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Cdd Report

Community
Oscaner
cdd-report

Analyzes the current CDD session for bugs and enhancement opportunities and files a single aggregate GitHub issue against Oscaner/skills via gh CLI. Open dedup matches become links in the issue body (never comments); closed matches are flagged as regression follow-ups, never reopened. Findings never auto-include branch names or paths. One-shot chain — no review self-loop. Repo development tool, not a regular workflow skill. Manual trigger only, never automatic.

Overview

PublisherOscaner
Repositoryskills
Skill namecdd-report
Stars
63
Forks
0
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Oscaner on GitHub. Read the source before you install it.

Installation

Install the Cdd Report AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Oscaner/skills/tree/main/packages/kairos/skills/cdd-report
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Oscaner/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/packages/kairos/skills/cdd-report .claude/skills/cdd-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cdd Report in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cdd Report on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cdd Report is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Kairos CDD-Report

Analyzes the current CDD session (session context records + {repo}/.kairos/cdd/*/progress.json ledgers + git log) and files one aggregate issue on Oscaner/skills via gh — or a links-only list when every confirmed finding dedup-matches an open issue — as a one-shot toolchain, manual trigger only.

Invocation discipline — every command runs as a background dispatch: launch it in the background and await the completion notification — never poll — then read the full output (stdout/stderr). Output filtering is forbidden — no piping to tail/head, no 2>&1 |, no EXIT=$? capture. One-shot toolchain — the flow is a single pass to the terminal (no review self-loop, no next: consumption); a rejected confirm or a failed create ends the chain with the finding preserved for manual retry. Failure face — cross-node failure handling lives in the Failure Modes table below (the single source); node Fail entries keep only local behavior.

Flow Digraph

Node Definitions

explore-current-session

  • Do: Capture the four-field Session Context snapshot once at report start (taken by this node; every downstream node derives from this snapshot, never from the live cwd) —
    • root: git top-level of the harness launch cwd (git rev-parse --show-toplevel). Captured once; never re-derived from the real-time cwd.
    • workspace: .kairos/cdd/<run-slug>/ of the current CDD run — present only when this session is a CDD run. Cross-repo reuse is forbidden: the workspace must resolve under root.
    • harness: the harness this session runs in (e.g. claude-code) — the value of the Session region's - Harness: row.
    • program-owning issue (CDD runs only): the phase-owning issue of the current program, resolved from the run workspace — progress.json#plan → that plan's slug: → the identity document sharing that slug, whose own attribution is the owning issue: a phase-spec names its overall through program: (the overall's Issue inventory owns the issue), while an overall or single-spec is its own chain root (its Issue inventory / issues: declares the issue). A pure lookup feeding the Related region's Program link, never a routing decision.
  • Read: harness launch cwd; (when CDD run) the run workspace
  • Exit: snapshot complete → collect
  • Fail: workspace cannot resolve under root → BLOCKED (cross-repo reuse forbidden)

collect

  • Do: Collect candidate findings from the session's reference sources — ① session context records: tool-call records / errors / handoff / review findings visible in this session; ② ledger: {repo}/.kairos/cdd/*/progress.json files — the JSON ledger read face: per run, read the waves[] rows' rounds{} records (the completed round counts per mode — implement/review/fix — and the failure counters) and surface findings from the runs whose records show fix rounds, blocked/failed reviews, or parked states; the retired text-line extraction no longer applies; ③ git log: git log $(git merge-base HEAD origin/main)..HEAD --oneline, falling back to a recent-scope log when origin/main is unavailable. These sources are described for reference, not as a fixed channel contract — in practice the surface flexes with the task; every candidate passes the toolchain scope filter regardless of how it surfaced. Apply the toolchain scope filter: a candidate enters the list only when it satisfies both —
    • component slot: the affected component is in the components enumeration (e.g. kairos:cdd-report(pi:/skill:cdd-report), cdd-engine);
    • behavior predicate: the finding touches a toolchain artifact — cdd command output / handoff / progress ledger / issue form / skill flow node. Consumer-project domain rules (e.g. a missing ruff config in the consumer project, a project-specific test flake) are rejected samples: component-free findings never enter the list. Redact secrets: replace any match of API_KEY=... / TOKEN=... / SECRET=... / PASSWORD=... with [REDACTED] before a finding leaves this node.
  • Read: session context records; {repo}/.kairos/cdd/*/progress.json; git log
  • Exit: filtered findings → reform
  • Fail: ledger / git log unavailable → use session context only (fail-open, never block)

reform

  • Do: Prepare each finding for filing — privacy strip (both directions: no consumer-identifiable data + a maintainer-reproducible mechanism) and maintainer-friendly formatting (readable problem statement, impact, suggested fix). Then derive the neutral topic: a short phrase from the first finding with type/component labels stripped, ≤ 60 chars — the base for the issue title. The title is the topic itself: no shell prefix and no run slug leak into it, so the title stays stable for consumers.
  • Read: collected findings
  • Exit: reformed findings + topic → confirm
  • Fail: no finding survives reform → BLOCKED (nothing to report)

confirm

  • Do: Human gate — present the N findings as a numbered list plus the recommended topic; the user may add, remove, or edit findings and confirm or replace the topic. Do not pre-create or pre-comment on any gh issue before explicit confirmation. Findings never include branch names, absolute paths, or filenames by default; such context is added only when the user opts in at this gate.
  • Read: reformed findings + topic
  • Exit: user confirms → dedup; user rejects → BLOCKED (user-reject)
  • Fail: no response / explicit rejection → BLOCKED (user-reject, flow terminates)

dedup

  • Do: Run single-pass dedup over the repository's recent issue history. Window: issues updated within the last 90 days — GitHub search accepts no relative duration, so materialize the window as an ISO absolute date: date -v-90d +%F → YYYY-MM-DD. Run one query, one network round-trip: gh issue list --repo Oscaner/skills --state all --limit 100 --search "updated:>=<now-90d-ISO>" --state all + --search covers open and closed issues in the same pass. Match each finding in memory, case-insensitively, by affected component + core behavior words + title/body keywords:
    • Open match → record the hit as #N (open): <component> · <reason> with a conditional landing: the issue body's Dedup region when the create-issue branch runs, or the links-only list when the gate routes everything to report-links-only. Never append a comment to, or edit, the matched issue.
    • Closed match → record Regression / follow-up of #N (closed) for the Related region; never reopen.
  • Read: the single gh issue list output; confirmed findings
  • Exit: dedup decisions complete → create-issue?
  • Fail: gh unavailable / network failure → fail-open (record stderr, keep finding for manual retry)

create-issue?

  • Do: Gate — if all confirmed findings matched an open issue in dedup, creating a new issue would be an empty issue → report-links-only. Otherwise (at least one unmatched new finding) → create-issue.
  • Read: dedup decisions
  • Exit: all findings open-dedup-matched → report-links-only; any new finding → create-issue
  • Fail: —

create-issue

  • Do: Compose and file the single aggregate issue.
    1. Render the body with the renderer CLI bare call: npx -y @oscaner-skills/cdd-engine@latest issue render reading stdin JSON { harness, findings, related } → aggregate body straight to stdout. The stdin contract: top-level harness (Session row value) / findings[] (non-empty) / optional related; per finding, type ∈ {bug, enhancement, chore} · lang ∈ {en, zh} · non-empty context/problem/impact/suggestedFix · meta{skill, step}. Input violations → exit 1 with the offending field path (e.g. findings[0].type: must be one of bug | enhancement | chore) — fix the stdin JSON and retry; never hand-assemble the body.
    2. Body layout (renderer-produced, deterministic): ## Session region with one - Harness: <harness> row; per finding, its four typed segments (section headings from the canonical issue-label oracle by type × lang) each followed by its 2-line report-meta - Skill: <skill> / - Step: <step> — position adjacency is the ownership declaration; single tail regions ## Dedup (all open hits) and ## Related (all closed hits + program ownership), never per-finding.
    3. Resolve labels — read the target repo's label surface once: gh label list --repo Oscaner/skills --limit 100. Create set = the default cdd-engine intersected with the labels the repo carries; a dropped default is recorded as a label deviation for report, and an empty intersection creates the issue with no label. Labels never block the create call.
    4. Create — gh issue create --repo Oscaner/skills --title <topic> --label <label> … with the rendered body. The flag is singular — --label, repeated once per resolved label; gh issue create has no --labels. Program ownership — the session's program-owning issue — is passed as related.program and renders as - Program: #N in the Related region.
  • Read: renderer CLI (bare call); the gh label list surface; confirmed findings + topic; dedup decisions; Session Context (harness / program-owning issue)
  • Exit: issue created → report
  • Fail: renderer rejects the stdin → exit 1 with field path (fix stdin, retry); the label pre-check fails → fail-open (create with the default set, record stderr); the create call fails → fail-open (record stderr, keep finding for manual retry)

report-links-only

  • Do: Create nothing — every confirmed finding already matched an open issue (no empty issue). Assemble the links-only list: each finding → matched issue number, with component and reason. No renderer run, no issue body.
  • Read: dedup decisions
  • Exit: links list complete → report
  • Fail: —

report

  • Do: Print the run summary — created issue URL (when create-issue ran) / the links-only list (when report-links-only ran); Regression / follow-up of #N (closed) notes with the closed issue number; the program ownership link; any label deviation the create-issue pre-check recorded; failed or skipped finding with reason.
  • Read: final action for each finding
  • Exit: summary presented → the APPROVED terminal
  • Fail: none (display only)

Invariants

#Invariant
I1Confirm Gate — no gh issue is created before explicit user confirmation (hard gate at confirm); dedup matches are recorded in the new issue body only, never acted on as comments on existing issues
I3Manual Trigger Only — cdd-report runs only on manual trigger, never automatically
I5Renderer Determinism — the issue body is produced by npx -y @oscaner-skills/cdd-engine@latest issue render as a bare call (stdin JSON → aggregate body → stdout, no mode flag, no hand-assembled paragraph structure in this skill)
I6Evidence Contract — findings never carry consumer-identifiable data (branch names, absolute paths, filenames, process counts, RSS values, launch dirs, session habits) — such context enters only on consumer opt-in at confirm — AND findings always describe a maintainer-reproducible mechanism (trigger conditions / mechanism / expected behavior / reproduction steps)
I8One-shot chain — the flow is a single pass to the terminal (no review self-loop, no next: consumption); a rejected confirm or a failed create terminates the chain with the finding preserved for manual retry
I9Label Safety — the create call carries the singular --label and only labels the repo's live label surface, resolved by the gh label list pre-check; a missing label shrinks the label set, never blocks the filing

Failure Modes

failurebehaviorreasonrecovery
User rejects filing (confirm rejected)BLOCKED (user-reject)no gh issue is created or commented on before confirmation (I1)flow terminates, nothing filed
gh CLI unavailable / network failurefail-open (record stderr, keep finding)external tool dependencymanual retry using the recorded stderr
renderer rejects the stdin JSONexit 1 + offending field path (early report)renderer input contract is strict — body is never hand-assembled (I5)fix the stdin JSON per the reported path, retry
a default label is absent, or the label pre-check failsauto-degrade (create with the surviving set, none when empty)labels are repo-side state, never a filing blockerissue is created; report prints the deviation
create call failsfail-open (record stderr, keep finding)title / body size / network / permissionsmanual retry, keep findings
no finding survives collect/reformBLOCKED (nothing to report)toolchain scope filter rejected everythingrevisit the session, re-run on manual trigger

Frequently asked questions

What does the Cdd Report AI skill do?

Analyzes the current CDD session for bugs and enhancement opportunities and files a single aggregate GitHub issue against Oscaner/skills via gh CLI. Open dedup matches become links in the issue body (never comments); closed matches are flagged as regression follow-ups, never reopened. Findings never auto-include branch names or paths. One-shot chain — no review self-loop. Repo development tool, not a regular workflow skill. Manual trigger only, never automatic.

Why use Cdd Report on TypingMind?

Because you install it once and use it with any model. Cdd Report is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cdd Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Oscaner/skills/tree/main/packages/kairos/skills/cdd-report. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cdd Report?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cdd Report?

As many as you like. As long as a model supports skills, you can use Cdd Report with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cdd Report AI skill free?

Yes. It is published on GitHub by Oscaner under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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