Adds the reconnaissance survey pass: a fast, ≤3-turn LLM call that
characterizes the target before any directory investigation begins.
The survey receives the file-type distribution (from the base scan),
a top-2-level tree preview, and the list of available dir-loop tools,
and returns description / approach / relevant_tools / skip_tools /
domain_notes / confidence via a single submit_survey tool call.
Wired into _run_investigation() before the directory loop. Output is
logged but not yet consumed — that wiring is #6. Survey failure is
non-fatal: if the call errors or runs out of turns, the investigation
proceeds without survey context.
Also adds a Band-Aid to _SURVEY_SYSTEM_PROMPT warning the LLM that
the file-type histogram is biased toward source code (the underlying
classifier has no concept of mail, notebooks, ledgers, etc.) and to
trust the tree preview when they conflict. The proper fix is #42.
Adds the system prompt for the survey reconnaissance pass. The survey
agent answers three questions (what is this, what approach, which tools
matter) from cheap signals — file type distribution and a top-2-level
tree — without reading files. Tool triage is tri-state: relevant, skip,
or unlisted (default), so skip is reserved for tools whose use would be
actively wrong rather than merely unnecessary.
Wiring of _run_survey() and the submit_survey tool follows in #5.
Add confidence and confidence_reason to both cache schemas in the dir
loop prompt. Add a Confidence section with categorical guidance
(high ≥ 0.8, medium 0.5–0.8, low < 0.5) and the rule to include
confidence_reason when confidence is below 0.7.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Moves _DIR_SYSTEM_PROMPT and _SYNTHESIS_SYSTEM_PROMPT from ai.py into
a dedicated prompts module. Both are pure template strings with .format()
placeholders — no runtime imports needed in prompts.py. Prompt content
is byte-for-byte identical to the original.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
