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skills/add-new-model/SKILL.md

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+---
+name: add-new-model
+description: Guided workflow for adding a new model architecture to llama.cpp. Use when the user wants to add/port a new model architecture.
+---
+
+# Add a new model architecture to llama.cpp
+
+This skill walks a contributor through adding a new model architecture. AI-generated code is permitted in this project, so you may write full implementations for the steps below rather than only pointing at patterns - but follow `AGENTS.md`'s AI usage policy throughout:
+
+- The contributor is 100% responsible for every line, however it was produced. They must be able to explain and defend any part of it to a reviewer. Check in with them as you go (don't silently generate everything and hand over a finished diff) so they actually absorb what was written.
+- Before writing code, make sure the contributor owns the design choices for this architecture (which reference model to follow, how non-standard bits like RoPE variants or MoE routing should be handled) - AI accelerates a design the contributor has already made, it doesn't make the design for them.
+- Disclosure is mandatory: any AI-meaningful contribution must be disclosed per the PR template. Remind the contributor of this before they open the PR.
+- Never write the PR description, commit message, GitHub issue/discussion post, or reviewer replies - those must come from the contributor. If asked to commit on their behalf, use `Assisted-by:` (never `Co-authored-by:`) and only after explicit confirmation.
+- If the requested change looks large or introduces a new pattern not covered here, pause and tell the user this kind of change is likely to need prior discussion with maintainers before a PR.
+- Keep the PR self-contained. If the work would require a lot of unconventional changes outside the new model file(s) (e.g. touching shared graph-building code, the sampler, or core APIs in ways other models don't), STOP and tell the contributor to open a discussion/issue first - invasive or excessive changes get closed without full review.
+- Do not bundle unrelated work into this PR - see Step 4 and Step 5 below for the specifics on multimodal and chat-template/parsing work.
+- Never hack around RoPE with a custom sin/cos implementation. Several past PRs tried this and were closed. If the existing `ggml_rope_ext` (see Step 2's RoPE tips) genuinely cannot express what this model needs, the contributor should open an issue to discuss it with maintainers first - not send a PR with a custom RoPE implementation.
+
+Before starting, read `CONTRIBUTING.md`, `AGENTS.md` and `docs/development/HOWTO-add-model.md` if they are not already in context. Also run `git log --oneline -- src/models` and look at at least 3 recent PRs that added a model (their merge commits/diffs) - this shows current convention more reliably than the docs, which can lag behind.
+
+## Step 0 - Scope and dedup check
+
+Ask the contributor:
+1. Which model (HF repo id or name)? Is it text-only or does it have a multimodal (vision/audio) encoder?
+2. Do they already have the HF `config.json`/weights available locally?
+3. Have they checked for an existing PR/issue on this model? Suggest `gh search issues "<model name>"` and `gh search prs "<model name>"` in the `ggml-org/llama.cpp` repo. If an existing PR covers it, the contributor should comment there and collaborate rather than open a duplicate (per CONTRIBUTING.md's AI Usage Policy).
+4. What existing supported architecture is this model closest to (e.g. "Llama-like with sliding window", "MoE like DBRX", "BERT-style encoder")?
+
+If the contributor doesn't know the closest reference architecture, you may grep `conversion/*.py` and `src/models/*.cpp` for architectures with a similar config shape (layer count, head count, MoE expert count, norm placement) and suggest 1-2 candidates - but let the contributor confirm the choice rather than picking one yourself; this choice is a design decision they need to own.
+
+Do not proceed to Step 1 until the contributor has answered these and named a reference architecture.
+
+## Step 1 - Convert the model to GGUF
+
+Follow HOWTO-add-model.md section 1 for the actual touch points (conversion class registration, `constants.py`, `tensor_mapping.py`, etc.) - don't re-derive them here, read them from the doc.
+
+Skill-specific addition: for each touch point, show the contributor the equivalent code in the reference architecture they named in Step 0 before writing the new version, and check that they understand what's different about their model (e.g. non-standard tensor shapes, extra hparams) rather than just copying the pattern silently.
+
+## Step 2 - Define the architecture in llama.cpp
+
+Follow HOWTO-add-model.md section 2 for the actual touch points (`llm_arch` enum, `LLM_ARCH_NAMES`, hparam loading, RoPE type case, etc.), including its "Tips and tricks" section for `ggml_rope_ext` gotchas.
+
+Skill-specific addition: never hack around RoPE with a custom sin/cos implementation - see the RoPE rule above.
+
+## Step 3 - Build the GGML graph
+
+Follow HOWTO-add-model.md section 3 for the actual touch points (`src/models/<name>.cpp` struct, `llama_model_mapping` registration, etc.).
+
+Skill-specific addition: before writing `src/models/<name>.cpp`, read at least 10 other files under `src/models/` (pick a mix, not just the one reference architecture) to confirm the struct layout, naming, and style you're about to write actually matches current convention - the pattern drifts over time and the HOWTO doc can lag behind it.
+
+## Step 4 - Optional: multimodal encoder
+
+Only do this if the contributor flagged a vision/audio encoder in Step 0. Follow HOWTO-add-model.md section 4 and `docs/multimodal.md` for the actual touch points (`MmprojModel` subclass, `clip.cpp`, `mtmd.cpp`, encoder graph in `tools/mtmd/models`, etc.).
+
+Skill-specific addition, and read this carefully: **whether the multimodal encoder can be bundled into the same PR as the base text-model support depends on how conventional the change is.** It's OK to bundle it if the encoder support is conventional - i.e. no new infra or logic is needed, it's just a new cgraph reusing existing preprocessing/projector machinery (e.g. siglip/pixtral/qwen with just a new projector). If it requires anything beyond that - a new preprocessor, non-standard projector logic, or changes to shared `libmtmd` infra/logic - STOP, tell the contributor this is non-conventional, and have them land the text model first with the encoder as a dedicated follow-up PR. Do not let this decision pass silently - call it out explicitly to the contributor before writing any `clip.cpp`/`mtmd.cpp` code.
+
+## Step 5 - Optional: chat template / parsing support
+
+Only do this if the model needs a new built-in chat template (`src/llama-chat.cpp`) or a new output parser (see `docs/development/parsing.md` and `docs/autoparser.md`). If either is needed beyond what a user-supplied Jinja template already covers, treat it as its own dedicated follow-up PR, not part of the base model-support PR - call this out explicitly to the contributor rather than silently bundling it in.
+
+## Common pitfalls (from past PR reviews)
+
+These recur often enough in review comments on past add-model PRs that they're worth checking proactively, not just waiting for a reviewer to catch them:
+
+- Don't validate the same hparam/config assumption in both the Python conversion script and the C++ load path - pick one layer to own the check, duplicating it just adds maintenance surface.
+- Optional hparams that are genuinely absent from some configs (e.g. a shared-expert count) should be read with an explicit optional/fallback accessor, not assumed present.
+- Hparams that are actually load-bearing (the model produces wrong output or crashes without them, e.g. `sliding_window_pattern`, norm-eps) must hard-error if missing, not silently fall back to a default.
+- Don't bake a default chat template into the C++ binary - inject it into the GGUF at conversion time instead, since one `llm_arch` can be reused by multiple fine-tunes with different templates, and a baked-in C++ default fails silently for those.
+- Before writing a dedicated tool-call/output parser, check whether the existing autoparser already handles the template (`llama-debug-template-parser <jinja>` shows what it detects).
+- Marking a custom EOS/closing-tag token as `eot` at conversion time isn't always sufficient - in long/agentic generations a model can emit the closing sequence as literal text instead of the token, so generation never stops on EOG and raw text leaks past the parser. Verify this case, not just the token path.
+- If reusing or aliasing an existing pre-tokenizer for convenience, justify and test that choice explicitly - silent reuse is an easy source of subtle tokenizer bugs.
+- Watch for excessive graph splits caused by building per-layer view/index tensors inside the layer loop - hoist tensors that don't vary per layer out of the loop (relevant if you hit `GGML_SCHED_MAX_SPLIT_INPUTS`).
+- A custom KQ mask fed into flash attention must match FA's expected dtype - cast it to F16 before passing it to `build_attn_mha` when FA is enabled.
+- When padding a custom KV-cache size to an alignment (e.g. `GGML_PAD(..., 256)`), apply the padding after all other size adjustments, not before - otherwise later logic can un-align it again.
+- For non-standard cache/SWA (sliding-window-attention) semantics, override the dedicated hook (e.g. `llama_model_n_swa()`) rather than mutating hparams to fake the behavior - hparams may be read elsewhere for unrelated purposes.
+- Don't ship unfinished or unverified speculative-decoding (e.g. MTP) scaffolding in the base model PR - if it hasn't actually been confirmed to work, pull it out and land it as its own follow-up.
+- Conversion code should call into the base class's existing hparam logic (e.g. `super().set_gguf_parameters()`) rather than re-deriving it - large blocks of code that duplicate what `TextModel`/`MmprojModel` already provide will get flagged as redundant.
+- Do constant tensor modifications (e.g. `norm(1 + weight)`) and permutations/chunking at conversion time, not in the graph - see HOWTO-add-model.md's "Prefer conversion-time tensor modifications" tip (Gemma 3 folds its `1 +` into the weights, Qwen3-Next permutes in `modify_tensors`). Doing these at runtime in the graph is very likely to be rejected as over-complicated; if you genuinely can't do it at conversion time, open a discussion first explaining why rather than implementing it in the graph.
+  - Exception: a plain `weight * scale` with a constant scale is usually better applied at inference time instead of being folded into the weight at conversion. The scale conceptually applies to the activation, not the weight, so folding it in can hurt numerical stability, and it shifts the weight's value range in a way that can make quantization worse.
+
+## Validation checklist
+
+Reference: `examples/model-conversion/README.md`.
+
+1. Convert to GGUF, then inspect/run both the original and converted tensors.
+2. Run logits verification (original vs converted). If this model is a new version of an already-supported family, verify the *previous* version still passes logits verification first - numerical differences may be pre-existing, not caused by the new work. The tools to perform full logits validation are available in `examples/model-conversion`.
+3. Quantize (including QAT variants if relevant) and re-verify.
+4. Run perplexity evaluation (simple and full).
+5. Sanity-check across `tools/cli`, `tools/completion`, `tools/imatrix`, `tools/quantize`, and `tools/server`.
+6. CPU backend first; other backends (CUDA, Metal, ...) can be separate follow-up PRs per `CONTRIBUTING.md`.
+7. Re-review every changed file against the coding/naming guidelines in `AGENTS.md` (and `CONTRIBUTING.md`'s "Coding guidelines"/"Naming guidelines" sections) - this is a separate pass from functional testing and is just as important: no forced line-wrapping, no unicode punctuation, minimal/non-redundant comments, `snake_case` naming (`kebab-case` for file names), matching indentation/brace style, etc.
+
+## Before opening a PR
+
+- Run the `code-review` skill on the diff first - it catches the convention and scope issues reviewers flag most often, and it's recommended to do this locally before pushing the PR.
+- Confirm the contributor can explain every changed line to a reviewer and is prepared to be asked about any of it - this is required regardless of how much of the code was AI-generated.
+- Confirm they did a comprehensive manual review of the full diff, not just a skim.
+- Fill in the AI-disclosure section of `.github/pull_request_template.md` describing how AI was used (do not omit or understate this).
+- Do not write the PR description, commit message, GitHub issue/discussion text, or any reviewer replies yourself - the contributor writes these.

skills/code-review/SKILL.md

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+---
+name: code-review
+description: Review llama.cpp changes against project conventions and common reviewer pitfalls before a PR. Use when the user wants to review a diff, branch, or PR.
+---
+
+# Review llama.cpp changes
+
+This skill reviews changes against llama.cpp's conventions and the pitfalls that reviewers flag most often, so the contributor can fix them before a maintainer has to. It has two modes:
+
+- **Self-review (default):** review the contributor's own local changes (uncommitted work, or a branch vs `master`) as a pre-PR pass. Ask which if it's ambiguous; default to `git diff master...HEAD` plus any uncommitted changes.
+- **Read-only review of a PR/file:** if the user points at a PR number or specific files (including code they didn't write), review those and report findings.
+
+In both modes the output is **private review notes for the user to read and act on** - it is never something to post. This is a hard rule from `AGENTS.md`: an agent must NEVER write, or help write, a PR comment, a review comment, or a reply to a reviewer, by any means including `gh`. Do not offer to. If the user asks you to post the notes, refuse and point them at that rule. Present findings in the conversation only.
+
+Before starting, read `AGENTS.md` and `CONTRIBUTING.md` if not already in context - the "Coding guidelines", "Naming guidelines", and AI usage sections are the baseline this review enforces. For a diff that adds a new model architecture, also read `docs/development/HOWTO-add-model.md` and consider the dedicated `add-new-model` skill.
+
+## Step 0 - Scope the diff and pick the checklists
+
+Identify what actually changed and which area checklists below apply. Run `git diff --stat` (or `gh pr view <n> --json files` for PR mode) and bucket the touched paths:
+
+- `conversion/`, `gguf-py/`, `src/models/`, `src/llama-arch.*` -> **New model / architecture**
+- `ggml/` (any backend, op, or `ggml.h`) -> **ggml / backend**
+- `include/llama.h` and other public headers -> **Public API**
+- `tools/server/` -> **Server**
+- anything else, plus all of the above -> **General** (always runs)
+
+Always run the **Scope and quick-reject gate**, the **Security review**, and the **General** checklist. Run each area checklist whose paths were touched. Additionally, if the diff introduces a new component, subsystem, or piece of infrastructure (a new file/class/module, a new abstraction, or hand-rolled machinery), run the **Approach and design** review. Tell the user which checklists you're running and why.
+
+## Scope and quick-reject gate (always)
+
+These are the patterns that get PRs closed without a full review. Check them first - a finding here is more important than any code nit, because it can mean the change shouldn't be a PR in its current form at all.
+
+- Is there a prior issue/discussion for this? Features are supposed to start as an issue, not a PR (`CONTRIBUTING.md`). If this is a nontrivial feature with no linked issue, flag it and suggest opening one first.
+- Is it a duplicate of existing/in-flight work? Suggest `gh search prs` / `gh search issues` for the feature. Many closed PRs were duplicates of something already queued.
+- Is it self-contained and single-purpose? Multiple unrelated changes/optimizations bundled together get sent back to be split. Flag unrelated changes and suggest separate PRs.
+- Does it touch multiple ggml backends at once? Initial support should be CPU-only, other backends as follow-ups (`CONTRIBUTING.md`). Flag CUDA/Metal/Vulkan/etc. changes bundled into a feature's first PR.
+- Does it add a new `ggml_type` / quantization type? That carries a disproportionate maintenance burden and needs the full justification package (GGUF sample upload, perplexity vs FP16/BF16 and similar sizes, KL-divergence data, CPU perf numbers). Absent that, it will be rejected regardless of code quality.
+- Is it invasive - new subsystem, core-API reshaping, changes to shared graph/sampler code that other models don't need? Flag it and suggest a discussion with maintainers before investing further.
+- Is it niche/vendor-specific in a way that adds a maintenance burden nobody will own long-term? Flag the maintenance-ownership question.
+- Is the change semantically correct, or a plausible-looking "fix" that misunderstands the code? Sanity-check the actual behavior, not just that it compiles.
+- AI-disclosure: if AI meaningfully contributed, is the PR template's disclosure section filled in? Remind the user. Never suggest writing the PR description or commit message for them.
+
+## Security review (mandatory)
+
+Mandatory on every review; any finding here is **blocking**. Rule of thumb: GGUF metadata, tensor shapes, tokenizer/grammar input, and all server/RPC fields are attacker-controlled - bound them before use.
+
+- **Sizes/counts from tensor dims:** validate before allocating. Products like `ne[i]*nb[i]`/nbytes can overflow on crafted dims into an undersized alloc then heap overflow. Overflow checks must run BEFORE the arithmetic they guard - padding/alignment macros wrap to 0 near `SIZE_MAX`, so a guard after the pad passes.
+- **GGUF strings/arrays:** cap declared lengths and element counts before using them to size a loop or buffer; validate element type and length before casting an array to a pointer or reading fixed indices (`[i+1]`, `[0..2]`).
+- **File-supplied counts indexing fixed arrays:** bound any count (e.g. layer/block count into a `LLAMA_MAX_*` array) before indexing; watch checks that only fire when an optional key is present.
+- **Bounds comparisons:** flag narrowing casts (`size_t`->`int32_t`) and signed/unsigned mixing that can bypass a length check and copy past a buffer.
+- **Parsed/derived indices:** range-check `stoi`/`atoi` results and catch parse throws; never use a default or derived token id (EOS/BOS/...) as an index without a bounds check.
+- **Reused/reserved buffers:** recheck bounds after a buffer is shrunk or reused; watch `reserve()` then index-by-assumed-size, and header fields read before their length is checked.
+- **Server JSON ints:** clamp client-supplied integers (token/discard counts, offsets) to non-negative and an upper bound before they reach index/pointer arithmetic.
+- **RPC-deserialized fields:** treat every field (type/buffer/data/ne/nb/op_params) as hostile - validate before use. Null/zero buffers skipping validation, attacker data pointers, out-of-range type indices, and negative strides sign-extending past a corner-only assert all give arbitrary read/write.
+- **Lifetime/UAF:** flag stored raw pointers to caller/temporary storage, cached pointers to buffers a later free releases, async ops whose source may drop before completion, and structures not invalidated on free/realloc. Null-check conditionally-built or "not required" tensors before dereferencing.
+
+## Approach and design (when a new component/infra is introduced)
+
+Run this whenever the diff adds a new component, subsystem, or piece of infrastructure. Reviews too often stop at "does it work" - a diff can be correct and still be the wrong approach, and a messy design costs more long-term than a bug. Evaluate the *approach*, not just the behavior; raising a cleaner one is a high-value finding, not a nit. If you see a better design, describe it concretely rather than just calling the current one bad.
+
+- **Simpler approach upstream:** the biggest win is often a different data model or design that removes whole subsystems, not tweaks to the code as written. Complexity must be justified by the problem, not by the first thing that worked.
+- **Reuse over reinvention:** grep for an existing helper, library, object, or mechanism before adding a new one. Reimplementing what the codebase already has reintroduces solved bugs and adds maintenance surface.
+- **Clear ownership/lifetime:** prefer RAII and obvious ownership over manual liveness flags, hand-tracked pointers, and "is it still alive?" checks - manual lifetime tracking is a recurring source of subtle bugs.
+- **Right-sized machinery:** flag redundant, overkill, or heavier-than-needed primitives and abstractions; use the minimum the design actually needs.
+- **Right structure and fit:** a new type should earn its place (split it if it serves two roles); follow existing patterns, idioms, and naming, and avoid constructs the project shuns.
+- **Root cause vs symptom:** fixes layered on fixes signal a design to correct, not guard around.
+
+## New model / architecture
+
+See the `add-new-model` skill and `docs/development/HOWTO-add-model.md` for the full workflow; this is the review-time subset that reviewers most often catch:
+
+- Don't branch on `model.arch` when the real dependency is a config/capability value - gate on the hparam/capability, not the architecture enum.
+- If the model is a close variant of an existing arch, is the delta justified? Prefer reusing or subclassing the existing arch/model class over duplicating it. A near-duplicate class or `src/models/<name>.cpp` will be asked to merge with its sibling.
+- New tensor names go through `tensor_mapping.py`, not ad-hoc name matching.
+- For QKV, split the *activation* with `ggml_view`, not the *weight* tensor; rely on ggml broadcasting instead of manually duplicating tensors.
+- New graph inputs are declared at the top of the graph-build function, not inline where first used.
+- Hparams that the model can't run correctly without must be mandatory (hard-error if missing), not read with a silent default fallback. Only genuinely-optional-across-configs values get a fallback accessor.
+- New/optional weight tensors (scales, etc.) must route through `build_lora_mm` and the existing helpers, matching convention - don't leave raw matmuls copied from another arch.
+- Don't hack RoPE with a custom sin/cos implementation. If `ggml_rope_ext` genuinely can't express it, that's an issue for discussion, not a PR.
+- Test the quantized-KV path (`-ctk`/`-ctv q8_0`), not just default f16 - new speculative/attention features silently break there.
+- Preserve existing explanatory comments about model-specific quirks when copying code; note the provenance ("copied from X, with Y added").
+- Remove dead code/branches left over from adapting a reference implementation.
+
+## ggml / backend
+
+- `supports_op` (and any dispatch/gating condition) must be scoped exactly to the cases being changed - a condition meant for a few quant types must not silently disable or enable everything else.
+- No hardcoded warp/lane size - use `ggml_cuda_get_physical_warp_size()` (32 on CUDA, 64 on HIP/ROCm) and the portable helpers.
+- Strip leftover debug/profiling/logging code before review.
+- New or changed op? Update `docs/ops.md` and the relevant `docs/ops/*.csv` for the touched backend.
+- New op or operator change needs corresponding `test-backend-ops` cases, and (per `CONTRIBUTING.md`) consistency across at least two backends.
+- New kernels are expected to come with concrete perf data (throughput across realistic tensor shapes), not just correctness.
+- Don't have a backend mutate the cgraph as a shortcut - that's an unresolved architectural question, not something to slip in.
+- Expect this to need two maintainer approvals; that's normal for `ggml/` changes, not a sign something is wrong.
+- For CUDA: Avoid excessively templating kernels, only add this where it shows visible performance gain.
+
+## Public API (`include/llama.h`)
+
+Public API changes carry a higher bar than internal ones (`CONTRIBUTING.md`). Review for:
+
+- Justification: why doesn't an existing mechanism (e.g. `cb_eval`, existing batch/sampler knobs) suffice? If it does, the change likely shouldn't add public surface. This is the single most common reason these PRs are rejected.
+- Experimental or stop-gap surface belongs in a side header (`llama-ext.h`), not in `llama.h`.
+- Keep it minimal and general: prefer one general call over several narrow convenience wrappers; make new calls forward-compatible (e.g. mixed-modality batches) rather than assuming today's shape.
+- The C API is the first-class, stable, ABI-defining surface - don't propose a parallel C++ API as a replacement. `llama-cpp.h` stays a thin convenience layer.
+- Types and naming: sized integer types (`int32_t`, `size_t` for sizes/offsets); `snake_case`; `<class>_<method>` = `<class>_<action>_<noun>`; enum values upper-case and prefixed with the enum name; `_t` suffix for opaque types. Avoid gratuitous signature/ABI changes to existing exported functions.
+- Every new API needs a working example/tool exercising it in the same PR - reviewers find real bugs by requiring it to be wired into `server`, `embedding`, `perplexity`, etc.
+
+## Server (`tools/server/`)
+
+- Is the feature within server's defined scope? Check `tools/server/README-dev.md` - out-of-scope features get declined.
+- Security: don't trust client-supplied headers (e.g. `X-Forwarded-For`) or add footguns; things like IP allowlisting belong at a reverse proxy unless there's a trusted-proxy design.
+- Wire new behavior into the existing request/response and checkpoint paths correctly; watch for resource leaks across requests.
+
+## Multimodal (`tools/mtmd/`)
+
+- Tensor names must be prefixed by `v.`, `a.`, `mm.` or `a.mm.` (legacy naming doesn't follow this convention - this is expected, but new code should follow it).
+- Do not use explicit sin/cos for RoPE; use `ggml_rope_ext` instead, see `HOWTO-add-model.md`. If it can't express the needed behavior, that's a design discussion, not a PR.
+- New GGML ops must not be introduced in the same PR, you must push it as a separate PR.
+- In most cases, `build_vit` should be enough to build the transformer graph for vision models. Do not add a loop to build the transformer graph manually, unless you have a very good reason to do so. If you do, please explain why in the PR description.
+- If you need a dedicated preprocessor, there is a high chance that it can be a derived class from one of the existing preprocessors. Check carefully before adding a new preprocessor class.
+- If the model need a new public API in `mtmd.h`, open a discussion first.
+
+## General (always)
+
+Enforce the `AGENTS.md` / `CONTRIBUTING.md` coding and naming guidelines on every changed line - this is a distinct pass from checking that the code works, and matters just as much for review speed:
+
+- ASCII only in code and comments - no emdash, unicode arrows, `x`, `...` used as unicode; use `-`, `->`, `x`, `...` ASCII equivalents.
+- Comments are concise and explain non-obvious *why*, not *what*. Flag verbose comments, comments that restate the code, comments that reference the current task/PR, and comments hard-wrapped to a fixed column width.
+- Do not force-wrap prose/comments to a fixed character count or split a sentence across lines.
+- `snake_case` names; `kebab-case` (lowercase-with-dashes) file names for C/C++, `.h` headers; Python files lowercase-with-underscores. Naming optimizes for longest common prefix (`number_small`, not `small_number`).
+- 4-space indentation, brackets on the same line, `void * ptr`, `int & a`, no trailing whitespace; match the surrounding style.
+- Reuse existing infrastructure over introducing new components; no new third-party dependencies, extra headers, or files unless clearly justified.
+- Keep it simple: a simpler change doing 90% is often preferable to a complex one doing 100%. Flag unnecessary templates/fancy STL; basic `for` loops are fine here.
+- Every added line should be something the contributor can explain and defend to a reviewer without AI help - flag anything that looks copied-in without understanding.
+
+## Reporting
+
+Group findings by severity so the user knows what actually blocks a merge:
+
+1. **Blocking** - quick-reject/scope issues and correctness bugs; these can sink the PR regardless of everything else.
+2. **Will slow the review** - convention/naming/comment violations, missing tests/docs/perf data, missing API justification or example.
+3. **Nits** - minor style, optional cleanups.
+
+For each finding, point to the file and line and say concretely what to change and why. Do not rewrite the whole diff unprompted; let the contributor make the fixes so they own and understand them. And do not draft any PR text, commit message, or reviewer reply - that is the contributor's to write.