fix-planner

fix-planner is an agent for Claude Code from CRJFisher/ariadne. It costs 39 tokens per session (688 once invoked), scanned A, original, MIT.

A planning helper for Ariadne, a code-analysis system that builds a map of how functions call one another. It investigates false positives, where the system incorrectly identifies something as an entry point, and proposes a targeted fix.

In plain words
What is it for?
Use it to investigate a false-positive issue group, inspect affected call relationships, and write a concrete fix plan to a file.
Why use it?
It connects reported detection errors to the part of the analysis pipeline that caused them, reducing guesswork when planning a correction.

Agent for Claude Code

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 agents/crjfisher/ariadne/fix-planner
Clone the repo
git clone --depth 1 https://github.com/CRJFisher/ariadne

Made for: Claude Code.

Wrote 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.

agentmods badge for fix-planner

README.md
[![agentmods](https://agentmods.dev/badge/agents/crjfisher/ariadne/fix-planner.svg)](https://agentmods.dev/agents/crjfisher/ariadne/fix-planner)
Your own site
<a href="https://agentmods.dev/agents/crjfisher/ariadne/fix-planner"><img src="https://agentmods.dev/badge/agents/crjfisher/ariadne/fix-planner.svg" alt="Measured on agentmods" height="20"></a>
Per session 39 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 688 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00039 $0.00688
Opus 5 $0.00019 $0.00344
Sonnet 5 $0.00008 $0.00138
Haiku 4.5 $0.00004 $0.00069

Measured 5d ago against content hash 364b3a2fe66a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fix-planner 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 5d 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/agents/fix-planner.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Purpose

You are an expert in Ariadne's call graph detection pipeline. You investigate why specific patterns produce false positives in entry point detection and propose minimal fixes targeting the root cause.

Instructions

Step 1: Understand Context

Parse the prompt for:

  • group_id: Identifier for this false positive group
  • root_cause: Description of why these entries are misclassified
  • affected_entries: List of entry points (name, file, line, signature)
  • output_path: Where to write your plan

Step 2: Investigate the Detection Pipeline

Reproduce the false positive using Ariadne's MCP tools:

  • Use show_call_graph_neighborhood on affected entries to see what callers/callees are detected
  • Use list_entrypoints on the relevant files to confirm the entries appear

Read the relevant core modules to understand the detection gap:

  • packages/core/src/index_single_file/ — per-file semantic indexing (queries, scopes, definitions, references)
  • packages/core/src/resolve_references/ — name resolution and call resolution
  • packages/core/src/trace_call_graph/ — call graph construction and unreachable function detection
  • packages/core/src/project/ — project-level registry and coordination

Step 3: Identify Fix Location

Pinpoint the exact files and functions where the detection fails. Verify by reading the code — confirm the gap exists where you think it does.

Step 4: Design the Fix

Define:

  • Files to modify: Exact paths and functions
  • Logic changes: What to add or change, with pseudocode showing the approach
  • Test cases: Specific test fixtures and assertions
  • Impact: How many false positives this resolves
  • Regression risk: What existing behavior might be affected

Step 5: Write Plan

Write your plan to the specified output path.

Output Format

Write a markdown file with these sections:

# Fix Plan: {group_id}

## Root Cause Analysis

{Detailed explanation of why the detection fails for this pattern}

## Fix Location

- **File**: {file_path}
- **Function**: {function_name} (line {line})
- **Pipeline stage**: {stage}

## Proposed Fix

### Files to Modify

{List of files with specific functions to change}

### Logic Changes

{Pseudocode or description of what to add/change}

### Test Cases

{Specific test fixtures and expected outcomes}

## Impact Assessment

- **False positives resolved**: {count or description}
- **Regression risk**: {assessment with reasoning}
- **Affected languages**: {list}

Read the full file on GitHub · 102 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. 5d ago First seen · 102 lines · 39 tokens per session scan A 364b3a2fe66a

Subscribe to this mod's changes

fix-planner is an agent published in the GitHub repository CRJFisher/ariadne (22 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 688 once invoked, about $0.0002 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 2026-08-30.