Provides a dashboard view of project progress and metadata.

Install

mkdir -p .claude/skills/gsd-stats && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12609" && unzip -o skill.zip -d .claude/skills/gsd-stats && rm skill.zip

Installs to .claude/skills/gsd-stats

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Display project statistics — phases, plans, requirements, git metrics, and timeline
83 charsno explicit “when” trigger
Beginner

Key capabilities

  • Display phase progress.
  • Show plan execution metrics.
  • Report requirements completion.
  • Present git history statistics.
  • Visualize project timeline.
  • Map user questions to `request_user_input` for interactive prompts.

How it works

The skill aggregates and displays various project statistics by translating GSD workflow commands into Codex-compatible actions. It uses `request_user_input` for interactive prompts and `spawn_agent` for tasks.

Inputs & outputs

You give it
user text mentioning '$gsd-stats' and optional arguments
You get back
complete project statistics including phase progress, plan execution metrics, requirements completion, git history stats, and project timeline

When to use gsd-stats

  • Checking project progress
  • Reviewing timeline status
  • Generating project metrics

About this skill

<codex_skill_adapter>

A. Skill Invocation

  • This skill is invoked by mentioning $gsd-stats.
  • Treat all user text after $gsd-stats as {{GSD_ARGS}}.
  • If no arguments are present, treat {{GSD_ARGS}} as empty.

B. AskUserQuestion → request_user_input Mapping

GSD workflows use AskUserQuestion (Claude Code syntax). Translate to Codex request_user_input:

Parameter mapping:

  • headerheader
  • questionquestion
  • Options formatted as "Label" — description{label: "Label", description: "description"}
  • Generate id from header: lowercase, replace spaces with underscores

Batched calls:

  • AskUserQuestion([q1, q2]) → single request_user_input with multiple entries in questions[]

Multi-select workaround:

  • Codex has no multiSelect. Use sequential single-selects, or present a numbered freeform list asking the user to enter comma-separated numbers.

Execute mode fallback:

  • When request_user_input is rejected or unavailable, activate TEXT_MODE: append --text to {{GSD_ARGS}} so the workflow's built-in text-mode branching takes over. Present every AskUserQuestion call as a plain-text numbered list, then stop and wait for the user's reply. Do NOT pick a default and continue (#3018 / #3808).
  • You may only proceed without a user answer when one of these is true: (a) the invocation included an explicit non-interactive flag (--auto or --all), (b) the user has explicitly approved a specific default for this question, or (c) the workflow's documented contract says defaults are safe (e.g. autonomous lifecycle paths).
  • Do NOT write workflow artifacts (CONTEXT.md, DISCUSSION-LOG.md, PLAN.md, checkpoint files) until the user has answered the plain-text questions or one of (a)-(c) above applies. Surfacing the questions and waiting is the correct response — silently defaulting and writing artifacts is the #3018 failure mode.

C. Task() → spawn_agent Mapping

GSD workflows use Task(...) (Claude Code syntax). Translate to Codex collaboration tools:

Schema detection (required first step): Codex exposes two spawn_agent schemas:

  • agent_type-capable schema (e.g. multi_agent_v2): spawn_agent accepts agent_type, message, reasoning_effort, fork_context, etc. — typed GSD agent dispatch is available.
  • Generic schema (multi_agent_v1): spawn_agent accepts only message, items, fork_context — there is no agent_type field. Typed GSD agent dispatch is unavailable in this session.

Before spawning, inspect the spawn_agent tool's visible parameter schema (via tool_search or the tool list) to determine which form is active.

Typed mapping (agent_type-capable schema only):

  • Task(subagent_type="X", prompt="Y")spawn_agent(agent_type="X", message="Y")
  • Agent(subagent_type="X", prompt="Y")spawn_agent(agent_type="X", message="Y")
  • Task(model="...") → omit. spawn_agent has no inline model parameter; GSD embeds the resolved per-agent model directly into each agent's .toml at install time so model_overrides from .planning/config.json and ~/.gsd/defaults.json are honored automatically by Codex's agent router.
  • Resolved reasoning_effort="low|medium|high|xhigh" (xhigh is a GSD/Codex tier, not a generic runtime enum) → pass reasoning_effort to spawn_agent when the runtime/tool supports it. Omit missing, empty, inherited, or unsupported values; do not invent one-off effort literals in workflow prose.
  • fork_context: false by default — GSD agents load their own context via <files_to_read> blocks
  • Task(isolation="worktree") / Agent(isolation="worktree") → no direct Codex mapping. Codex spawn_agent does not create or bind a git worktree automatically. Workflows that require this isolation must fail closed or use an explicit manual worktree protocol before spawning (#3360).

Generic-agent workaround (multi_agent_v1 schema — NO agent_type field): When only the generic multi_agent_v1 schema is available, typed GSD agent dispatch (gsd-planner, gsd-executor, etc.) is NOT possible. This is a known Codex limitation (openai/codex#15250). This workaround is NOT equivalent to typed gsd-planner/gsd-executor execution — GSD agents carry project-aware prompts, audit logging, and workflow context that a generic subagent lacks. Use the following fallback:

  1. Resolve your active Codex config root — the directory that contains your config.toml. This directory is determined in priority order: $CODEX_HOME (if set), the path given by --config-dir (if passed on invocation), a local .codex directory in the current project (if --local was used), or the default global config directory. Read agents/<agent-name>.toml relative to that config root to extract the agent's system instructions.
  2. Inject those instructions as a role-preamble into a generic spawn_agent(message=...) call.
  3. Label results and logs clearly as "generic-agent workaround" so the orchestrator and user know full typed-agent guarantees are not in effect.
  4. Where typed dispatch is mandatory for correctness (e.g. worktree isolation), fail closed and report the schema limitation rather than silently degrading.

Spawn restriction:

  • Codex restricts spawn_agent to cases where the user has explicitly requested sub-agents. When automatic spawning is not permitted, do the work inline in the current agent rather than attempting to force a spawn.
  • In some Codex sessions, multi-agent tooling can be deferred. If spawn_agent is not currently visible, discover tools first via tool_search before defaulting to inline execution.

Parallel fan-out:

  • Spawn multiple agents → collect agent IDs → wait(ids) for all to complete

Result parsing:

  • Look for structured markers in agent output: CHECKPOINT, PLAN COMPLETE, SUMMARY, etc.
  • close_agent(id) after collecting results from each agent </codex_skill_adapter>
<objective> Display comprehensive project statistics including phase progress, plan execution metrics, requirements completion, git history stats, and project timeline. </objective>

<execution_context> @/home/user/projects/temp/ai-apps/.personal-projects/registry-atlas/.codex/gsd-core/workflows/stats.md </execution_context>

<process> Execute end-to-end. </process>

When not to use it

  • When `request_user_input` is rejected or unavailable, and the skill is not in TEXT_MODE.
  • When the user has not explicitly approved a specific default for a question.
  • When the workflow's documented contract does not state that defaults are safe.

Limitations

  • It cannot proceed without a user answer unless specific conditions are met.
  • It will not write workflow artifacts until the user has answered plain-text questions or conditions apply.
  • It requires schema detection to determine the available `spawn_agent` capabilities.

How it compares

This skill provides a consolidated view of project health and progress through automated reporting, offering a more efficient and consistent overview than manually gathering metrics from disparate sources.

Compared to similar skills

gsd-stats side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
gsd-stats (this skill)01moNo flagsBeginner
task-master226moReviewIntermediate
agile-product-owner107moReviewBeginner
writing-plans153moReviewIntermediate

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