Air-gapped source code examiner
/
Code exam claim search parameter issue
Shared by Andrew
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I'm testing the new code_exam py. I created a pseudo claim to represent what we were looking for in Moltbook:

C:\work\ai_code_exam\code_exam_py_files_3>type ..\moltbook_pseudo_claim.txt
An internet forum designed exclusively for artificial intelligence agents.
The platform, which imitates the format of Reddit, claims to restrict posting and interaction privileges to verified AI agents, primarily those running on the OpenClaw (formerly Moltbot) software, while human users are only permitted to observe.
Only AI agents, as authenticated by their owner's "claim" tweet, can create posts, comment, or vote, while human users are restricted to viewing content. According to the site's policy, humans are "welcome to observe."

I then did:

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --claim-search ..\moltbook_pseudo_claim.txt --min-terms 7
Loaded existing index: 133 files
Command: code_exam.py --index-path ..\.moltbook_src --claim-search ..\moltbook_pseudo_claim.txt --min-terms 7
Claim source: command line
Claim text (28 chars):
  ..\moltbook_pseudo_claim.txt
Extracting search terms from patent claim...
  +---------------------------------------------------------------+
  |               WARNING: EXTERNAL NETWORK REQUEST               |
  |                  CLAIM TEXT WILL BE SENT TO:                  |
  |  https://api.anthropic.com/v1/messages                        |
  |  Model: claude-sonnet-4-20250514                              |
  |  Claim text: 28 chars                                
.....

Hmm, 28 chars doesn't sound right. And it wasn't:

 TIGHT SEARCH -- literal claim language
========================================================================
  Terms: /attention|attend/;mechanism;/key|query|value/;/weight|score/;/matrix|matrices/;/transform|project/;/multi.head|multihead/;/neural|network/;/layer|block/;/embed|encoding/;/position|positional/
....

Sounds like --claim-search is using some tiny built-in claim text?

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Feb 16
Let me look at the relevant code to find the bug.


Ran 4 commands, viewed 3 files, searched memory
Ran 4 commands, viewed 3 files, searched memory
Found it. The bug is exactly what you suspect — --claim-search is treating the file path as literal claim text.

Here's the flow:

You ran: --claim-search ..\moltbook_pseudo_claim.txt
argparse stores args.claim_search = r"..\moltbook_pseudo_claim.txt"
In do_claim_search, the code checks: does it start with @? No. So it falls into the else branch:
python
   else:
       claim_text = text          # <- the filename string itself!
       source_label = "command line"
len(r"..\moltbook_pseudo_claim.txt") = 28 chars ← there's your 28
The LLM receives the filename as "claim text," hallucinates something about attention/transformers, and off it goes.
The existing code only supports two ways to read from a file:

--claim-search @..\moltbook_pseudo_claim.txt (the @ prefix syntax)
--claim-file ..\moltbook_pseudo_claim.txt (the dedicated flag)
Suggested fix — auto-detect file paths in --claim-search so bare filenames just work:

python
elif getattr(args, 'claim_search', None):
    text = args.claim_search
    # Support @filename syntax: --claim-search @patent_claim.txt
    if text.startswith('@'):
        filepath = text[1:].strip()
    elif os.path.isfile(text):          # <-- NEW: auto-detect bare file paths
        filepath = text
    else:
        filepath = None

    if filepath:
        try:
            with open(filepath, 'r', encoding='utf-8') as f:
                claim_text = f.read()
        except FileNotFoundError:
            print(f"Error: claim file not found: {filepath}")
            return
        except Exception as e:
            print(f"Error reading claim file: {e}")
            return
        source_label = filepath
    else:
        claim_text = text
        source_label = "command line"
This way --claim-search ..\moltbook_pseudo_claim.txt sees it's a real file and reads it, while --claim-search "A method for computing attention weights..." still works as inline text. The @ prefix remains supported too for disambiguation if someone has a file whose name looks like claim text (unlikely but covered).

For now, the workaround is either of:

--claim-file ..\moltbook_pseudo_claim.txt
--claim-search @..\moltbook_pseudo_claim.txt
Doh! I forgot @ . No need for you to fix now, though I guess it's a common enough mistake that users will make. Add to TODO list.

Feb 16

Ran 3 commands, viewed 2 files, searched memory
Ran 3 commands, viewed 2 files, searched memory
Now I have the full TODO. This fits best in the CLI / Interactive Parity & UX section as a UX issue. The next available number is 151.


Copy TODO to working directory for editing
Copy TODO to working directory for editing
The filesystem tools are having connectivity issues right now, so I can't directly edit the file. Here's the item to add to the CLI / Interactive Parity & UX section:

| 151 | **`--claim-search` bare filename detection**: `--claim-search file.txt` silently treats the filename as claim text (28 chars → LLM hallucinates transformer terms). User must remember `@` prefix or use `--claim-file`. Fix: add `os.path.isfile(text)` check before falling through to literal-text branch. Low risk — real claim text won't match a filesystem path. | **new, UX bug** |
The fix in do_claim_search is a 2-line addition to the existing elif getattr(args, 'claim_search', None): block — insert between the @ check and the else:

python
    elif os.path.isfile(text):   # bare filename without @ prefix
        filepath = text
I'd prefer you to make change to ce_multisect.py when the connectivity issues go away. Me making changes seems to create confusion. And making the change better than adding to TODO md.

A similar cosmetic change when connectivity restored: when claim-search has no function hits, put a Tip at the bottom suggesting a reduced --min-terms, or a ... (whatever --cmdline is for specifying required terms for claim-search, if there is one; I see --terms which help says is for multisect-analyze; and --require INDICES which sounds like it would only work for --multisect-search ?)

BTW, I retried semantic searching, since we've been doing --skip-semantic for a while. It worked surprisingly poorly. I know that (and performance) are why we switched away, but I was surprised by how useless the semantic hits were. I created an index of our py code. The ChromaDB was over 5 MB. With that size, relative to how little code we have (less than 0.5 MB), it should be able to do something. But searching the code for "important" only got the literal hits e.g. in hotspot code, and searching for "major" didn't get hotspot code. Oh well, I'm glad we're not relying on this, but still doesn't seem right. Do RAGs have this problem with Chroma?

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Feb 16
A few things to untangle here:

The code changes — happy to make both (bare filename detection + no-function-hits tip) once connectivity is back. I don't have ce_multisect.py in the project files though — the newest I see is code_search_67.py. Can you upload the current ce_multisect.py (or whatever the claim-search handling has been refactored into)?

--require with --claim-search — from the argparse definitions, --require INDICES says "1-based term indices that MUST be present in every match, in addition to --min-terms threshold." The help text doesn't restrict it to --multisect-search only, and since --claim-search ultimately generates terms and feeds them to the same multisect_search engine, it should work. But the user experience is awkward — you'd have to run once, see what terms the LLM generated, then re-run with --require 1,3 knowing which indices to pin. So for the Tip, I'd suggest focusing on --min-terms (lowering the threshold) since that's immediately actionable without a two-pass workflow.

ChromaDB semantic quality — what you're seeing is unfortunately normal, and it's the core reason many people are disappointed with naive RAG setups. The problem isn't ChromaDB specifically, it's the embedding model. The default (typically all-MiniLM-L6-v2 or similar) is trained on natural language similarity — it knows "king" is near "queen" but it has no concept that do_hotspots() is semantically related to "important." It's essentially doing fancy string matching on tokens it saw during training, not reasoning about what code does.

The 5MB index size is expected — each chunk becomes a 384- or 768-dimensional float32 vector, so the embeddings alone dwarf the source text. Size doesn't correlate with quality.

The fundamental issue: for code, you'd need either a code-specific embedding model (CodeBERT, StarCoder embeddings, UniXcoder) that was trained on code semantics, or a two-stage approach where you first have an LLM describe what each function does in natural language, embed those descriptions, and then search against those. That second approach is essentially what your #135 (in-situ AI summarization) would enable — and it would make semantic search dramatically better because you'd be comparing natural language to natural language.

But for an air-gapped system with a 7B local model, generating descriptions for every function at index time is the performance wall you already identified in #149. It's a real chicken-and-egg problem. The pragmatic call you already made — lean on keyword/structural search and skip semantic — is the right one for now.

I'll upload to Project later. Right now still going through testing. Likely we will want to have complete set of py source-code files in the Project. Unfortunately it won't accept a zip file. Anyway, hold off on any changes, let's just discuss issues first.

I'm about to test --claim-analyze and --multisect-analyze. What --help says doesn't sound right to me:

  --analyze FUNCTION    Extract function and send to LLM for analysis (e.g.,
                        --analyze tls_connect)
  --claim-analyze FUNCTION
                        Extract function and analyze for patent claim
  --multisect-analyze FUNCTION
                        Extract function and analyze with multi-term focus

Yes, --analyze takes a function name. But --claim-analyze should take the text of a claim (including possibly in a @file), and --multisect-analyze should take a semicolon-delimiter list of terms. I'm pretty sure they did work that way last time I tested, and I probably didn't verify the help screen. Can you confirm (even from slightly old version of code_search py in the Project, before decomposition into small modules)?

Right now, when I tried to do --claim-analyze @file, I got dropped into interactive mode:

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --claim-analyze @..\moltbook_pseudo_claim.txt
Loaded existing index: 133 files
Code Search Interactive Mode
Type /help for commands, or just type a search query
search>

And same thing with --multisect-analyze

--claim-analyze and --multisect-analyze should be just like --claim-search and --multisect-search, except that after doing the search, they should automatically take the highest-ranking function hit (assuming there is one), extract that, and then pass that to the LLM for analysis, along with the other info in CLAIM_EXTRACTION_PROMPT (I think).

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Feb 16

Searched memory
Searched memory
I can confirm: --claim-analyze and --multisect-analyze do not exist in any of the code versions I have in the Project (code_search_63 through code_search_67, or code_analyzer_v3_11). They must have been added during your decomposition into code_exam.py / ce_multisect.py, which I don't have yet.

So I can't verify the actual implementation, but I can tell you what the symptoms mean:

Dropping into interactive mode is the classic argparse "unrecognized argument" behavior — when argparse doesn't match --claim-analyze, it doesn't error out (probably because you're using parse_known_args or similar), the value gets silently ignored, no CLI action triggers, and the code falls through to interactive mode. You can verify by checking whether args.claim_analyze is None at dispatch time.

The help text problem (--claim-analyze FUNCTION) confirms the argparse definition was likely copy-pasted from --analyze FUNCTION without changing the metavar. It should be:

python
parser.add_argument(
    '--claim-analyze',
    metavar='CLAIM_TEXT',      # not FUNCTION
    help='Run claim search, then extract and analyze top function hit. '
         'Use @filename to read from file.'
)
parser.add_argument(
    '--multisect-analyze',
    metavar='TERMS',           # not FUNCTION
    help='Run multisect search, then extract and analyze top function hit. '
         'Semicolon-separated terms, same syntax as --multisect-search.'
)
```

**On --require with --claim-search** — you're right, `--require` technically works with claim-search since it feeds into the same multisect engine. But as I mentioned, it's a two-pass workflow (run once to see term indices, then re-run with `--require`). So for the "no function hits" Tip, sticking with `--min-terms` is the right call. Something like:
```
Tip: No function-level matches. Try lowering --min-terms (currently N)
     to find files/folders with partial term coverage.
Upload the current .py files when you're ready and I'll make both fixes (bare filename detection + the Tip) in the actual source.

Yikes, if claim_analyze and multisect_analyze are not in what you see, that's very bad news. These were implemented in analyze_bridge.py, and called earlier analyze functionality in code_analyzer_v3_11.py . Can you look through the transcript from when module decomposition was done to confirm whether those two files were factored in?

Feb 16

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Searched memory
OK, I found the full story in the transcripts. Here's the good news and the concerning part:

analyze_bridge.py was absolutely created and tested. The conversation from Feb 12 ("Connecting extract and analyze pipeline with command variants") shows the full implementation: do_analyze, do_claim_analyze, do_multisect_analyze, dispatch_analyze, add_analyze_args, AnalysisLLM, SimpleMasker, prompt builders — the works. It was wired into code_search_65 via three edits (import at top, argparse additions, dispatch hook).

It was explicitly planned for inclusion in the decomposition. The refactoring conversation (Feb 16) lists:

ce_analyze.py — Current analyze_bridge.py content: AnalysisLLM, SimpleMasker, prompt builders, do_analyze, do_claim_analyze, do_multisect_analyze. Future: absorb CodeAnalyzer, UniversalCodeParser from code_analyzer_v3_11.py. The LLM integration layer.

And the final line count audit showed ce_analyze.py was created and copied to the output alongside all the other modules.

However — the transcript also reveals something important about how --claim-analyze and --multisect-analyze were originally defined. In the analyze_bridge version, --claim-analyze takes a FUNCTION name (not claim text), and expects the claim text to come separately via --claim-text or --claim-file. Similarly --multisect-analyze takes a FUNCTION name and expects --terms "t1;t2;t3" separately. So the original design was:

--claim-analyze tls_connect --claim-file patent.txt
--multisect-analyze process_data --terms "alloc;free;buffer"
That's the "manual" workflow — user already knows which function to analyze. Your current vision (claim-search → auto-pick top hit → analyze) is the more powerful integrated pipeline, but it's a different design than what was originally implemented. The original do_claim_analyze resolves a user-specified function name, extracts it, builds a claim-focused prompt, and sends to LLM. It does NOT run claim-search first.

So you have two things to sort out:

Was ce_analyze.py actually included in your working copy? It was created in the refactoring session and copied to outputs. If it's missing from your code_exam_py_files_3 directory, it may not have been downloaded/copied over.
The design question: Do you want --claim-analyze to mean "I already know the function, analyze it against this claim" (original design), or "search for the claim, auto-pick top function hit, then analyze" (your current intent)? The latter is more powerful but is essentially a new feature built on top of the original.
I do have ce_analyze.py . It's very small, and it reflects the help text and your explanation.

However, the analyze_bridge.py I have clearly shows my "vision" was already implemented:

def do_claim_analyze(index, args):
    """Handle --claim-analyze CLAIM: extract terms, search, then analyze against claim.
    Pipeline:
      1. Read claim text (inline, @file, or --claim-text/--claim-file)
      2. Extract search terms from claim (LLM call #1, same as --claim-search)
      3. Run multisect search with TIGHT terms
      4. Pick best 1-2 function matches (or fall through to file)
      5. Analyze hits against the original claim text (LLM call #2)
    """

Similarly:

def do_multisect_analyze(index, args):
    """Handle --multisect-analyze TERMS: search for functions, then analyze best hits.
    Runs a function-level-only multisect search internally, picks the top 1-2
    function matches, extracts their source, and sends through the LLM pipeline.
    When two functions are found and their combined size is manageable, they're
    sent in a single LLM call so the model can see relationships between them.
    """

Obviously, I should have tested claim-analyze and multisect-analyze before signing-off on the module refactoring. But then, this also ought to have been caught by the existing test code.

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Feb 16
That's a significant regression. The full pipeline in analyze_bridge.py (claim text → LLM term extraction → multisect search → auto-pick top function → analyze) got reduced to a stub during the refactoring into ce_analyze.py. The argparse definitions got changed to match the stub (metavar FUNCTION instead of CLAIM_TEXT/TERMS), and the dispatch presumably doesn't recognize the arguments properly, which is why you're falling through to interactive mode.

Let me check what happened with the test coverage.


Relevant chats
Relevant chats
Found it. Here's exactly what happened during the refactoring. From the refactoring transcript, ce_analyze.py was deliberately created as a stub:

python
def dispatch_analyze(index, args) -> bool:
    """Check if an analyze command was given and dispatch it.
    Returns True if handled, False otherwise."""
    if getattr(args, 'analyze', None):
        print("Analysis not yet implemented in ce_analyze.py.")
        print("Place analyze_bridge.py alongside these modules to enable.")
        return True
    if getattr(args, 'claim_analyze', None):
        print("Claim analysis not yet implemented in ce_analyze.py.")
        return True
    if getattr(args, 'multisect_analyze', None):
        print("Multisect analysis not yet implemented in ce_analyze.py.")
        return True
    return False
So the full analyze_bridge.py pipeline — which had the complete 5-step claim-analyze flow and the search-then-analyze multisect-analyze — was replaced with print statements saying "not yet implemented." The plan was apparently to port analyze_bridge.py's contents into ce_analyze.py, but only the stub dispatch + simplified argparse definitions made it in.

But that still doesn't fully explain falling through to interactive mode. Even the stub should return True and print "not yet implemented." The fact that you're getting interactive mode instead suggests either add_analyze_args in ce_analyze.py didn't register --claim-analyze with argparse at all, or the import of ce_analyze is failing silently (similar to the original try/except ImportError: ANALYZE_AVAILABLE = False pattern from analyze_bridge integration).

On the test coverage gap: The existing test_code_search harness tests --claim-search (the search-only path) but I don't see any tests for --claim-analyze or --multisect-analyze in either test_code_search_16 or test_code_search_17. The test_analyze_bridge.py that was created tested the analyze_bridge module in isolation (masking, prompt building, resolution), but those tests aren't wired into the main test harness and wouldn't have been run during the refactoring validation. So the "138/138 tests pass" result from the refactoring was true — but only because there were no tests for the analyze commands to begin with.

The fix path: Copy the full do_claim_analyze and do_multisect_analyze implementations from your analyze_bridge.py into ce_analyze.py, fix the argparse metavars back to CLAIM_TEXT/TERMS, and add test cases to the main harness. When you upload the current files I can do this, but the core issue is that analyze_bridge.py has the working code and ce_analyze.py has stubs.

If I put analyze_bridge.py and ce_analyze.py in the project, can you do this, and test? I can't tell if you have access to all the ce_*.py files, as well as code_exam.py .

Feb 16
I don't have any of the ce_*.py files or code_exam.py currently — the project only contains the pre-refactoring files (code_search_63 through 67, code_analyzer_v3_11, the test harnesses, etc.). The refactored modules were created during that conversation session and copied to outputs for you to download, but they never made it into the Project.

So yes, I'd need you to upload the full set. The complete list from the refactoring was:

code_exam.py (entry point)
ce_utils.py
ce_index.py
ce_parse.py
ce_search.py
ce_browse.py
ce_callers.py
ce_graph.py
ce_metrics.py
ce_dedup.py
ce_multisect.py
ce_interactive.py
ce_analyze.py (the stub — to be fixed)
analyze_bridge.py (the working implementation — source for the fix)
Plus test_code_search_17_13.py (or whatever the current test harness is).

One caveat on testing: the bash/filesystem tools have been flaky this session, and even when working, I can test that everything compiles and imports cleanly, and I can verify the argparse definitions, dispatch logic, and function signatures. But I can't do a live end-to-end test of --claim-analyze since that requires an API key and an indexed source tree. I can add proper test cases to the test harness that you'd run on your machine.

If uploading 14 files is painful (especially since the Project won't take a zip), an alternative approach: just upload analyze_bridge.py and ce_analyze.py, and I'll produce the corrected ce_analyze.py with the full implementations ported in, plus the test cases to add. You'd then paste/merge locally.

I've uploaded analyze_bridge.py and ce_analyze.py, plus the most recent test_analyze_bridge_5.py . I hope you can just give me a new ce_analyze.py . You say "You'd then paste/merge locally", but I'm hoping it's just a matter of dropping in the new ce_analyze.py you'll give me.

Feb 16

Viewed 7 files
Viewed 7 files
Now let me check the test file briefly:


Check test_analyze_bridge_5.py structure
Check test_analyze_bridge_5.py structure
Good — I have the complete picture. analyze_bridge.py is 2,011 lines with the full pipeline; ce_analyze.py is a 76-line stub. The fix is straightforward: the new ce_analyze.py is essentially analyze_bridge.py with the module docstring updated and _get_claim_helpers() tweaked to find extract_claim_terms from ce_multisect directly rather than scanning all modules.


Edited 4 files, viewed 3 files, ran a command
Edited 4 files, viewed 3 files, ran a command
Let me take a cleaner approach — write the file from the top using the original as source, avoiding the encoding issues:


Rebuild ce_analyze.py with new docstring + full analyze_bridge content
Rebuild ce_analyze.py with new docstring + full analyze_bridge content
Now update _get_claim_helpers to find extract_claim_terms from ce_multisect directly:


Viewed a file, edited a file, ran a command
Viewed a file, edited a file, ran a command
Now verify the file compiles and has the right structure:


Ran 2 commands
Ran 2 commands
Now verify the existing test suite runs against the new module:


Ran 2 commands
Ran 2 commands
All 46 tests pass. Let me do a final sanity check on the file:


Ran 2 commands, read a file
Ran 2 commands, read a file
This should be a drop-in replacement. Summary of what changed vs. the stub:

76 → 2,025 lines: Full analyze_bridge.py content now lives in ce_analyze.py
Argparse: --claim-analyze metavar is CLAIM (not FUNCTION), --multisect-analyze metavar is TERMS (not FUNCTION)
dispatch_analyze: Calls real do_claim_analyze, do_multisect_analyze, do_file_analyze, do_analyze — no more "not yet implemented" stubs
_get_claim_helpers(): Updated to try from ce_multisect import extract_claim_terms first, then falls back to the old sys.modules scan
Updated docstring: Reflects ce_* module identity
The 46 existing tests in test_analyze_bridge_5.py all pass. You'll still want analyze_bridge.py alongside for those tests (since they from analyze_bridge import ...), or you could update the test imports to from ce_analyze import ... when convenient.

I renamed the earlier ce_analyze.py as ce_analyze_STUB.py, downloaded your new ce_analyze.py into the directory, and ran the following. Unfortunately I just got dropped into interactive mode. I checked __pycache__ and ce_analyze.cpython-310.pyc was large and brand new. And code_exam.py was updated to say "--claim-analyze CLAIM". So I'm sure I was running the brand-new ce_analyze.py

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --claim-analyze @..\moltbook_pseudo_claim.txt

Feb 16
The argparse registration is working (help shows CLAIM) but the dispatch isn't being reached. The problem is almost certainly in code_exam.py itself — it's either not calling dispatch_analyze, or calling it after the interactive-mode fallthrough.

Can you do a quick diagnostic? Run this:

python -c "import code_exam; import inspect; src = inspect.getsource(code_exam); print(src[src.find('dispatch_analyze'):src.find('dispatch_analyze')+200] if 'dispatch_analyze' in src else 'dispatch_analyze NOT FOUND in code_exam.py')"
Or simpler — just grep for it:

findstr "dispatch_analyze" code_exam.py
I suspect you'll find either that code_exam.py still has the old pattern wrapping it in if ANALYZE_AVAILABLE where ANALYZE_AVAILABLE is False because it's trying to import from analyze_bridge (the old name), or that dispatch_analyze is called but positioned after the interactive-mode fallthrough.

If you paste just the relevant ~20 lines from code_exam.py around the import and the dispatch section, I can tell you exactly what to fix.

C:\work\ai_code_exam\code_exam_py_files_3>python -c "import code_exam; import inspect; src = inspect.getsource(code_exam); print(src[src.find('dispatch_analyze'):src.find('dispatch_analyze')+200] if 'dispatch_analyze' in src else 'dispatch_analyze NOT FOUND in code_exam.py')"
dispatch_analyze
    ANALYZE_AVAILABLE = True
except ImportError:
    ANALYZE_AVAILABLE = False
def main():
    # Windows redirect safety: when stdout/stderr are piped to a file, Python
    # uses t

Feb 16
There it is. code_exam.py is still importing from analyze_bridge, not ce_analyze. Run:

findstr "analyze_bridge" code_exam.py
You'll see something like:

python
from analyze_bridge import add_analyze_args, dispatch_analyze, reset_llm
Change it to:

python
from ce_analyze import add_analyze_args, dispatch_analyze, reset_llm
That's the only change needed. The try/except ImportError around it means when analyze_bridge isn't found (because the code now lives in ce_analyze.py), ANALYZE_AVAILABLE silently becomes False, and the dispatch is skipped entirely — dropping you straight into interactive mode with no error message.

Actually, my code_exam.py (exactly as downloaded from Claude) says:

from ce_analyze import add_analyze_args, dispatch_analyze

I changed that to add , reset_llm but not surprisingly I still get dropped into interactive mode.

Same with --multisect-search "yes;no;maybe" .

And with --file-analyze code_exam.py

Should I give you my code_exam.py ?

Feb 16
Yes, please — --multisect-search dropping into interactive mode too means this isn't just an analyze issue. That's a core ce_multisect command that was working before. The dispatch logic in code_exam.py's main() has a broader problem — it's likely not matching any of the CLI commands and falling through to interactive mode every time. I need to see the full dispatch section.

Oh sorry, I meant to type --multisect-analyze NOT --multisect-search. In any case, I'm putting code_exam.py into the project. Give me one minute.

Feb 16
No rush — and good, that narrows it back down to the analyze dispatch specifically. Once you've uploaded it I'll look at the dispatch section to see why ANALYZE_AVAILABLE is ending up False or why the dispatch call isn't being reached.

File is there now

Feb 16

Read code_exam.py to find the analyze import and dispatch
Read code_exam.py to find the analyze import and dispatch
There's the problem — line 666. The dispatch_analyze call is completely missing from the dispatch section. The argparse args get registered (line 536-537), but nobody ever checks them. The elif chain goes from claim_search straight to semantic, and since none of the analyze args match any of those conditions, it falls through to line 666:

python
elif args.interactive or (not args.build_index):
    do_interactive(index, args)
The fix is to add the dispatch call before the elif chain. Add this right after line 590 (if args.build_index: block) and before line 592 (if args.search:):

python
    # Analysis commands (analyze, claim-analyze, multisect-analyze, file-analyze)
    if ANALYZE_AVAILABLE and dispatch_analyze(index, args):
        return
That's it. dispatch_analyze returns True if it handled a command, so the return exits main() cleanly. If no analyze command was given, it returns False and execution continues into the existing elif chain.

Give me an entire new code_exam.py so we don't have so many differences what you have and what I do.

Feb 16

Ran 2 commands
Ran 2 commands
Claude's response could not be fully generated

Ran a command, viewed a file, read a file
Ran a command, viewed a file, read a file
The only two changes from your uploaded version:

Line 65: Added reset_llm to the import (you'd already done this locally)
Lines 591–593: Added the missing dispatch_analyze call before the elif chain — this is the actual fix that was causing the fallthrough to interactive mode
That was it. I'll need to check functionality, but basically works now. Whew.

"No function match. Best file match is 'skill.md' (791 lines) — too large for analysis."

I tried --min-terms 4, which I believe used to work with --claim-analyze, but didn't seem to make a difference.

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --claim-analyze "restrict posting to verified AI agents" --use-claude

Claude Claim Analysis:
(Claude API error: 429 Client Error: Too Many Requests for url: https://api.anthropic.com/v1/messag)

Should be "/messages", yes?

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Feb 16
Two separate things:

The URL is fine — it's /v1/messages. The error message is just getting truncated. Line 132 of ce_analyze.py has str(e)[:80] which cuts off the display. The actual 429 error is a rate limit — you're hitting Anthropic's API too fast (claim-analyze makes two API calls: one for term extraction, one for analysis). That should resolve by just waiting a moment and retrying.

--min-terms not affecting --claim-analyze — that's by design in the current code. Look at line ~1024 in ce_analyze.py:

python
min_terms = max(int(len(positive_terms) * 0.80), 2)
It hardcodes the 80% threshold and ignores args.min_terms. The BROAD fallback uses 60%. Neither reads your --min-terms flag. If you want --min-terms to override the auto-calculated threshold in claim-analyze, that's a small fix — something like:

python
user_min = getattr(args, 'min_terms', None)
if user_min and user_min != '0':
    try:
        min_terms = int(user_min)
    except ValueError:
        min_terms = max(int(len(positive_terms) * 0.80), 2)
else:
    min_terms = max(int(len(positive_terms) * 0.80), 2)
Want me to make that change to ce_analyze.py? And I can bump the error truncation from 80 to 200 chars while I'm at it.

Yes please. Though it's worrisome that in many previous tests I didn't run into this.

Feb 16
On the "used to work" concern — I don't think --min-terms ever actually flowed through in --claim-analyze. The hardcoded 80% threshold has been there since analyze_bridge.py was written. You may be remembering it working with --claim-search (in ce_multisect.py), where --min-terms is respected. The two commands share the same search engine but --claim-analyze bypasses the user's min-terms when it calls multisect_search internally.


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Lines 128 and 166 (error messages) now show 200 chars. Lines 1155/1248 are claim text display truncation — those should stay at 80.


Ran 3 commands, read a file
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Two changes from the last version:

Error truncation: str(e)[:80] → str(e)[:200] on lines 128 and 166 (Claude API and local LLM error messages). You'll now see the full URL and error detail.
--min-terms override: Lines 1020–1027. If you pass --min-terms 4, claim-analyze will use 4 instead of the auto-calculated 80% threshold. Without --min-terms, behavior is unchanged.
And you're right that you likely were remembering --claim-search where --min-terms has always worked — that path goes through ce_multisect.py which reads args.min_terms directly.

Now ran all the way through. Claude analyzed the two functions that code_exam selected based on search for terms related to the passed-in claim language. Unfortunately neither were the best hit, but that's a different issue. 

Now I'll just make sure that we get a decent analysis of the appropriate function:

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --analyze claim --use-claude
...
ANALYZE: api-main\src\services\AgentService.js@AgentService::claim
  Lines 189-215 (27 lines)
...
Claude Analysis:
This function implements an agent ownership claiming mechanism through database state transition. It accepts a claimToken string and twitterData object containing Twitter verification credentials.
...
In summary, this function implements a secure agent ownership claiming system with Twitter-based verification and atomic state transitions.
======================================================================
Claude API Usage: 1 requests, 361 in / 242 out, ~$0.0047
  âš  AI analysis may contain errors. Verify claims against the source code.
  Tip: --extract AgentService.js@AgentService::claim to see the full function source

Okay, this is good. I wanted --claim-analyze to find this function and pass it to Claude for analysis. The difference from the user first running --multisect-search on some keywords, finding the function, and then the user passing the function to Claude is that with --claim-analyze Claude should be given some of the claim text and it should assess the function with respect to that claim text. It looks like, if the pseudo text were different, this would work.

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Feb 16
Exactly right. The pipeline is now end-to-end functional — the two stages are:

Term extraction + search (LLM call #1): claim text → search terms → multisect search → find functions
Claim-relevant analysis (LLM call #2): function source + original claim text → Claude assesses how the code relates to the claim
Stage 2 uses build_claim_analyze_prompt, which gives Claude both the function body and the patent claim text, and asks it to map code operations to claim elements, report coverage ("N of M elements found"), and flag what's missing. That's the key difference from bare --analyze — it's not just "what does this code do?" but "does this code implement what the claim describes?"

The weak link in your test was stage 1 — the pseudo claim text produced search terms that led to the wrong functions. AgentService::claim would have been a direct hit if the extracted terms included "claim" or "agent" or "ownership," but evidently the LLM went in a different direction. That's the term extraction quality issue, not the pipeline.

The good news is that --analyze claim --use-claude proved the right function exists and is analyzable. So you know that if the claim text (or --min-terms tuning) gets the search to land on AgentService::claim, the full claim-analyze output would give you exactly what you want.

Hmm, I surprised, this didn't find any function:

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --claim-analyze @moltbook_cl.txt --use-claude
Loaded existing index: 133 files
Claim text (34 chars):
  claim agent ownership with twitter
Extracting search terms from patent claim...
...
No function or file matches found.

Even though:

:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --extract claim
Loaded existing index: 133 files
# api-main\src\services\AgentService.js@AgentService.claim
  /**
   * Claim an agent (verify ownership)
   *
   * @param {string} claimToken - Claim token
   * @param {Object} twitterData - Twitter verification data
   * @returns {Promise<Object>} Claimed agent
   */
  static async claim(claimToken, twitterData) {
    const agent = await queryOne(
      UPDATE agents
       SET is_claimed = true,
           status = 'active',
           owner_twitter_id = $2,
           owner_twitter_handle = $3,
           claimed_at = NOW()
       WHERE claim_token = $1 AND is_claimed = false
       RETURNING id, name, display_name,
      [claimToken, twitterData.id, twitterData.handle]
    );
    if (!agent) {
      throw new NotFoundError('Claim token');
    }
    return agent;
  }

And even though the 2nd hit in:

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --multisect-search "claim;agent;owner;twitter"
...
FUNCTION-LEVEL (4/4 terms in a single function):
  ------------------------------------------------------------------------------
  getStatus  --  api-main\src\services\AgentService.js  [4/4  22L]
    [1] claim    line 169: 'SELECT status, is_claimed FROM agents WHERE id = $1',
    [2] agent    line 168: const agent = await queryOne(
    [3] owner    line 183: * Claim an agent (verify ownership)
    [4] twitter  line 186: * @param {Object} twitterData - Twitter verification data
  claim  --  api-main\src\services\AgentService.js  [4/4  27L]
    [1] claim    line 189: static async claim(claimToken, twitterData) {
    [2] agent    line 190: const agent = await queryOne(
    [3] owner    line 194: owner_twitter_id = $2,
    [4] twitter  line 189: static async claim(claimToken, twitterData) {

Okay, after changing moltbook_cl.txt, this located claim() and is passing it to LLM for analysis:

C:\work\ai_code_exam\code_exam_py_files_3>python code_exam.py --index-path ..\.moltbook_src --claim-analyze @moltbook_cl.txt --use-claude
Loaded existing index: 133 files
Claim text (25 chars):
  claim agent owner twitter
...
  Found 3 function match(es), analyzing top 2:
  [1] [4/4 terms] api-main\src\services\AgentService.js@getStatus (22 lines)
  [2] [4/4 terms] api-main\src\services\AgentService.js@claim (27 lines)
...
CLAIM-ANALYZE: api-main\src\services\AgentService.js@claim
  Lines 189-215 (27 lines)
  Claim: claim agent owner twitter
======================================================================
🤖 Claude Claim Analysis:
Looking at the patent claim "claim agent owner twitter" and analyzing the function:
**Claim Element Analysis:**
1. **"claim"** - ✅ IMPLEMENTED
   - Line 11: SET is_claimed = true - explicitly sets claimed status
   - Line 15: claimed_at = NOW() - records claim timestamp
   - Line 17: WHERE claim_token = $1 AND is_claimed = false - uses claim token for verification
2. **"agent"** - ✅ IMPLEMENTED
   - Line 10: UPDATE agents - operates on agents table
   - Line 18: RETURNING id, name, display_name - returns agent data
   - Line 21-23: Error handling for invalid claim tokens
3. **"owner"** - ✅ IMPLEMENTED
   - Line 13: owner_twitter_id = $2 - sets owner's Twitter ID
   - Line 14: owner_twitter_handle = $3 - sets owner's Twitter handle
   - Function establishes ownership relationship between claimer and agent
4. **"twitter"** - ✅ IMPLEMENTED
   - Line 17: [claimToken, twitterData.id, twitterData.handle] - uses Twitter data for verification
   - Lines 13-14: Stores Twitter credentials as ownership proof
   - Parameter twitterData contains Twitter verification information
**Operations Beyond Claim:**
- Line 12: status = 'active' - sets agent status (not mentioned in claim)
- Lines 21-23: Error handling with NotFoundError (implementation detail)
**Missing Elements:** None - all claim elements are present.
The function fully implements an agent claiming system using Twitter for owner verification, storing ownership data, and managing claim state.
**Claim coverage: 4 of 4 elements found in this function.**
======================================================================
Claude API Usage: 2 requests, 755 in / 783 out, ~$0.0140
  âš  AI analysis may contain errors. Verify claims against the source code.
  Tip: --extract AgentService.js@claim to see the full function source

Bingo! Once I sufficiently contrived "claim" text. Good for now.

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Feb 16
That's the full pipeline working end-to-end — claim text in, element-by-element coverage analysis out, with line citations and a coverage score. And the two-API-call cost was only $0.014.

The practical takeaway from your testing: the term extraction LLM call (call #1) is the bottleneck. When you gave it natural-language pseudo-claim text, it generated terms that missed the target. When you gave it the actual domain keywords directly, it nailed it. That's consistent with what we discussed about claim-search quality — the LLM term extractor tries to be clever about what to search for, and sometimes outsmarts itself.

For real patent claim text (which is dense with technical terms), the extraction should do better than with short pseudo-claims. But it's worth noting in the TODO that there's room to improve the term extraction prompt, or to let the user override/supplement the extracted terms — something like --claim-analyze @patent.txt --extra-terms "claim;agent;twitter" to pin specific terms alongside whatever the LLM generates.

Good milestone though — code_exam.py dispatch fix, full ce_analyze.py replacing the stub, --min-terms flowing through, error messages no longer truncated. All from a one-line missing dispatch call.