One-line definitions of development-harness (dh) plugin terminology — RT-ICA, ARL, SAM, the S1-S7 pipeline stage names, Impact Radius — each with a pointer to its canonical source file. Use when a dh skill, agent, or workflow step references a term without defining it, before guessing what the term means from its observed outputs, or when asked what a dh concept or acronym stands for.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
A mandate that names a dh concept states only the name, not the method — the orchestrator sees the outcome (a verdict, a gate, a report) and can misinfer the mechanism from watching what it does. Load this skill instead of inferring; each entry states the method, not just the outcome, and points to the file that is authoritative if this entry ever drifts from it.
RT-ICA (Reverse Thinking - Information Completeness Assessment) — works backward from the
stated goal through its prerequisite chain: what must exist to reach the goal, then what each of
those things requires, recursively back to the current state. Classifies each prerequisite as
AVAILABLE, DERIVABLE, or MISSING. dh:rt-ica blocks planning on any MISSING condition (S2
implementation gate); dh:planner-rt-ica is the non-blocking sister used during grooming, which
localizes a MISSING condition to the affected task instead of halting. Canonical: load the
dh:rt-ica skill.
ARL (Autonomous Refinement Loop) — research into the prerequisites for autonomous agent
execution without synchronous human blocking gates: failure categories, and the conditions under
which a machine-verifiable check can replace human judgment. dh's human-touchpoint model is
ARL-derived — see the S1-S7 entry below. Canonical: load the plugin-creator:arl skill.
SAM (Stateless Agent Methodology) — the 7-stage development pipeline this harness implements
(S1-S7, below). "Stateless" means each stage's state lives in a provider-owned artifact, never in
conversation history — a stage reads its input from the prior stage's registered artifact, not
from what an agent remembers saying earlier. Canonical: load the dh:dh-meta-docs skill.
S1-S7 — the SAM pipeline stage names, in order: S1 Discovery (understand the feature,
codebase, constraints) → S2 Planning + RT-ICA (generate a plan, gated by dh:rt-ica) → S3
Context Integration (validate the plan against actual codebase state) → S4 Task
Decomposition (break the plan into executable tasks) → S5 Execution (implement tasks via
language-specific specialists) → S6 Forensic Review (verify each task against its acceptance
criteria) → S7 Final Verification (certify the feature meets the original requirements). Not
every stage requires human review — ARL-derived constraint analysis decides when to escalate, not
a fixed checkpoint per stage. Canonical: load the dh:dh-meta-docs skill.
Impact Radius — the backlog-item section whose Systems Inventory records the estimated impact
set across software, data, runtime, prompts, controls, people, and processes, with causal paths,
risk, verification, exclusions, and owned unknowns. It is written by @dh:impact-analyst before
planning; the feasibility and staleness gates consume it. Older items may hold a file-centric
version under Impact Radius or Resources; consumers retain that fallback. Canonical:
plugins/development-harness/agents/impact-analyst.md.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.
日本語の概要は準備中です。原文の説明を表示しています。
SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into an executable SAM plan.
日本語の概要は準備中です。原文の説明を表示しています。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
日本語の概要は準備中です。原文の説明を表示しています。
Browser automation for AI agents using the agent-browser CLI and Playwright. Use when navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, logging into sites, or automating any browser task. Triggers on "open a website", "fill out a form", "click a button", "scrape data", "test this web app", "automate browser actions", or any programmatic web interaction request.
日本語の概要は準備中です。原文の説明を表示しています。
Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Create or adapt Claude Code agent definitions. Use when creating an agent, changing subagent configuration, selecting agent scope, or designing a specialized delegation role.
日本語の概要は準備中です。原文の説明を表示しています。