kano-agent-backlog-skill
Local-first backlog workflow. Use when planning work, creating/updating backlog items, writing ADRs, enforcing Ready gate, generating views, or maintaining derived indexes (SQLite/FTS/embeddings).
Best use case
kano-agent-backlog-skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Local-first backlog workflow. Use when planning work, creating/updating backlog items, writing ADRs, enforcing Ready gate, generating views, or maintaining derived indexes (SQLite/FTS/embeddings).
Teams using kano-agent-backlog-skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/kano-agent-backlog-skill-majiayu000/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How kano-agent-backlog-skill Compares
| Feature / Agent | kano-agent-backlog-skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Local-first backlog workflow. Use when planning work, creating/updating backlog items, writing ADRs, enforcing Ready gate, generating views, or maintaining derived indexes (SQLite/FTS/embeddings).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Kano Agent Backlog Skill (local-first)
## Scope
Use this skill to:
- Plan new work by creating backlog items before code changes.
- Maintain hierarchy and relationships via `parent` links, as defined by the active process profile.
- Record decisions with ADRs and link them to items.
- Keep a durable, append-only worklog for project evolution.
## Agent compatibility: read the whole skill
- Always load the entire `SKILL.md` before acting; some agent shells only fetch the first ~100 lines by default.
- If your client truncates, fetch in chunks (e.g., lines 1-200, 200-400, …) until you see the footer marker `END_OF_SKILL_SENTINEL`.
- If you cannot confirm the footer marker, stop and ask for help; do not proceed with partial rules.
- When generating per-agent guides, preserve this read-all requirement so downstream agents stay in sync.
## Non-negotiables
- Planning before coding: create/update items and meet the Ready gate before making code changes.
- Worklog is append-only; never rewrite history.
- Update Worklog whenever:
- a discussion produces a clear decision or direction,
- an item state changes,
- scope/approach changes,
- or an ADR is created/linked.
- Archive by view: hide `Done`/`Dropped` items in views by default; do not move files unless explicitly requested.
- Backlog volume control:
- Only create items for work that changes code or design decisions.
- Avoid new items for exploratory discussion; record in existing Worklog instead.
- Keep Tasks/Bugs sized for a single focused session.
- Avoid ADRs unless a real architectural trade-off is made.
- Ticketing threshold (agent-decided):
- Open a new Task/Bug when you will change code/docs/views/scripts.
- Open an ADR (and link it) when a real trade-off or direction change is decided.
- Otherwise, record the discussion in an existing Worklog; ask if unsure.
- Ticket type selection (keep it lightweight):
- Epic: multi-release or multi-team milestone spanning multiple Features.
- Feature: a new capability that delivers multiple UserStories.
- UserStory: a single user-facing outcome that requires multiple Tasks.
- Task: a single focused implementation or doc change (typically one session).
- Example: "End-to-end embedding pipeline" = Epic; "Pluggable vector backend" = Feature; "MVP chunking pipeline" = UserStory; "Implement tokenizer adapter" = Task.
- Bug vs Task triage (when fixing behavior):
- If you are correcting a behavior that was previously marked `Done` and the behavior violates the original intent/acceptance (defect or regression), open a **Bug** and link it to the original item.
- If the change is a new requirement/scope change beyond the original acceptance, open a **Task/UserStory** (or Feature) instead, and link it for traceability.
- Bug origin tracing (when diagnosing a defect/regression):
- Record **when the issue started** and the **evidence path** you used to determine it.
- Prefer VCS-backed evidence when available:
- last-known-good revision (commit hash or tag)
- first-known-bad revision (commit hash or tag)
- suspected introducing change(s) (commit hash) and why (e.g., `git blame` on specific lines)
- If git history is unavailable (zip export, shallow clone, missing remote), explicitly record that limitation and what alternative evidence you used (e.g., release notes, timestamps, reproduction reports).
- Keep evidence lightweight: record commit hashes + 1–2 line summaries; avoid pasting large diffs into Worklog. Attach artifacts when needed.
- Suggested Worklog template:
- `Bug origin: last_good=<sha|tag>, first_bad=<sha|tag>, suspect=<sha> (reason: blame <path>:<line>), evidence=<git log/blame/bisect|other>`
- State ownership: the agent decides when to move items to InProgress or Done; humans observe and can add context.
- State semantics: Proposed = needs discovery/confirmation; Planned = approved but not started; Ready gate applies before start.
- Hierarchy is in frontmatter links, not folder nesting; avoid moving files to reflect scope changes.
- Filenames stay stable; use ASCII slugs.
- Never include secrets in backlog files or logs.
- Language: backlog and documentation content must be English-only (no CJK), to keep parsing and cross-agent collaboration deterministic.
- Agent Identity: In Worklog and audit logs, use your own identity (e.g., `[agent=antigravity]`), never copy `[agent=codex]` blindly.
- Always provide an explicit `--agent` value for auditability (some commands currently default to `cli`, but do not rely on it).
- Model attribution (optional but preferred): provide `--model <name>` (or env `KANO_AGENT_MODEL` / `KANO_MODEL`) when it is known deterministically.
- Do not guess model names; if unknown, omit the `[model=...]` segment.
- **Agent Identity Protocol**: Supply `--agent <ID>` with your real product name (e.g., `cursor`, `copilot`, `windsurf`, `antigravity`).
- **Forbidden (Placeholders)**: `auto`, `user`, `assistant`, `<AGENT_NAME>`, `$AGENT_NAME`.
- File operations for backlog/skill artifacts must go through the `kano-backlog` CLI
(`python skills/kano-agent-backlog-skill/scripts/kano-backlog <command>`) so audit logs capture the action.
- Skill scripts only operate on paths under `_kano/backlog/` or `_kano/backlog_sandbox/`;
refuse other paths.
- After modifying backlog items, refresh the plain Markdown views immediately using
`python skills/kano-agent-backlog-skill/scripts/kano-backlog view refresh --agent <agent-id> --backlog-root <path>` so the dashboards stay current.
- Persona summaries/reports are available via `python skills/kano-agent-backlog-skill/scripts/kano-backlog admin persona summary|report ...`.
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem update-state ...` auto-syncs parent states forward-only by default; use `--no-sync-parent`
for manual re-plans where parent state should stay put.
- Add Obsidian `[[wikilink]]` references in the body (e.g., a `## Links` section) so Graph/backlinks work; frontmatter alone does not create graph edges.
## Agent compatibility: read the whole skill
- Always load the entire `SKILL.md` before acting; some agent shells only fetch the first ~100 lines by default.
- If your client truncates, fetch in chunks (e.g., lines 1-200, 200-400, …) until you see the footer marker `END_OF_SKILL_SENTINEL`.
- If you cannot confirm the footer marker, stop and ask for help; do not proceed with partial rules.
- When generating per-agent guides, preserve this read-all requirement so downstream agents stay in sync.
## First-run bootstrap (prereqs + initialization)
Before using this skill in a repo, the agent must confirm:
1) Python prerequisites are available (or install them), and
2) the backlog scaffold exists for the target product/root.
If the backlog structure is missing, propose the bootstrap commands and wait for user approval before writing files.
### Developer vs user mode (where to declare it)
- **Preferred source of truth**: product config in `_kano/backlog/products/<product>/_config/config.json`.
- `mode.skill_developer`: `true` when this repo actively develops the skill itself (this demo repo).
- `mode.persona`: optional string describing the primary human persona (e.g. `developer`, `pm`, `qa`), used only for human-facing summaries/views.
- **Secondary**: agent guide files (e.g., `AGENTS.md` / `CLAUDE.md`) can document expectations, but are agent-specific and not script-readable.
### Skill developer gate (architecture compliance)
**If `mode.skill_developer=true`**, before writing any skill code (in `scripts/` or `src/`), you **must**:
1. Read **ADR-0013** ("Codebase Architecture and Module Boundaries") in the product decisions folder.
2. Follow the folder rules defined in ADR-0013:
- `scripts/` is **executable-only**: no reusable module code.
- `src/` is **import-only**: core logic lives here, never executed directly.
- All agent-callable operations go through `scripts/kano-backlog` CLI.
3. Place new code in the correct package:
- Models/config/errors → `src/kano_backlog_core/`
- Use-cases (create/update/view) → `src/kano_backlog_ops/`
- Storage backends → `src/kano_backlog_adapters/`
- CLI commands → `src/kano_backlog_cli/commands/`
Violating these boundaries will be flagged in code review.
### Prerequisite install (Python)
Detect:
- Run `python skills/kano-agent-backlog-skill/scripts/kano-backlog doctor --format plain`.
If packages are missing, install once (recommended):
- **Default**: `python -m pip install -e skills/kano-agent-backlog-skill`
- **Skill contributors**: `python -m pip install -e skills/kano-agent-backlog-skill[dev]`
- Optional heavy dependencies (FAISS, sentence-transformers) should be installed manually per platform requirements before running the CLI against embedding features.
### Backlog initialization (file scaffold + config + dashboards)
Detect (multi-product / platform layout):
- Product initialized if `_kano/backlog/products/<product>/_config/config.json` exists (or confirm via `python skills/kano-agent-backlog-skill/scripts/kano-backlog doctor --product <product>`).
Bootstrap:
- Run `python skills/kano-agent-backlog-skill/scripts/kano-backlog admin init --product <product> --agent <agent-id> [--backlog-root <path>]` to scaffold `_kano/backlog/products/<product>/` (items/, decisions/, views/, `_config/`, `_meta/`, `_index/`).
- The init command derives a project prefix, writes `_config/config.json`, and refreshes dashboards so views exist immediately after initialization.
- Manual fallback (only if automation is unavailable): follow `_kano/backlog/README.md` to copy the template scaffold, then refresh views via `kano-backlog view refresh`.
## Optional LLM analysis over deterministic reports
This skill can optionally append an LLM-generated analysis to a deterministic report.
The deterministic report is the SSOT; analysis is treated as a derived artifact.
- Deterministic report: `views/Report_<persona>.md`
- Derived LLM output: `views/_analysis/Report_<persona>_LLM.md` (gitignored by default)
- Deterministic prompt artifact: `views/_analysis/Report_<persona>_analysis_prompt.md`
Enable by config (per product):
- `analysis.llm.enabled = true`
Execution:
- The **default workflow** is: generate the deterministic report → use it as SSOT → fill in the analysis template.
- The skill generates a deterministic prompt file to guide the analysis, and a derived markdown file with placeholder headings.
- Optional automation: when `analysis.llm.enabled = true` in config, view refresh generates `views/snapshots/_analysis/Report_<persona>_analysis_prompt.md` (deterministic prompt) and `Report_<persona>_LLM.md` (template or LLM output)
- Never pass API keys as CLI args; keep secrets in env vars to avoid leaking into audit logs.
## ID prefix derivation
- Source of truth:
- Product config: `_kano/backlog/products/<product>/_config/config.toml` (`product.name`, `product.prefix`), or
- Repo config (single-product): `_kano/backlog/_config/config.toml` (`product.name`, `product.prefix`).
- Derivation:
- Split `product.name` on non-alphanumeric separators and camel-case boundaries.
- Take the first letter of each segment.
- If only one letter, take the first letter plus the next consonant (A/E/I/O/U skipped).
- If still short, use the first two letters.
- Uppercase the result.
- Example: `product.name=kano-agent-backlog-skill-demo` -> `KABSD`.
## Recommended layout
This skill supports both single-product and multi-product layouts:
- Single-product (repo-level): `_kano/backlog/`
- Multi-product (monorepo): `_kano/backlog/products/<product>/`
Within each backlog root:
- `_meta/` (schema, conventions)
- `items/<type>/<bucket>/` (work items)
- `decisions/` (ADR files)
- `views/` (dashboards / generated Markdown)
## Item bucket folders (per 100)
- Store items under `_kano/backlog/items/<type>/<bucket>/`.
- Bucket names use 4 digits for the lower bound of each 100 range.
- Example: `0000`, `0100`, `0200`, `0300`, ...
- Example path:
- `_kano/backlog/items/task/0000/KABSD-TSK-0007_define-secret-provider-validation.md`
## Index/MOC files
- For Epic, create an adjacent index file:
- `<ID>_<slug>.index.md`
- Index files should render a tree using Dataview/DataviewJS and rely on `parent` links.
- Track epic index files in `_kano/backlog/_meta/indexes.md` (type, item_id, index_file, updated, notes).
## References
- Reference index: `REFERENCE.md`
- Schema and rules: `references/schema.md`
- Templates: `references/templates.md`
- Workflow SOP: `references/workflow.md`
- View patterns: `references/views.md`
- Obsidian Bases (plugin-free): `references/bases.md`
- Context Graph + Graph-assisted retrieval: `references/context_graph.md`
If the backlog structure is missing, propose creation and wait for user approval before writing files.
## Kano CLI entrypoints (current surface)
`scripts/` exposes a single executable: `scripts/kano-backlog`. The CLI is intentionally organized as nested command groups so agents can discover operations via `--help` on-demand (instead of hard-coding the full command surface into this skill).
### Help-driven discovery (preferred)
Run these in order, expanding only what you need:
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog --help`
- Shows top-level groups (e.g., `backlog`, `item`, `state`, `worklog`, `view`) and global options.
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog <group> --help`
- Shows subcommands for that group.
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog <group> <command> --help`
- Shows required args/options for that command.
Guideline: do not paste large `--help` output into chat; inspect it locally and run the command.
### Canonical examples (keep these few memorized)
- Bootstrap:
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog doctor --format plain`
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog admin init --product <name> --agent <id>`
- Daily workflow:
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem create --type task --title "..." --agent <id> --product <name>`
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem set-ready <item-id> --context "..." --goal "..." --approach "..." --acceptance-criteria "..." --risks "..." --product <name>`
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem validate <item-id> --product <name>`
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem update-state <item-ref> --state InProgress --agent <id> --message "..." --product <name>`
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem attach-artifact <item-id> --path <file> --shared --agent <id> --product <name> [--note "..."]`
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog view refresh --agent <id> --product <name>`
- Backlog integrity checks:
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog admin validate uids --product <name>`
## Conflict handling policy (configurable)
Use product config to control how duplicate IDs and UIDs are handled by maintenance commands
such as `admin links normalize-ids`.
- Config keys (product `_config/config.toml`):
- `conflict_policy.id_conflict`: default `rename` (rename duplicate IDs).
- `conflict_policy.uid_conflict`: default `trash_shorter` (move shorter duplicate content to `_trash/`).
- `trash_shorter` uses `_trash/<YYYYMMDD>/...` under the product root; items get a Worklog entry.
### Sandbox workflow (isolated experimentation)
For testing, prototyping, or demos without affecting production backlog:
- Create: `python skills/kano-agent-backlog-skill/scripts/kano-backlog admin sandbox init <sandbox-name> --product <source-product> --agent <id>`
- Use: `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem create --product <sandbox-name> ...` (same CLI, different product)
- Cleanup: `rm -rf _kano/backlog_sandbox/<sandbox-name>` (git will ignore this directory)
- Rationale: Sandboxes mirror production structure but live in `_kano/backlog_sandbox/`, so changes never leak into `_kano/backlog/`.
## Artifacts policy (local-first)
- Storage locations:
- Shared across products: `_kano/backlog/_shared/artifacts/<ITEM_ID>/` (use `--shared`).
- Product-local: `_kano/backlog/products/<product>/artifacts/<ITEM_ID>/` (use `--no-shared`).
- Usage:
- Attach via `workitem attach-artifact` — copies the file and appends a Worklog link.
- Prefer lightweight, text-first artifacts (Markdown, Mermaid, small images). Use Git LFS for large binaries if needed.
- Git policy:
- Commit human-readable artifacts that aid review. Avoid committing generated binaries unless justified.
- Sandboxes under `_kano/backlog_sandbox/` are gitignored; artifacts there are ephemeral.
- For derived analysis, store under `views/_analysis/` (gitignored by default), and keep deterministic reports in `views/`.
- Linking:
- The CLI appends a Markdown link relative to the item file. Optionally add a `## Links` section for richer context.
## State update helper
- Use `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem update-state ...` to update state + append Worklog.
- Prefer `--action` on `kano-backlog state transition` for the common transitions (`start`, `ready`, `review`, `done`, `block`, `drop`).
- Use `python skills/kano-agent-backlog-skill/scripts/kano-backlog workitem validate <item-id>` to check the Ready gate explicitly.
## Topic and Workset workflow (context management)
### When to use Topics
**Topics** are shareable context buffers for multi-step work that spans multiple work items or requires exploratory research before creating formal backlog items.
Use Topics when:
- Exploring a complex problem that may result in multiple work items
- Collecting code snippets, logs, and materials across multiple sessions
- Collaborating across agents/sessions with a shared context
- Refactoring work that requires tracking multiple code locations
**Topic lifecycle**:
1. **Create**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog topic create <topic-name> --agent <id>`
- Creates `_kano/backlog/topics/<topic>/` with `manifest.json`, `brief.md`, `brief.generated.md`, `notes.md`, and `materials/` subdirectories
2. **Collect materials**:
- Add items: `topic add <topic-name> --item <ITEM_ID>`
- Add code snippets: `topic add-snippet <topic-name> --file <path> --start <line> --end <line> --agent <id>`
- Pin docs: `topic pin <topic-name> --doc <path>`
3. **Distill**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog topic distill <topic-name>`
- Generates/overwrites deterministic `brief.generated.md` from collected materials
- `brief.md` is a stable, human-maintained brief (do not overwrite it automatically)
4. **Switch context**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog topic switch <topic-name> --agent <id>`
- Sets active topic (affects config overlays and workset behavior)
5. **Close**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog topic close <topic-name> --agent <id>`
- Marks topic as closed; eligible for TTL cleanup
6. **Cleanup**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog topic cleanup --ttl-days <N> [--dry-run]`
- Removes raw materials from closed topics older than TTL
**Topic snapshots (retention policy)**:
- Snapshots are intended for **milestone checkpoints** (pre-merge/split/restore, risky bulk edits), not every small edit.
- To prevent noise, keep only the **latest snapshot per topic** in this demo repo.
- After creating a snapshot (or periodically), prune all but the newest snapshot:
- `python skills/kano-agent-backlog-skill/scripts/kano-backlog topic snapshot cleanup <topic-name> --ttl-days 0 --keep-latest 1 --apply`
**Topic structure**:
```
_kano/backlog/topics/<topic>/
manifest.json # refs to items/docs/snippets, status, timestamps
brief.md # stable, human-maintained brief (do not overwrite automatically)
brief.generated.md # deterministic distilled brief (generated/overwritten by `topic distill`)
notes.md # freeform notes (backward compat)
materials/ # raw collection (gitignored by default)
clips/ # code snippet refs + cached text
links/ # urls / notes
extracts/ # extracted paragraphs
logs/ # build logs / command outputs
synthesis/ # intermediate drafts
publish/ # prepared write-backs (patches/ADRs)
config.toml # optional topic-specific config overrides
```
### When to use Worksets
**Worksets** are per-item working directories (cached, derived data) for a single backlog item.
Use Worksets when:
- Starting work on a specific Task/Bug/UserStory
- Need scratch space for deliverables (patches, test artifacts, etc.)
- Want item-specific config overrides (rare)
**Workset lifecycle**:
1. **Initialize**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog workset init <ITEM_ID> --agent <id> [--ttl-hours <N>]`
- Creates `_kano/backlog/.cache/worksets/items/<ITEM_ID>/` with `meta.json`, `plan.md`, `notes.md`, `deliverables/`
2. **Work**: Store scratch files in `deliverables/` (patches, test outputs, etc.)
3. **Refresh**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog workset refresh <ITEM_ID> --agent <id>`
- Updates `refreshed_at` timestamp
4. **Cleanup**: `python skills/kano-agent-backlog-skill/scripts/kano-backlog workset cleanup --ttl-hours <N> [--dry-run]`
- Removes stale worksets older than TTL
**Workset structure**:
```
_kano/backlog/.cache/worksets/items/<ITEM_ID>/
meta.json # workset metadata (item_id, agent, timestamps, ttl)
plan.md # execution plan template
notes.md # work notes with Decision: marker guidance
deliverables/ # scratch outputs (patches, logs, test artifacts)
config.toml # optional item-specific config overrides
```
### Topic vs Workset decision guide
| Scenario | Use Topic | Use Workset |
|----------|-----------|-------------|
| Exploring before creating items | ✅ Yes | ❌ No |
| Multi-item refactor | ✅ Yes | ❌ No |
| Collecting code snippets across files | ✅ Yes | ❌ No |
| Shared context for collaboration | ✅ Yes | ❌ No |
| Single item scratch space | ❌ No | ✅ Yes |
| Item-specific deliverables | ❌ No | ✅ Yes |
| Version-controlled distillation | ✅ Yes (brief.generated.md) | ❌ No |
**Best practice**: Start exploration in a Topic, create work items as scope clarifies, then use Worksets for individual item execution.
### Active topic and config overlays
- Active topic is per-agent: `_kano/backlog/.cache/worksets/active_topic.<agent>.txt`
- When an agent has an active topic, config resolution includes topic overrides:
- Layer order: defaults → product → **topic** → workset → runtime
- Topic config: `_kano/backlog/topics/<topic>/config.toml`
- Use for temporary overrides (e.g., switch `default_product` during exploration)
- Get active topic: `python skills/kano-agent-backlog-skill/scripts/kano-backlog topic show --agent <id>`
### Materials buffer (Topic-specific)
- **Reference-first snippet collection**: Avoid large copy-paste; store file+line+hash+optional snapshot
- **Snippet refs** include:
- `file`: relative path from workspace root
- `lines`: `[start, end]` (1-based inclusive)
- `hash`: `sha256:...` of content for staleness check
- `cached_text`: optional snapshot (use `--snapshot` to include)
- `revision`: git commit hash if available
### Human decision materials vs. machine manifest
**Dual-Readability Design**: Every artifact checks against both human and agent readability:
- **Human-Readable**: High-level summaries, clear checklists, "manager-friendly" reports for rapid decision-making
- **Agent-Readable**: Structural precision, file paths, line numbers, explicit markers for action without hallucination
**Implementation in Topics**:
- Treat `manifest.json` as **machine-oriented** metadata:
- `seed_items`: UUID list for precise agent reference
- `snippet_refs`: file+line+hash for deterministic loading
- `pinned_docs`: absolute paths for unambiguous reference
- Keep `brief.generated.md` **deterministic** and **tool-owned** (generated/overwritten by `topic distill`):
- Readable item titles (e.g., "KABSD-TSK-0042: Implement tokenizer adapter")
- If available, include item path and keep UID in a hidden HTML comment for deterministic mapping
- Materials index with items/docs/snippets sorted for repeatability
- Keep `brief.md` **human-oriented** and **stable** (do not overwrite automatically):
- Context summary and key decisions
- Optional: include a human-friendly materials list (do not duplicate raw snippet text)
- Put human-facing decision support in `_kano/backlog/topics/<topic>/notes.md` (and/or pinned docs), e.g.:
- Decision to make
- Options + trade-offs
- Evidence (ADR links, snippet refs, benchmark/log artifacts)
- Recommendation + follow-ups
- **Staleness detection**: Compare current file hash with stored hash to detect if code changed
- **Distillation**: `topic distill` generates deterministic `brief.generated.md` with a repeatable materials index
---
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Test MCP servers for quality and reliability. Verify tool functionality, test error handling, generate tests, and assess response quality with no dependencies other than curl. Use this when validating MCP server implementations, testing OpenAPI-to-MCP conversions, or assessing API tool quality.
mcp-repo-scan
Comprehensive RE-Engine repository health audit, issue resolution, and architectural enhancement through systematic codebase analysis
mcp-patterns
MCP server building, advanced patterns, and security hardening. Use when building MCP servers, implementing tool handlers, adding authentication, creating interactive UIs, hardening MCP security, or debugging MCP integrations.
MCP Integration
Model Context Protocol (MCP) integration specialist. Use when creating MCP server configurations, implementing MCP integrations, or optimizing MCP performance. Specializes in MCP server architecture and integration patterns.
mcp-development
Model Context Protocol (MCP) server development and AI/ML integration patterns. Covers MCP server implementation, tool design, resource handling, and LLM integration best practices. Use when developing MCP servers, creating AI tools, integrating with LLMs, or when asking about MCP protocol, prompt engineering, or AI system architecture.
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
MATLAB离散点Hough变换直线检测
实现离散点Hough变换算法,输入包含浮点数的坐标点,输出检测到的直线斜率和截距,不使用MATLAB自带函数
mastra
Comprehensive Mastra framework guide. Teaches how to find current documentation, verify API signatures, and build agents and workflows. Covers documentation lookup strategies (embedded docs, remote docs), core concepts (agents vs workflows, tools, memory, RAG), TypeScript requirements, and common patterns. Use this skill for all Mastra development to ensure you're using current APIs from the installed version or latest documentation.
mastra-dev
Ultimate Mastra Framework development toolkit for agent orchestration, workflow design, and MCP integration. Use when Codex should run the converted mastra-dev workflow. Inputs: command, options.