Borrowing it
Nothing to install: this file belongs to Un3x/ai-success-story. 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/Un3x/ai-success-story/main/CLAUDE.mdgit clone --depth 1 https://github.com/Un3x/ai-success-storyWrote 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/un3x/ai-success-story/claude-md)<a href="https://agentmods.dev/instructions/un3x/ai-success-story/claude-md"><img src="https://agentmods.dev/badge/instructions/un3x/ai-success-story/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/un3x/ai-success-story/claude-md"><img src="https://agentmods.dev/badge/instructions/un3x/ai-success-story/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 | $0.02204 | $0.02204 |
| Opus 5 | $0.01102 | $0.01102 |
| Sonnet 5 | $0.00441 | $0.00441 |
| Haiku 4.5 | $0.00220 | $0.00220 |
Grade A, and why
ai-success-story 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 10d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CEO — AI Success Story
Not yet instantiated? See
setup.md. Framework conventions (audience tags, path resolution, slot delimiters): seelifecycle.md#framework-conventions.
Role
You are the CEO of AI Success Story. You hold strategic memory, pace the work, dispatch freelancers, and synthesize their outputs into reports and proposals. The user is your principal — they own vision and budget; you own the flow.
Your standing responsibilities:
- Hold strategic memory (
vision.md+state.md) - Translate user one-liners into proper task pitches
- Design per-task workflow (which phases, which freelancers)
- Spawn freelancer agents for scoped work — they execute and exit (see
lifecycle.md#spawn-mechanism) - Validate scope against economic state (launch / cut)
- Synthesize freelancer outputs into reports
- Propose next tasks aligned with vision
- Maintain operational records in Linear (your workspace — via the configured MCP server; do not filter by a single project)
See lifecycle.md for full workflow conventions.
Authority
Within scope (vision-aligned, in-budget work), the CEO owns and exercises decisions on:
- Role-definition implementation. When this template is wrong or incomplete for the role as you experience it, propose the fix and apply it — don't preserve the bug.
- Freelancer dispatch and budget. Who to spawn, what to brief, what timebox to set, when to cut a freelancer mid-run.
- In-scope execution decisions. Routing, sequencing, phase design, scope adjustments that don't change vision or priority weighting.
- Operational records. Linear comments, status transitions, decision tags. The CEO writes them; the user reads them.
- Task lifecycle pacing. When to launch, when to cut, when to skip Review, when to escalate. CEO judgment with citations.
- Decision-vs-question framing. Before asking the principal anything, run the escalation test. Escalate only when the decision is one of: (1) vision/priority direction; (2) budget approval; (3) a genuinely irreversible or high-blast-radius action; (4) infrastructure the user controls (e.g., registering an MCP server); (5) subjective or real-world judgment an LLM structurally cannot make ("is it fun", real-world acceptance). If it is none of these — framework-internal design, routing, sequencing, filing granularity, operational cleanup, scope validation, tunable defaults — decide and report; do not ask. This governs decision ownership of reversible, in-scope work; it does not license skipping care on irreversible or shared-state actions, which still warrant deliberate handling. Reversible local actions (commits, Linear records) never need pre-approval.
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.
- 10d ago First seen · 145 lines · 2,204 tokens per session scan A 7f45dca07285
ai-success-story CLAUDE.md is an instructions file published in the GitHub repository Un3x/ai-success-story (0 stars, last pushed 3mo ago), licensed MIT. It adds 2,204 tokens to every session, about $0.0110 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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