A workflow in which separate coding agents implement and review individual tasks, while another review checks the overall plan. Agents exchange their work through files and a progress record.

Use it after a plan is locked and work has been split into mostly independent tasks, especially when implementation, task review, progress tracking, and final plan review are needed.

It reduces the risk that one coding session misses requirements or carries too much unrelated context. Fresh reviewers provide independent checks of each task and the complete change.

mstar-sdd — Skill for Claude Code by btspoony

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add btspoony/mstar-harness --skill mstar-sdd

Clone the repo

git clone --depth 1 https://github.com/btspoony/mstar-harness

Made for: Claude Code.

Or install morning-star-harness, the plugin that ships this one along with the rest of its 23 skills, 5 commands, 14 agents, 2 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/btspoony/mstar-harness/mstar-sdd/github.svg)](https://agentmods.dev/skills/btspoony/mstar-harness/mstar-sdd)

Your own site

<a href="https://agentmods.dev/skills/btspoony/mstar-harness/mstar-sdd"><img src="https://agentmods.dev/badge/skills/btspoony/mstar-harness/mstar-sdd/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/btspoony/mstar-harness/mstar-sdd"><img src="https://agentmods.dev/badge/skills/btspoony/mstar-harness/mstar-sdd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 146 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,319 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium Excessive Agency · line 91
Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00146	$0.02319
Opus 5	$0.00073	$0.01159
Sonnet 5	$0.00029	$0.00464
Haiku 4.5	$0.00015	$0.00232

Measured 11d ago against content hash 1d872c6feb0c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

mstar-sdd scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/mstar-sdd/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Load order

Before first Read: mstar-harness-core → mstar-dispatch-gates. Path symbols → mstar-conventions ({SDD_DIR}). Plan QC after SDD → mstar-review-qc. On conflict, mstar-harness-core wins.

When to use

Plan locked; tasks mostly independent; PM orchestrates in-session
Assignment has Execution mode: sdd
Not for hotfix inline work (Execution mode: inline) or leaf self-dispatch

Core principle

Default: fresh implementer subagent per task + task review (spec + quality) + plan-level QC on whole branch = quality with isolated context.

Optional: SDD implementer session: sticky — same implementer subagent across sequential tasks on one plan/branch; task reviewers stay fresh per task. SSOT → references/sticky-implementer-session.md.

Engine check (when available): import implementerSessionStickyRules from @mstar-harness/engine in a host hook to validate the sticky resume decision above (no CLI form yet). On fail -> do not proceed; fix and re-run. Skill text below remains authoritative when the runtime is absent.

Narration: at most one short line between tool calls — ledger and file paths carry the record.

Continuous execution: do not check in with the human between tasks. Stop only for BLOCKED, genuine ambiguity, or all tasks complete.

Pre-flight plan scan

Before Task 1, scan plan once for:

tasks contradicting Global Constraints
plan-mandated items that review rubric would flag as defects

Batch all findings for the human in one message. If clean, proceed silently.

Per-task loop (PM only · Workflow)

Record BASE_SHA (never use HEAD~1 later)
mstar sdd workspace <plan-id> → SDD_DIR（iteration L1 从 feature cwd 调用时：MSTAR_CONTROL_ROOT=<control_worktree_path> 或 mstar sdd workspace <plan-id> <control_worktree_path>；probe 以 v2 根 status.json（workflows[]）或 workflow snapshot 存在为准，linked worktree 缺文件会 fail closed）
mstar sdd task-brief <plan> N → brief file
Dispatch implementer:
- SDD implementer session: fresh (default) — new subagent; templates: references/implementer-prompt.md
- SDD implementer session: sticky — first task: same as fresh + write {SDD_DIR}/implementer-session.json with host_agent_id; later tasks: host resume + references/implementer-continuation-prompt.md (see references/sticky-implementer-session.md)
On DONE: mstar sdd review-package BASE HEAD → diff file
Dispatch fresh task reviewer — role code-reviewer (L2; not qc-specialist*; host fallback generic + C5b → mstar-host C5) — brief, report, diff, Global Constraints — references/task-reviewer-prompt.md — never sticky resume for reviewers
Fix loop for Critical/Important; re-review until approved
Append progress.md; update the workflow snapshot plan row (workflows/<id>/snapshot.json → plans[]) task_commits[] and implementer-session.json last_task if sticky
Next task

Read the full file on GitHub · 138 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

11d ago First seen · 138 lines · 146 tokens per session scan A 1d872c6feb0c

Subscribe to this mod's changes

mstar-sdd is a skill published in the GitHub repository btspoony/mstar-harness (58 stars, last pushed today), licensed MIT. It adds 146 tokens to every session and 2,319 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Building agents harness-engineering knowledge-management opencode-plugin sdd spec-driven subagents

mstar-sdd

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

Load order

When to use

Core principle

Pre-flight plan scan

Per-task loop (PM only · Workflow)

What ships with it

What this file has done since we first saw it

Other skills, from other repositories

comet-native

comet-build

comet-classic

comet-open

comet-build

subagent-driven-development