Borrowing it
Nothing to install: this file belongs to Olawoyin007/intentKeeper. 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/Olawoyin007/intentKeeper/main/CLAUDE.mdgit clone --depth 1 https://github.com/Olawoyin007/intentKeeperWrote 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/olawoyin007/intentkeeper/claude-md)<a href="https://agentmods.dev/instructions/olawoyin007/intentkeeper/claude-md"><img src="https://agentmods.dev/badge/instructions/olawoyin007/intentkeeper/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/olawoyin007/intentkeeper/claude-md"><img src="https://agentmods.dev/badge/instructions/olawoyin007/intentkeeper/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 | $0.02200 | $0.02200 |
| Opus 5.5 | $0.00880 | $0.00880 |
| Sonnet 5.5 | $0.00440 | $0.00440 |
| Haiku 4.5 | $0.00220 | $0.00220 |
Grade A, and why
intentKeeper 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 2d 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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
Before any PR: read
MERGE_CHECKLIST.mdBefore any release: runpython3 scripts/check_version.py
Project Overview
intentKeeper is a local-first content filter that classifies social media content by its underlying intent - ragebait, fearmongering, hype, or genuine insight. It runs entirely on local hardware via Ollama. No external API calls, no telemetry, no cloud.
Core philosophy: "The content isn't the problem. The intent behind it is."
Development Commands
# Install with dev dependencies
pip install -e ".[dev]"
# Start the classification server
intentkeeper-server
# or
uvicorn server.api:app --reload --port 8420
# Run unit tests (93 tests, Ollama not required - all mocked)
pytest tests/
# Run tests with coverage
pytest tests/ --cov=server
# Run the classification eval (requires Ollama running)
python eval/run_eval.py
python eval/run_eval.py --verbose # show every item
python eval/run_eval.py --filter ragebait # one intent only
# Version consistency check
python3 scripts/check_version.py
# Linting and formatting
ruff check server/
ruff check --fix server/
black server/
Required Environment Variables
Configure in .env (see .env.example):
Required:
OLLAMA_HOST- Ollama server URL (default:http://localhost:11434)OLLAMA_MODEL- Model name (default:llama3.1:8b- the model the 96% baseline is measured on)
Optional:
OLLAMA_TEMPERATURE- LLM temperature (default:0.1)OLLAMA_SEED- Pin sampling seed for reproducible output (unset = non-deterministic)OLLAMA_VISION_MODEL- Vision model for image/thumbnail analysis (e.g.moondream,llava:7b); when unset, image analysis is skipped and only text is classifiedINTENTKEEPER_HOST- Server bind address (default:127.0.0.1)INTENTKEEPER_PORT- Server port (default:8420)MANIPULATION_THRESHOLD- Score threshold for treatments (default:0.6)CACHE_TTL- Cache time-to-live in seconds (default:300)CACHE_MAX_SIZE- Max LRU cache entries (default:1000)DEBUG- Enable debug logging (default:false)
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.
- 2d ago Changed · +1 lines · +17 tokens per session 7a2ffdf0965c
- 1mo ago Changed · +11 tokens per session 95bb01faef94
- 1mo ago Changed · +14 lines · +219 tokens per session 208e4b2ae926
- 2mo ago First seen · 203 lines · 1,953 tokens per session scan A 0c4a2d1e93d4
intentKeeper CLAUDE.md is an instructions file published in the GitHub repository Olawoyin007/intentKeeper (11 stars, last pushed 3d ago), licensed MIT. It adds 2,200 tokens to every session, about $0.0088 per session on Opus 5.5. 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 2026-08-30.
intentKeeper CLAUDE.md compared with instructions from other repositories
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|---|---|---|---|---|---|---|---|
| intentKeeper CLAUDE.mdOlawoyin007/intentKeeper | Instructions file | 11 | 3d ago | MIT | A | 2,200 | Claude Code |
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