recap AGENTS.md

A project instruction document for RECAP, a Chrome extension and local Python service that indexes pages you read and lets you search them in natural language.

In plain words
What is it for?
Use it when installing, running, testing, or changing the RECAP codebase.
Why use it?
It explains the project's setup, architecture, privacy model, and required development commands in one place.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/atliq/recap/agents-md
Clone the repo
git clone --depth 1 https://github.com/atliq/recap

Made for: Codex, OpenCode.

Per session 2,577 This file is loaded in full into every session.
When invoked 2,577 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.02577 $0.02577
Opus 5 $0.01288 $0.01288
Sonnet 5 $0.00515 $0.00515
Haiku 4.5 $0.00258 $0.00258

Measured yesterday against content hash af54dce777a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

recap AGENTS.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 yesterday.

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.

AGENTS.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Guidance for AI coding agents - and humans - working in the RECAP repository. This is the single source of truth for how to set up, run, test, and change this project; tool-specific files (CLAUDE.md, etc.) import it. The human-facing overview is in README.md; the security policy is in SECURITY.md.

What RECAP is

RECAP is a local-first Chrome extension (Manifest V3) plus a local Python (FastAPI) RAG backend. It passively indexes the pages a user actually reads and lets them search their browsing history in natural language. Everything runs on the user's machine; page content only leaves the device when the user asks a question - and only the retrieved snippets, sent to the LLM provider the user configured.

Setup & run

Requires Docker (recommended) or Python 3.11+, plus Chrome. Three paths, all of which install spaCy + the en_core_web_sm model (so the knowledge graph works as soon as you opt in):

# Docker - recommended: reproducible, one command. Serves http://127.0.0.1:8000 (loopback only).
docker compose up --build

# uv - fast, reproducible local dev. `uv sync` installs deps + the spaCy model.
uv sync && uv run python main.py

# pip - simple fallback.
pip install -r requirements.txt && python main.py

pyproject.toml (+ uv.lock) is the canonical dependency set for uv/Docker; requirements.txt mirrors it for the pip path - keep the two in sync when changing versions. torch is pinned to the CPU wheel (tool.uv.sources) so images stay small and it runs anywhere; GPU users override it.

  • Copy .env.example.env and set at least one LLM key (or point at a local Ollama). See LLM & embeddings below.
  • Docker + a host Ollama: the container can't see the host's localhost - set LLM_BASE_URL/EMBEDDING_BASE_URL to http://host.docker.internal:11434/v1 (compose already maps host.docker.internal).
  • Load the extension: chrome://extensions → enable Developer modeLoad unpacked → select the extension/ folder.
  • Knowledge graph: off by default - retrieval runs BM25 + dense vectors only. Opt in from the extension Options page toggle (calls POST /settings/kg; takes effect immediately, persisted in DB meta so it survives backend restarts and overrides .env), or set ENABLE_KG=true in .env for a config-only setup. The master switch gates both ingestion NER and the KG retrieval leg; a request's use_kg can never override it on. After enabling, backfill already-indexed pages without re-browsing via POST /maintenance/rebuild_kg (re-runs NER over stored text; SQLite is the source of truth). Entities need spaCy (installed by default).

Read the full file on GitHub · 118 lines

Changes

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.

  1. yesterday First seen · 118 lines · 2,577 tokens per session scan A af54dce777a5

Subscribe to this mod's changes

recap AGENTS.md is an instructions file published in the GitHub repository atliq/recap (10 stars, last pushed 28d ago), licensed MIT. It adds 2,577 tokens to every session, about $0.0129 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.

Related

Other instructions, from other repositories