agentdelta: Instructions file for Claude Code

CLAUDE.md

agentdelta CLAUDE.md is an instructions file for Claude Code from sandeep-alluru/agentdelta. It costs 830 tokens per session, scanned A, original, MIT.

Developer instructions for agentdelta, a tool that compares two AI-agent runs and finds where their behavior first meaningfully diverged. It records agent steps, compares their meaning, and can produce terminal, JSON or Markdown reports.

In plain words
What is it for?
Use it when developing agentdelta, recording or inspecting agent traces, comparing runs, running its command-line interface, or executing its tests and quality checks.
Why use it?
The guide gives coding agents the project architecture, setup commands and required checks needed to modify the tool consistently.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md.

This is sandeep-alluru/agentdelta's own configuration. It tells Claude Code how to work on agentdelta itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agentdelta configures →

Reuse

Borrowing it

Nothing to install: this file belongs to sandeep-alluru/agentdelta. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/sandeep-alluru/agentdelta/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/sandeep-alluru/agentdelta

Made for: Claude Code.

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README.md
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Per session 830 This file is loaded in full into every session.
When invoked 830 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00830 $0.00830
Opus 5 $0.00415 $0.00415
Sonnet 5 $0.00166 $0.00166
Haiku 4.5 $0.00083 $0.00083

Measured 6d ago against content hash bf0bfadf8619, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

agentdelta 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 6d 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.

CLAUDE.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

agentdelta — Developer Guide

agentdelta is a semantic diff engine for AI agent behavior. It records step-by-step agent traces as JSONL, embeds nodes with all-MiniLM-L6-v2, aligns two traces by cosine similarity, and detects the first semantic fork point.

Architecture

trace.py        Data model: NodeType, EdgeType, TraceNode, TraceEdge, AgentTrace
embed.py        sentence-transformers singleton + sliding-window alignment
diff.py         Fork detection → DiffResult, ForkPoint, StepDiff
instrument.py   LangChain callback (AgentdeltaCallback) + record() context manager
report.py       Rich terminal / JSON / Markdown output formatters
cli.py          Click CLI: `agentdelta diff` and `agentdelta inspect`

See ARCHITECTURE.md for full data flow and algorithm details.

Setup

git clone https://github.com/sandeep-alluru/agentdelta
cd agentdelta
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pre-commit install

Development commands

make test       # pytest with branch coverage (fails under 85%)
make lint       # ruff check + format check
make typecheck  # mypy --strict
make fmt        # ruff format (auto-fix)
make all        # lint + typecheck + test

Running tests

pytest                          # all 43 tests
pytest tests/test_diff.py -v    # single module
pytest -k "test_fork"           # by name

Key invariants

  • TraceNode.id is content-addressed (SHA-256[:16] of {node_type}:{content}). Same content = same ID across runs.
  • embed_trace() mutates nodes in-place and is idempotent — safe to call twice.
  • align_traces() uses greedy 1:1 matching, O(n·window). Default window=5.
  • fork_threshold=0.70: first aligned pair below this becomes the ForkPoint.
  • match_threshold=0.85: pairs above this are "match" (no change).
  • has_regression is True iff fork_point is not None.

Adding a new output format

  1. Add a to_<format>(result: DiffResult) -> str function in report.py
  2. Add the format name to the --format choice in cli.py
  3. Add tests in tests/test_report.py

Read the full file on GitHub · 85 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. 6d ago First seen · 85 lines · 830 tokens per session scan A bf0bfadf8619

Subscribe to this mod's changes

agentdelta CLAUDE.md is an instructions file published in the GitHub repository sandeep-alluru/agentdelta (0 stars, last pushed 19d ago), licensed MIT. It adds 830 tokens to every session, about $0.0042 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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