SymbolForge Code Intelligence
Give your AI coding agent IDE-grade symbol lookup, dependency analysis, and surgical edits — smaller diffs, fewer broken call sites, far less token waste.
Free
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentsSymbolForge Code Intelligence
Example session with this skill installed
I need to change OrderService.cancel in src/services/order.py so it validates order state before refunding, but that method is called from many places in this repo and I don't know all of them. Can you locate the exact symbol, show me every call site plus any implementations of the underlying gateway interface, then replace just the method body without touching the rest of the file? Confirm nothing else depends on the old behavior, re-run diagnostics on the edited file, and run the order-related tests. It's a Python service — index it first if needed.
- Read your context and instructions
- Compiled the symbolforge code intelligence
Summary
Refactored OrderService.cancel to enforce state validation before refunding. Used symbol lookup, call-site analysis, surgical body replacement, and post-edit diagnostics — zero changes outside the target symbol.
Impact analysis
| Metric | Before | After |
|---|---|---|
| Files read to locate logic | 6 files, ~2,140 tokens | 2 signature queries, ~310 tokens |
| Lines changed | 74 (whole-file rewrite) | 11 (symbol body only) |
| Call sites verified | 0 (text grep only) | 7 via find_callers + 2 impls |
| Post-edit checks | none | 0 diagnostics, 18 tests passed |
Edits applied
replace_symbol_bodyonsrc/services/order.py::OrderService.cancel(state guard + refund ordering)insert_before_symboladded theInvalidStateErrorimportrun_diagnostics+ targeted pytest ontests/services/test_order.py
Next steps
- Run the full CI suite to confirm the 7 call sites in
api/views.pyandjobs/still pass - Deprecate the legacy
cancel_order()helper — one caller remains, flagged by find_usages - Apply the same locate-analyze-edit-validate loop to the
PaymentGatewaymigration next
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About this skill
Stop forcing your AI coding agent to guess where functions are defined or waste tokens reading 1,000-line files just to change a single guard clause. Most agents fail on real-world repositories because they lack the structural awareness of a human developer. This skill provides a symbol-aware intelligence layer that lets agents navigate, analyze, and edit codebases using language-level constructs instead of raw text search.
What it does
- Symbol navigation allows agents to query for declarations, classes, methods, and interfaces by name rather than searching for text fragments.
- Relational analysis identifies all call sites, interface implementations, and dependencies across the entire project graph.
- Surgical editing replaces specific symbol bodies or inserts code around declarations to avoid risky whole-file rewrites.
- Impact validation runs syntax checks and semantic diagnostics immediately after edits to catch errors before they reach human review.
- Context optimization fetches only method signatures or specific bodies, significantly reducing token consumption in large repositories.
How it works
- Orient using
project_mapto list packages and top-level symbols. - Locate specific code by querying for definitions with
find_symbol. - Analyze the impact of a change by running
find_callersorfind_usages. - Mutate code precisely using
replace_symbol_bodyorinsert_after_symbol. - Verify the change with
run_diagnosticsto ensure project integrity.
Frameworks & tools
This skill integrates via the Model Context Protocol (MCP) or HTTP and supports backend engines like Language Server Protocol (LSP) or IDE-specific analyzers. It handles Python, TypeScript, Go, Java, Rust, and several other mainstream languages.
Why this beats prompting it yourself
Standard prompts rely on the LLM to grep through file contents, which leads to hallucinated paths and broken call sites. This skill gives the agent actual IDE-grade primitives, ensuring it only interacts with valid code structures and never misses a reference due to naming collisions.
Use cases
- Refactoring legacy codebases by identifying and updating every call site of a deprecated method.
- Surgical bug fixing in high-density files where whole-file replacement is too expensive or error-prone.
- Automated migrations of HTTP routes or ORM models across multi-module projects.
- Onboarding agents to large, unfamiliar repositories by providing a structural map.
Known limitations
Indexing speed and feature depth depend on the chosen language backend (LSP vs IDE). Large-scale refactors may require incremental index rebuilds to maintain accuracy after many changes.
Semantic accuracy is backend-specific: uncommon languages or misconfigured projects may fall back to plain text search, and very large repos index slowly on first run. Little benefit on single small scripts or projects with no structure yet.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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Requires
Python
uv (Python tool runner)Permissions
File Scopes
Terminal, file read/write, and one-time network access are needed to install and run the local symbol indexer and apply edits to your repository's source files.