skills instead).
What to Open-Source vs. What to Exclude
| Category | Must Include (Publish) | Must Exclude (Never Publish) | Conditional / Needs Sanitization |
|---|---|---|---|
| Source Code | Core source files (src/, lib/), package entry points, domain models |
Hardcoded API keys, private tokens, internal staging endpoints | Custom tool adapters (sanitize private API endpoints) |
| Configuration | Config schemas (pydantic-settings), .env.example with dummy placeholders |
Live .env, .env.local, .env.production, cloud credential files |
docker-compose.yml (use standard generic default passwords) |
| Tests & Fixtures | Unit tests (tests/), synthetic mock fixtures, benchmark schemas |
Production database dumps, real customer PII, real flight/booking tokens | Integration test credentials (use environment variables/mocks) |
| Documentation | README.md, LICENSE, CONTRIBUTING.md, SECURITY.md, architecture docs |
Internal employee notes, private company URLs, unreleased roadmap items | Implementation plans (strip internal employee emails/confidential info) |
| State & Storage | SQL schema definitions (schema.sql), migration scripts |
Live database volumes (pgdata/, *.db, *.sqlite), Vector DB store dumps |
Pre-packaged reference seed data (must be synthetic/public domain) |
| Build & Runtime | pyproject.toml, Dockerfile, package.json, CI workflow templates |
Virtualenvs (.venv/, env/), build output (dist/, build/), .coverage |
CI secrets (use GitHub Actions Secrets, never commit tokens) |
| IDE & OS Metadata | Generic recommended extensions (.vscode/extensions.json) |
.DS_Store, Thumbs.db, .idea/, .vscode/settings.json (if user-specific) |
Custom launch configs (strip hardcoded environment variables) |
| Agent / LLM Data | Skill files (.agents/skills/), public system prompts |
Private conversation transcripts, memory store dumps, user session logs | Benchmark logs (ensure queries are synthetic) |
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.
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.
- 11d ago First seen · 249 lines · 54 tokens per session scan A bea065c440ae
sanitizing-repo-for-open-source is a skill published in the GitHub repository anthonywang-sg/Personal-Travel-Agent (0 stars, last pushed 20d ago), licensed MIT. It adds 54 tokens to every session and 2,458 once invoked, about $0.0003 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…