openup-log-run

Create traceability logs (markdown + JSONL) for the current agent run

6 stars

Best use case

openup-log-run is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Create traceability logs (markdown + JSONL) for the current agent run

Teams using openup-log-run 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

$curl -o ~/.claude/skills/openup-log-run/SKILL.md --create-dirs "https://raw.githubusercontent.com/GermanDZ/open-up-for-ai-agents/main/docs-eng-process/.claude-templates/skills/openup-log-run/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/openup-log-run/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How openup-log-run Compares

Feature / Agentopenup-log-runStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Create traceability logs (markdown + JSONL) for the current agent run

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

# Log Run

Create traceability logs for the current agent run. **Call only AFTER all changes are committed** (logs require actual commit SHAs).

> **Scribe step** — this entire skill is mechanical. Delegate to the
> `openup-scribe` agent (Agent tool, subagent_type: "openup-scribe"). You
> determine the values; the scribe only writes. Brief it with:
>
> ```
> Agent(subagent_type="openup-scribe", description="Write agent run log",
>   prompt="Write a traceability log entry.
>   Branch: [branch]. Commits: [sha list]. Phase: [phase]. Task: [task_id].
>   Start: [ts]. End: [ts]. Files changed: [list].
>   1. Create docs/agent-logs/YYYY/MM/DD/<timestamp>-agent-<branch>.md
>      with the run metadata, commits, and key decisions listed below: [decisions].
>   2. Append a JSONL record to docs/agent-logs/agent-runs.jsonl.
>   Report: paths created and JSONL record written.")
> ```
>
> Collect the commit SHAs and metadata yourself (they require git commands), then
> hand off the write operations to the scribe.

## Prerequisites

- `git status --porcelain` returns empty (all changes committed)
- Commit SHAs are available for reference

## Process

### 1. Generate Run ID

If `$ARGUMENTS[run_id]` is not provided, generate: `YYYY-MM-DDTHH:MM:SSZ-agent-branch`

### 2. Collect Run Metadata

- Branch: `git branch --show-current`
- Trunk: detect via `origin/HEAD`, fallback `main`/`master`
- Start/end timestamps
- Phase from `docs/project-status.md`
- Commits: `git log --oneline <since>...HEAD`

### 3. Create Markdown Log

Create `docs/agent-logs/YYYY/MM/DD/<timestamp>-<agent>-<branch>.md` with:
- Run metadata (branch, trunk, timestamps)
- Roles assumed and switches
- Tasks performed (one per primary-role task boundary)
- Commit SHAs created during run
- Consulting-role usage
- Key decisions/assumptions + doc links
- Initial instructions/prompt (verbatim)

### 4. Append JSONL Entry

Append to `docs/agent-logs/agent-runs.jsonl`:

```json
{"run_id":"<id>","agent":"claude","branch":"<branch>","trunk":"<trunk>","start":"<ts>","end":"<ts>","phase":"<phase>","iteration_goals":["..."],"prompt_hash":"sha256:...","md_log_path":"<path>","tasks":[{"role":"<role>","objective":"<obj>","start":"<ts>","end":"<ts>","commits":["<sha>"],"docs_updated":["<path>"],"consulting_roles":["<role>"]}],"decisions":["<path>"],"notes":"<summary>"}
```

### 5. Record the log gate

Once the markdown log and JSONL record are written, flip the iteration-state gate (no-op if there is no active `.openup/state.json`):

```bash
python3 scripts/openup-state.py set-gate log_written true 2>/dev/null || true
```

### 6. Verify

- Markdown log exists and is readable
- JSONL entry is valid JSON
- Commit SHAs referenced actually exist
- All required fields are populated

## Common Errors

| Error | Cause | Solution |
|-------|-------|----------|
| Uncommitted changes | Files not committed | `git add -A && git commit` first |
| Invalid JSONL | JSON format error | Verify syntax before appending |
| Missing commits | No commits for run | Verify run is complete |
| Directory not found | docs/agent-logs/ missing | Create directory structure first |

## References

- Traceability Logging SOP: `docs-eng-process/agent-workflow.md`

## See Also

- [openup-complete-task](../complete-task/SKILL.md) - Calls this skill automatically
- [openup-start-iteration](../start-iteration/SKILL.md) - Logs iteration start

Related Skills

openup-transition

6
from GermanDZ/open-up-for-ai-agents

Initialize and manage Transition phase activities - deploy to users

openup-tdd-workflow

6
from GermanDZ/open-up-for-ai-agents

Guide Test-Driven Development cycle adapted for AI agents with a pragmatic approach

openup-sync-spec

6
from GermanDZ/open-up-for-ai-agents

Back-propagate pure refactors to stale artifacts; classify the diff, refuse behaviour-changes, propose targeted edits for approval (read-only by default)

openup-start-iteration

6
from GermanDZ/open-up-for-ai-agents

Begin a new OpenUP iteration with proper phase context and task selection

openup-shared-vision

6
from GermanDZ/open-up-for-ai-agents

Create shared technical vision for team alignment

openup-retrospective

6
from GermanDZ/open-up-for-ai-agents

Generate iteration retrospective with feedback and action items

openup-request-input

6
from GermanDZ/open-up-for-ai-agents

Create an input request document for asynchronous stakeholder communication

openup-readiness

6
from GermanDZ/open-up-for-ai-agents

Compute the change-folder dependency DAG and print READY/BLOCKED/collision report for PM intake

openup-quick-task

6
from GermanDZ/open-up-for-ai-agents

Fast iteration mode for small changes - simplified workflow with minimal overhead

openup-plan-feature

6
from GermanDZ/open-up-for-ai-agents

Generate iteration plan and roadmap entry for a feature idea

openup-phase-review

6
from GermanDZ/open-up-for-ai-agents

Check phase completion criteria and prepare for phase review

openup-orchestrate

6
from GermanDZ/open-up-for-ai-agents

Run a full orchestrated iteration — PM decomposes the goal, delegates to specialist roles, collects outputs, and synthesizes results