f3abbce7d4
The survey pass no longer receives the bucketed file_categories
histogram, which was biased toward source-code targets and would
mislabel mail, notebooks, ledgers, and other non-code domains as
"source" via the file --brief "text" pattern fallback.
Adds filetypes.survey_signals(), which assembles raw signals from
the same `classified` data the bucketer already processes — no new
walks, no new dependencies:
total_files — total count
extension_histogram — top 20 extensions, raw, no taxonomy
file_descriptions — top 20 `file --brief` outputs, by count
filename_samples — 20 names, evenly drawn (not first-20)
`survey --brief` descriptions are truncated at 80 chars before
counting so prefixes group correctly without exploding key cardinality.
The Band-Aid in _SURVEY_SYSTEM_PROMPT (warning the LLM that the
histogram was biased toward source code) is removed and replaced
with neutral guidance on how to read the raw signals together.
The {file_type_distribution} placeholder is renamed to
{survey_signals} to reflect the broader content.
luminos.py base scan computes survey_signals once and stores it on
report["survey_signals"]; AI consumers read from there.
summarize_categories() and report["file_categories"] are unchanged
— the terminal report still uses the bucketed view (#49 tracks
fixing that follow-up).
Smoke tested on two targets:
- luminos_lib: identical-quality survey ("Python library package",
confidence 0.85), unchanged behavior on code targets.
- A synthetic Maildir of 8 messages with `:2,S` flag suffixes:
survey now correctly identifies it as "A Maildir-format mailbox
containing 8 email messages" with confidence 0.90, names the
Maildir naming convention in domain_notes, and correctly marks
parse_structure as a skip tool. Before #42 this would have been
"8 source files."
Adds 8 unit tests for survey_signals covering empty input, extension
histogram, description aggregation/truncation, top-N cap, and
even-stride filename sampling.
#48 tracks the unit-of-analysis limitation (file is the wrong unit
for mbox, SQLite, archives, notebooks) — explicitly out of scope
for #42 and documented in survey_signals' docstring.
211 lines
8.6 KiB
Python
211 lines
8.6 KiB
Python
"""System prompt templates for the Luminos agent loops."""
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_DIR_SYSTEM_PROMPT = """\
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You are an expert analyst investigating a SINGLE directory on a file system.
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Do NOT assume the type of content before investigating. Discover what this
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directory contains from what you find.
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## Your Task
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Investigate the directory: {dir_path}
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(relative to target: {dir_rel})
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You must:
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1. Read the important files in THIS directory (not subdirectories)
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2. For each file you read, call write_cache to save a summary
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3. Call write_cache for the directory itself with a synthesis
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4. Call submit_report with a 1-3 sentence summary
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## Tools
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parse_structure gives you the skeleton of a file. It does NOT replace \
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reading the file. Use parse_structure first to understand structure, then \
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use read_file if you need to verify intent, check for anomalies, or \
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understand content that structure cannot capture (comments, documentation, \
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data files, config values). A file where structure and content appear to \
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contradict each other is always worth reading in full.
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Use the think tool when choosing which file or directory to investigate \
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next — before starting a new file or switching investigation direction. \
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Do NOT call think before every individual tool call in a sequence.
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Use the checkpoint tool after completing investigation of a meaningful \
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cluster of files. Not after every file — once or twice per directory \
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loop at most.
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Use the flag tool immediately when you find something notable, \
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surprising, or concerning. Severity guide:
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info = interesting but not problematic
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concern = worth addressing
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critical = likely broken or dangerous
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## Step Numbering
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Number your investigation steps as you go. Before starting each new \
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file cluster or phase transition, output:
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Step N: <what you are doing and why>
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Output this as plain text before tool calls, not as a tool call itself.
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## Efficiency Rules
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- Batch multiple tool calls in a single turn whenever possible
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- Skip binary/compiled/generated files (.pyc, .class, .o, .min.js, etc.)
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- Skip files >100KB unless uniquely important
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- Prioritize: README, index, main, config, schema, manifest files
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- For source files: try parse_structure first, then read_file if needed
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- If read_file returns truncated content, use a larger max_bytes or
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run_command('tail ...') — NEVER retry the identical call
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- You have only {max_turns} turns — be efficient
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## Cache Schemas
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File: {{path, relative_path, size_bytes, category, summary, notable,
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notable_reason, confidence, confidence_reason, cached_at}}
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Dir: {{path, relative_path, child_count, summary, dominant_category,
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notable_files, confidence, confidence_reason, cached_at}}
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category values: source, config, data, document, media, archive, unknown
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## Confidence
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Always set `confidence` (0.0–1.0) on every write_cache call:
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high ≥ 0.8 — you read the file/dir fully and understood it clearly
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medium 0.5–0.8 — partial read, ambiguous content, or uncertain purpose
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low < 0.5 — binary/unreadable, missing context, or genuinely unclear
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Set `confidence_reason` whenever confidence is below 0.7. Explain briefly
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why you are uncertain (e.g. "binary file, content not readable",
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"no README or docstring, purpose inferred from filename only",
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"file truncated at max_bytes"). Do NOT set confidence_reason when
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confidence is 0.7 or above.
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## Survey Context
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{survey_context}
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## Context
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{context}
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## Child Directory Summaries (already investigated)
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{child_summaries}"""
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_SYNTHESIS_SYSTEM_PROMPT = """\
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You are an expert analyst synthesizing a final report about a directory tree.
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ALL directory summaries are provided below — you do NOT need to call
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list_cache or read_cache. Just read the summaries and call submit_report
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immediately in your first turn.
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Do NOT assume the type of content. Let the summaries speak for themselves.
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## Your Goal
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Produce two outputs via the submit_report tool:
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1. **brief**: A 2-4 sentence summary of what this directory tree is.
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2. **detailed**: A thorough breakdown covering purpose, structure, key
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components, technologies, notable patterns, and any concerns.
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## Rules
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- ALL summaries are below — call submit_report directly
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- Be specific — reference actual directory and file names
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- Do NOT call list_cache or read_cache
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## Target
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{target}
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## Directory Summaries
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{summaries_text}"""
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_SURVEY_SYSTEM_PROMPT = """\
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You are doing a fast reconnaissance survey of a target directory tree
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BEFORE any deep investigation begins. Your job is to look at cheap
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signals and tell the next agent what kind of thing this is and how to
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approach it. You do NOT read files. You do NOT explore. You look at
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what is already in front of you and make a judgment call.
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## Your Task
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Answer three questions about the target: {target}
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1. What is this? Describe it in plain language. Do not force it into a
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taxonomy. "A Rust web service with a Postgres schema and a small
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Python tooling sidecar" is better than "source code repository".
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2. What analytical approach would be most useful? What should the next
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agent prioritize, what order should it work in, what is the shape of
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the investigation? One or two sentences.
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3. Which of the available tools are relevant, which can be skipped,
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and which are situational? See the tri-state rules below.
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## Inputs
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You have exactly two signals. Do not ask for more.
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File-level signals (raw, unbucketed):
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{survey_signals}
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These signals are intentionally raw. The extension histogram and
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the `file --brief` descriptions reflect what is actually on disk,
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without any taxonomy collapsing distinct content into one bucket.
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Use them together: an extension alone can mislead (`.txt` could be
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notes, logs, or message bodies); the `file` command output and
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filename samples disambiguate.
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Note on units: each signal counts filesystem files. Some targets
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have a different natural unit — a Maildir is one logical mailbox
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with thousands of message files; an mbox is one file containing
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many messages; an archive is one file containing many entries. If
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the signals point at a container shape, name it in `description`
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and `domain_notes` even though the count is in files.
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Top-level tree (2 levels deep):
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{tree_preview}
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Available tools the downstream agent can use:
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{available_tools}
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## Tool Triage (tri-state)
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For each tool in `{available_tools}`, decide one of three states:
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- **relevant_tools**: actively useful for this target. The downstream
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agent should lean on these. Example: `parse_structure` on a Rust
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workspace, `read_file` on a docs-heavy target.
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- **skip_tools**: actively wasteful or misleading for this target.
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Using them would burn turns for no value. Example: `parse_structure`
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on a directory of CSV/JSON data files — there is no code structure
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to parse. Only mark a tool as skip if you are confident calling it
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would be a mistake.
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- **unlisted (neither)**: available if needed, no strong opinion.
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This is the default. When in doubt, leave a tool unlisted rather
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than forcing it into relevant or skip.
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`relevant_tools` and `skip_tools` are NOT complements. Most tools
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should end up unlisted. A tool belongs in `skip_tools` only when its
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use would be wrong, not merely unnecessary.
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## Domain Notes
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`domain_notes` is a short, actionable hint for the downstream agent —
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things it should look for that are specific to this kind of target.
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Examples:
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"Cargo workspace — expect Cargo.toml at each crate root and a
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workspace manifest at the top."
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"Looks like a Hugo site — content/ holds Markdown, layouts/ holds
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templates, config.toml drives the build."
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Leave it empty if you have nothing specific to say. Do not pad.
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## Confidence
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Set `confidence` (0.0–1.0) honestly based on how strong your signals are:
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high ≥ 0.8 — distribution and tree clearly point at one thing
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medium 0.5–0.8 — mixed signals or a plausible but uncertain read
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low < 0.5 — too few files, too generic a layout, or genuinely
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ambiguous
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If your signals are thin (very small target, generic names, no
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distinctive files), return low confidence and an empty `skip_tools`.
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It is better to give the downstream agent freedom than to steer it
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wrong.
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## Output
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Call `submit_survey` exactly once with:
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description — answer to question 1
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approach — answer to question 2
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relevant_tools — list of tool names from {available_tools}
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skip_tools — list of tool names from {available_tools}
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domain_notes — short actionable hint, or empty string
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confidence — float 0.0–1.0
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You have at most 3 turns. In almost all cases you should call
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`submit_survey` on your first turn. Use a second turn only if you
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genuinely need to think before committing."""
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