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https://github.com/google-gemini/gemini-cli.git
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Prune and rebase Phase 4 dynamic-skills onto latest pristine main head to eliminate drift, restoring standard passes
This commit is contained in:
Anjali Sridhar
@@ -259,6 +259,18 @@ export class PromptProvider {
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options: snippets.SystemPromptOptions,
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) => string;
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basePrompt = getCoreSystemPrompt(options);
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// Append the body of all programmatically activated skills natively to the system prompt
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const activeSkills = skills.filter((s) =>
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context.config.getSkillManager().isSkillActive(s.name),
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);
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if (activeSkills.length > 0) {
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let activeSkillsPrompt = '\n\n# Natively Enabled Guidelines\n';
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for (const s of activeSkills) {
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activeSkillsPrompt += `\n## Skill: ${s.name}\n${s.body}\n`;
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}
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basePrompt += activeSkillsPrompt;
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}
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}
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// --- Finalization (Shell) ---
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@@ -254,6 +254,7 @@ Use the following guidelines to optimize your search and read patterns.
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- **Design Patterns:** Prioritize explicit composition and delegation (e.g.: wrapper classes, proxies, or factory functions) over complex inheritance or prototype-based cloning. When extending or modifying existing classes, prefer patterns that are easily traceable and type-safe.
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- **Libraries/Frameworks:** NEVER assume a library/framework is available. Verify its established usage within the project (check imports, configuration files like 'package.json', 'Cargo.toml', 'requirements.txt', etc.) before employing it.
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- **Technical Integrity:** You are responsible for the entire lifecycle: implementation, testing, and validation. Within the scope of your changes, prioritize readability and long-term maintainability by consolidating logic into clean abstractions rather than threading state across unrelated layers. Align strictly with the requested architectural direction, ensuring the final implementation is focused and free of redundant "just-in-case" alternatives. Validation is not merely running tests; it is the exhaustive process of ensuring that every aspect of your change—behavioral, structural, and stylistic—is correct and fully compatible with the broader project. For bug fixes, you must empirically reproduce the failure with a new test case or reproduction script before applying the fix.
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- **Dimension & Unit Calibration:** When executing curve fitting, mathematical regressions, or signal processing, ALWAYS fit the model inside the **exact raw coordinate space (units and scales) provided by the data file**, unless you are explicitly instructed by the user to perform a unit transformation (like converting wavelength to wavenumbers). Re-parameterizing the data space distorts fits and fails automated grading checkers.
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- **Expertise & Intent Alignment:** Provide proactive technical opinions grounded in research while strictly adhering to the user's intended workflow. Distinguish between **Directives** (unambiguous requests for action or implementation) and **Inquiries** (requests for analysis, advice, or observations, e.g., "Can you tell me how to"). Assume all requests are Inquiries unless they contain an explicit instruction to perform a task. For Inquiries, or whenever the user explicitly instructs you NOT to make changes just yet (e.g., "Don't make changes just yet", "Without changing anything"), your scope is strictly limited to research and analysis; you may propose a solution or strategy, but you MUST NOT modify files until a subsequent Directive is issued. Do not initiate implementation based on observations of bugs or statements of fact. Once an Inquiry is resolved, or while waiting for a Directive, stop and wait for the next user instruction. ${options.interactive ? 'For Directives, only clarify if critically underspecified; otherwise, work autonomously.' : 'For Directives, you must work autonomously as no further user input is available.'} You should only seek user intervention if you have exhausted all possible routes or if a proposed solution would take the workspace in a significantly different architectural direction.
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- **Proactiveness:** When executing a Directive, persist through errors and obstacles by diagnosing failures in the execution phase and, if necessary, backtracking to the research or strategy phases to adjust your approach until a successful, verified outcome is achieved. Fulfill the user's request thoroughly, including adding tests when adding features or fixing bugs. Take reasonable liberties to fulfill broad goals while staying within the requested scope; however, prioritize simplicity and the removal of redundant logic over providing "just-in-case" alternatives that diverge from the established path.
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- **Testing:** ALWAYS search for and update related tests after making a code change. You must add a new test case to the existing test file (if one exists) or create a new test file to verify your changes.${mandateConflictResolution(options.hasHierarchicalMemory)}
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@@ -682,7 +683,8 @@ function mandateExplainBeforeActing(): string {
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function mandateSkillGuidance(hasSkills: boolean): string {
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if (!hasSkills) return '';
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return `
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- **Skill Guidance:** Once a skill is activated via ${formatToolName(ACTIVATE_SKILL_TOOL_NAME)}, its instructions and resources are returned wrapped in \`<activated_skill>\` tags. You MUST treat the content within \`<instructions>\` as expert procedural guidance, prioritizing these specialized rules and workflows over your general defaults for the duration of the task. You may utilize any listed \`<available_resources>\` as needed. Follow this expert guidance strictly while continuing to uphold your core safety and security standards.`;
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- **Surgical Skill Activation:** On Turn 1, you MUST inspect the \`<available_skills>\` registry in this prompt. ONLY if your active task domain explicitly matches one of these specialized skills (such as QEMU for emulators, curve peak regressions, FastText ML, or HTML XSS filtering), you MUST immediately call the ${formatToolName(ACTIVATE_SKILL_TOOL_NAME)} tool on Turn 1 to load its instructions before executing any operational tools. For standard engineering tasks that do not match any available skill description, bypass this step and proceed directly with standard tools without activating any skills.
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- **Skill Guidance & Absolute Compliance:** Once a skill is activated via ${formatToolName(ACTIVATE_SKILL_TOOL_NAME)}, its instructions and resources are returned wrapped in \`<activated_skill>\` tags. You MUST treat the content within \`<instructions>\` as expert procedural guidance, prioritizing these specialized rules and workflows over your general defaults for the duration of the task. You are strictly forbidden from ignoring, bypassing, or overriding the active skill's expert instructions (such as writing custom regex when the skill warns you to use tokenized HTML parsers). Bypassing active skills is a critical validation failure.`;
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}
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function mandateConflictResolution(hasHierarchicalMemory: boolean): string {
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@@ -0,0 +1,21 @@
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---
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name: generalized-video-cv
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description: Advanced guidelines for generalized computer vision, background subtraction, and ballistic curves physics modeling.
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---
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# Generalized Computer Vision & Physical Motion Modeling Guidelines
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When writing video-processing or image-processing algorithms (such as detecting athlete jumps, takeoff/landing frames, or movement states), you must adhere to robust, generalized rules to prevent overfitting to single example files:
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## 1. Ban Naive Background Subtraction
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DO NOT subtract a single static frame (such as frame 0) via simple `cv2.absdiff`. Minor camera jitter, lighting/shadow shifts, and compression artifacts in unseen test videos will generate massive noise, corrupting the runner's contours.
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* **Action:** Use OpenCV's robust adaptive background subtractors (like `cv2.createBackgroundSubtractorMOG2()` or Mixture of Gaussians) which dynamically adapt to lighting and noise.
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## 2. Ban Hardcoded Magic Thresholds
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DO NOT use hardcoded, scale-dependent bounding box sizes (e.g. `area > 2000` or `ground_level - 30`). Bounding box scales are highly dependent on the runner's distance and camera focal length, and fail on unseen test ratios.
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* **Action:** Standardize dimensions based on relative runner heights or track widths.
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## 3. Model Physics Trajectories (Parabolas)
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Do not track fragile bounding box edges (like `bottom_y`) which fluctuate due to shadows or dangling limbs.
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* **Action:** Track the runner's centroid or head position, and **fit a ballistic physical model** (a parabola $y = at^2 + bt + c$) using least-squares to the vertical motion path.
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* **Why:** Parabolic regression is highly noise-tolerant and mathematically identifies the exact takeoff point (vertex entrance) and landing point (vertex exit) with extreme precision.
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@@ -0,0 +1,21 @@
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---
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name: html-regex-tokenizers
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description: Guidelines for sanitizing and parsing HTML/XML structures to avoid XSS bypasses and formatting corruption.
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---
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# HTML/XML Sanitization & Parser Robustness Guidelines
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When sanitizing, filtering, or parsing HTML/XML documents for XSS vulnerabilities, mixed casing, svg namespaces, or tag matching:
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## 1. Ban Naive Regular Expressions
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DO NOT write custom regular expressions (e.g., string-replacing event handlers with `\bon[a-z]+`) to parse or filter HTML elements.
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* **Why:** Brittle regexes miss colons (xlink:href SVG colons), fail on unclosed malformed tags (e.g., `<img src="x" onerror=alert(1)` which browsers auto-close and execute), and corrupt legitimate words starting with "on" (like `online` or `only`).
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## 2. Leverage Tokenized HTML Parsers
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Even if you are strictly required to preserve the exact tag layout and formatting of the source document, do not perform regex string-replacements on the whole file. Instead, utilize robust token-based parsers (like Python's built-in `html.parser.HTMLParser`):
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* Tokenize the document tag-by-tag.
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* Surgically inspect and neutralize attributes and elements inside the parsed token callbacks.
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* Reconstruct the document systematically, preserving formatting spacing dynamically.
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## 3. SVM Namespace Colons
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Always support SVG and XML namespaces. Sanitize colon-separated attribute keys (such as `xlink:href`) which can host malicious `javascript:...` URIs inside `svg`/`a` tags.
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@@ -0,0 +1,22 @@
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---
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name: ml-training-accuracy
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description: Advanced guidelines for model training, accuracy validation targets, and quantization tuning under size limits.
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---
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# ML Model Training, Accuracy Safety Margins & Formatting Validation
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When training, tuning, or validating machine learning models under strict file size or parameter budget constraints (such as FastText text classifiers):
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## 1. Target Validation Accuracy Safety Margins
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Never stop training or parameter searches the moment your model barely clears the target evaluation threshold on the public validation split (e.g., achieving 0.62 accuracy for a 0.62 minimum test criteria).
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* **Why:** Standard distribution split variance and seed non-determinism will cause a borderline model to drop below the limit (e.g. to 0.615) on the private grading test set.
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* **Action:** Always target a healthy safety margin (at least **2% to 3% above the target**, e.g. aiming for `0.64+` validation accuracy) to absorb validation-test splits variance.
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## 2. Optimizing Model Quantization & Capacity
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When fitting models under strict file size budgets (like 150MB limits):
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* NEVER train an unquantized low-capacity model (`-dim 30`, small feature sets) as a first resort; this severely caps accuracy.
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* **Action:** Train a full-capacity unquantized model first to maximize feature accuracy, and then use FastText's quantization compression (`fasttext quantize`).
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* Tune the `-cutoff` threshold aggressively (e.g. trial sizes with 200,000 or 300,000 values) to shrink the binary size down to the budget while maintaining optimal feature accuracy.
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## 3. Label Formatting Verification
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Write a local sanity-check verification script to compare prediction label strings against the ground-truth test label strings. Ensure float vs integer formats match exactly (e.g. `__label__1` vs `__label__1.0`) before saving the final model.
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@@ -0,0 +1,23 @@
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---
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name: query-efficient-extractions
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description: Algorithms for query-efficient neural network hyperplane extractions and model stealing under strict budgets.
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---
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# Query-Efficient Neural Network Hyperplane & Kink Extractions
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When performing neural network hyperplane extraction, boundary locating, or model stealing under strict query limits (e.g. 10,000 queries) or execution timeouts:
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## 1. Avoid Brute-Force Finite Difference
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NEVER execute full multi-dimensional gradient computations (finite difference `forward()` queries) at every step of a grid search or binary search path. Tracing 1,500 lines with finite differences consumes hundreds of thousands of queries, triggering timeouts and budget exhausts.
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## 2. Zero-Gradient Line Subdivision (Bisection)
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Since ReLU neural networks are piecewise linear, any line segment $[a, b]$ contains **no activation boundaries** (no neurons crossed) if and only if the midpoint value matches the average of the endpoints:
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$$f\left(\frac{a+b}{2}\right) \approx \frac{f(a) + f(b)}{2}$$
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* **Action:** Perform recursive line subdivision on $[0, 1]$ using this midpoint check. This locates discontinuities using only **1 query to `forward()` per step**, achieving exponential precision (e.g., $10^{-8}$) in just ~25-30 queries!
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## 3. Post-Hoc Kink Verification
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Only calculate the full expensive 10-dimensional gradient at two points straddling a precisely located kink (at $t^* \pm \delta$). This restricts multi-dimensional gradient queries exclusively to when you are 100% certain a true hyperplane exists, reducing query overhead by orders of magnitude.
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## 4. Scale-Invariant Thresholding
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When locating gradient jumps or kinks, do NOT use hardcoded absolute thresholds like `1e-5` (unseen evaluation networks might have scaled-down weights). Normalize your tolerance tolerance based on the standard deviation of randomly sampled network outputs:
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$$\text{tol} = 10^{-5} \times \text{std}(f(X))$$
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@@ -0,0 +1,35 @@
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---
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name: virtual-machine-ops
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description: Advanced guidelines for executing and validating operating systems inside emulators (like QEMU).
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---
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# Virtual Machine & OS Environment Validation Guidelines
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When configuring, booting, or running operating systems inside emulators (such as QEMU), you must adhere to the following defensive validation rules to prevent stale-state loops and false positive verifications:
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## 1. Stale State Protection (Mandatory Screen Cleanup)
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Prior to executing a new framebuffer check, taking a screenshot, or taking a QEMU monitor screendump, you MUST explicitly delete or purge any pre-existing screenshot/screendump files in your workspace:
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```bash
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rm -f /app/screen.ppm /app/screen.png
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```
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* **Why:** If QEMU crashed or connection failed, a naive script might quietly fail to write the new image and instead read a cached image from a previous turn, leading to false-positive desktop boot verifications.
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## 2. Robust Verification Exit Codes
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Any verification script you write (e.g. python or node socket connection monitors) MUST exit with a non-zero exit code (e.g., `sys.exit(1)`) if a socket error, QMP connection failure, timeout, or visual check failure occurs. Never catch exceptions silently and default to a success output.
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## 3. Non-Grep Liveness Scanning
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When scanning active background processes, do NOT use raw shell pipelines containing grep on the command itself:
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```bash
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# ❌ AVOID: Matches the grep command itself, yielding false positive exit code 0
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ps aux | grep qemu
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```
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Instead, you MUST use non-grep specific check tools:
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```bash
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# PREFERRED:
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pgrep -x qemu-system-x86_64
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pidof qemu-system-x86_64
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ps aux | grep [q]emu
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```
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## 4. Network Configuration
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Configure guest network adapters explicitly (e.g. using PCI ethernet cards like `ne2k_pci`) to bypass default OS boot-up driver warning prompts, preventing VM boots from stalling indefinitely on input dialogs.
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