Borrowing it
Nothing to install: this file belongs to dungnotnull/indie-game-match-history-database-agent-skill. 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/dungnotnull/indie-game-match-history-database-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/indie-game-match-history-database-agent-skillWrote 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/instructions/dungnotnull/indie-game-match-history-database-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/indie-game-match-history-database-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/indie-game-match-history-database-agent-skill/claude-md/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/instructions/dungnotnull/indie-game-match-history-database-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/indie-game-match-history-database-agent-skill/claude-md.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 | $0.01663 | $0.01663 |
| Opus 5 | $0.00831 | $0.00831 |
| Sonnet 5 | $0.00333 | $0.00333 |
| Haiku 4.5 | $0.00166 | $0.00166 |
Grade A, and why
indie-game-match-history-database-agent-skill CLAUDE.md 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 12d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md - Skill 263: indie-game-match-history-database
Skill Identity
- Skill Name:
indie-game-match-history-database - Tagline: Historical Match Database Architecture & Engine for Indie Multiplayer Games - Indie Game Match-History Data Engineering analysis & decision-support harness backed by a runnable, tested Python engine.
- Current Phase: Phase 5 - Integration & Polish (PRODUCTION READY v1.1.0)
- Folder:
D:\972026\263-indie-game-match-history-database\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Indie Game Match-History Data Engineering. It gathers authoritative real-time and reference data, applies recognized domain methods, cross-references academic research, and delivers actionable outputs that are fully evidenced, risk/limitation-disclosed, and traceable to authoritative sources - continuously self-improving through an automated knowledge crawl pipeline.
Crucially, the skill is grounded in a real, tested engine package
(indie_match_history/) implementing the data model, ELO/Glicko-2 ratings,
tiered storage, leaderboards, replay blobs, and GDPR/COPPA retention it
analyses - so recommendations reference runnable code, not prose.
Harness Flow Summary
/indie-game-match-history-database invoked
|
+-- Step 1: sub-gather-requirements -> clarify object, scope, timeframe, inputs, audience, language
+-- Step 2: sub-evidence-collector -> fetch authoritative real-time + reference + academic data
+-- Step 3: sub-core-analysis -> design storage: model, tiering, queries, scale, replay, privacy
+-- Step 4: sub-knowledge-updater -> query SECOND-KNOWLEDGE-BRAIN.md; surface tiered citations
+-- Step 5: sub-advisor -> synthesize risk-disclosed conclusion + evidence chain
+-- Step 6: main (quality gate) -> verify evidence hierarchy, disclosure, output polish
See skills/main.md for the full protocol, Pre-Flight language detection,
10 quality gates (U1-U6 + G1-G4), graceful degradation levels 0-4, the error
recovery table, and the mandatory output template.
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.
- 12d ago First seen · 154 lines · 1,663 tokens per session scan A 74952315141d
indie-game-match-history-database-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/indie-game-match-history-database-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,663 tokens to every session, about $0.0083 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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