Agent skill

Workflow Improvement

by athola in athola/claude-night-market

Evaluates and improves skills, agents, commands, and hooks after a workflow slice.

MITAuto-check passedDevelopment

Install Workflow Improvement

skills CLI
$ npx skills add athola/claude-night-market --skill workflow-improvement -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install athola/claude-night-market workflow-improvement --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/athola/claude-night-market.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/sanctum/skills/workflow-improvement .claude/skills/workflow-improvement && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
workflow-improvement
GitHub stars
342
Token cost
~2.7k tokens
SKILL.md length
1,061 words
Files
2
Skills in repo
159
Repo updated
First seen
Licence
MIT

At a glance

Evaluates and improves skills, agents, commands, and hooks after a workflow slice.

  • Works in 8 steps: Gather Improvement Context… → Capture the Session Slice (slice-captured) → Recreate the Workflow (workflow-recreated) → …
  • Execution felt slow
  • SKILL.md covers When To Use, When NOT To Use, Required TodoWrite Items and Step 0: Gather Improvement…, plus 10 more sections
  • Calls git

What it does

Workflow Improvement is an agent skill from athola/claude-night-market. Evaluates and improves skills, agents, commands, and hooks after a workflow slice. Use when execution felt slow, confusing, repetitive, or fragile.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `modules/auto-issue-creation.md`).

It sits in Development. The repository describes itself as: 23 Claude Code plugins: TDD enforcement hooks, git/PR workflows, spec-driven development, code review, project lifecycle, fix-from-error, maintenance automation, context… The licence is MIT.

When your agent uses it

  • Execution felt slow

Example prompts

  • “Use the workflow-improvement skill to evaluate and improves skills, agents, commands, and hooks after a workflow slice”
  • “/workflow-improvement”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Gather Improvement Context (context-gathered)
  2. Capture the Session Slice (slice-captured)
  3. Recreate the Workflow (workflow-recreated)
  4. Generate Improvements (improvements-generated)
  5. Agree on a Plan (plan-agreed)
  6. Implement (changes-implemented)
  7. Validate Substantive Improvement (validated)
  8. Close the Loop (Store Lessons)

What it can do on your machine

Read from SKILL.md and the folder at commit 9f3eb00. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Workflow Improvement loads about 2.7k tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 1,061 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from athola/claude-night-market at commit 9f3eb00, republished under its MIT licence (© athola). 1,061 words, ~2,727 tokens.

Download SKILL.mdSave it as .claude/skills/workflow-improvement/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
workflow-improvement
description
Evaluates and improves skills, agents, commands, and hooks after a workflow slice. Use when execution felt slow, confusing, repetitive, or fragile.
alwaysApply
false
category
workflow-ops
tags
workflow, retrospective, efficiency, commands, agents, skills, hooks
complexity
medium
model_hint
standard
estimated_tokens
900

Workflow Improvement

When To Use

Use this skill after running a command or completing a short session slice where execution felt slow, confusing, repetitive, or fragile.

This skill focuses on improving the workflow assets (skills, agents, commands, hooks) that were involved, not on feature work itself.

When NOT To Use

  • Implementing features - focus on feature work first

Required TodoWrite Items

  1. fix-workflow:context-gathered
  2. fix-workflow:slice-captured
  3. fix-workflow:workflow-recreated
  4. fix-workflow:improvements-generated
  5. fix-workflow:plan-agreed
  6. fix-workflow:changes-implemented
  7. fix-workflow:validated
  8. fix-workflow:lesson-stored

Step 0: Gather Improvement Context (context-gathered)

Before analyzing the current session, gather existing improvement data:

0.1: Check Skill Execution History

Query memory-palace logs for recent performance issues:

bash
# Recent failures (last 7 days)
/skill-logs --failures-only --last 7d

# Performance metrics for involved plugins
pensive:skill-review --plugin sanctum --recommendations

Capture:

  • Skills with stability_gap > 0.3
  • Recent failure patterns and error messages
  • Performance degradation trends
0.2: Query Knowledge Base

Search for previously captured workflow lessons:

bash
# If memory-palace review-chamber is available
/review-room search "workflow improvement" --room lessons
/review-room search "efficiency" --room patterns

Look for:

  • Similar workflow issues from past PRs
  • Recurring patterns in workflow failures
  • Architectural decisions affecting workflows
0.3: Check Git History

Identify recurring issues through commit patterns:

bash
git log --oneline --grep="improve\|fix\|optimize" --since="30 days ago" \
  -- plugins/sanctum/skills/ plugins/sanctum/commands/

# Look for unstable components (frequent fixes)
git log --oneline --since="30 days ago" --follow \
  -- plugins/sanctum/skills/workflow-improvement/

Extract:

  • Components with frequent bug fixes (instability signals)
  • Patterns in improvement commit messages
  • Recurring issue themes

Output Format:

markdown
## Improvement Context

### Skill Performance Issues
- sanctum:workflow-improvement: stability_gap 0.35 (5 failures in 7 days)
- Error pattern: "Missing validation in Step 2"

### Knowledge Base Lessons
- PR #42 lesson: "Workflow validation should happen at start, not end"
- Pattern: Early validation reduces iteration time by 30%

### Git History Insights
- workflow-improvement skill: 8 commits in 30 days (instability signal)
- Recurring theme: "Add missing prerequisite checks"

Step 1: Capture the Session Slice (slice-captured)

Identify the most recent command or session slice in the current context window and capture:

  • Trigger: What command / request started it (include the literal /command if present)
  • Goal: What "done" meant for the user
  • Artifacts touched: Skills, agents, commands, hooks (names + file paths)
  • Evidence: Key tool calls / errors / retries that indicate inefficiency
  • Context from Step 0: Reference any relevant patterns from improvement context

If the slice is ambiguous, pick the most recent complete attempt and state the exact boundary you chose.

Step 2: Recreate the Workflow (workflow-recreated)

Reconstruct the workflow as a numbered list of 5 to 20 steps, identifying inputs, branch points for decisions, and outputs such as file changes or state modifications. During this reconstruction, identify specific friction points that reduce efficiency. These often include repeated steps or redundant tool calls, as well as missing guardrails where validation occurs too late or prerequisites are unclear. Other common issues are a lack of automation for tasks that should be scripted, and discoverability gaps caused by confusing naming conventions.

Cross-reference with Step 0 context:

  • Are friction points matching known failure patterns?
  • Do repeated steps align with git history themes?
  • Are missing guardrails mentioned in review-chamber lessons?

Step 3: Generate Improvements (improvements-generated)

Generate 3 to 5 distinct improvement approaches and score each on impact, complexity, reversibility, and consistency with existing sanctum patterns. The scoring should specifically address whether the change prevents the recurrence of patterns identified in Step 0. Prioritize improvements that address components with a high stability gap (greater than 0.3) or recurring issues found in the git history. You should also incorporate lessons from the review-chamber and aim to reduce failure modes identified in the skill logs. Prefer small, high-use changes such as tightening a skill's exit criteria, adding missing command options, improving hook guardrails for better observability, or splitting overloaded commands into clearer phases.

Step 4: Agree on a Plan (plan-agreed)

Choose 1 approach and define:

  • Acceptance criteria ("substantive difference")
  • Files to change
  • Validation commands to run
  • Out-of-scope items to defer

Keep the plan bounded: aim for ≤ 5 files changed unless the workflow truly spans more.

Step 5: Implement (changes-implemented)

Apply changes following sanctum conventions:

  • Keep naming consistent across commands/, agents/, skills/, hooks/
  • Prefer documentation-first improvements if ambiguity was the primary issue
  • If behavior changes, add/adjust tests in plugins/sanctum/tests/

Step 6: Validate Substantive Improvement (validated)

Validation should include at least 2 of:

  • Plugin validators / unit tests passing (targeted)
  • Re-running the minimal workflow reproduction with fewer steps or less manual work
  • A clear reduction in failure modes (e.g., earlier validation, clearer options)

Record the before/after comparison as metrics, not prose:

  • Step count reduction
  • Tool call reduction
  • Errors avoided (what would have failed before)
  • Duration improvement (if measurable)
Metrics Comparison Template
markdown
## Validation Results

### Before Improvement
- Step count: 15
- Tool calls: 23
- Failure points: 3
- Duration: ~8 minutes
- Manual interventions: 5

### After Improvement
- Step count: 11 (-4, -27%)
- Tool calls: 17 (-6, -26%)
- Failure points: 0 (-3, -100%)
- Duration: ~5 minutes (-37%)
- Manual interventions: 2 (-3, -60%)

### Verification
[E1] Command: `python3 plugins/sanctum/scripts/test_workflow.py`
Output: All tests passed (0.5s)

[E2] Command: `/validate-plugin sanctum`
Output: No issues found
Show full SKILL.md (431 more words)Show less

Step 7: Close the Loop (Store Lessons)

After validation, capture the improvement for future reference:

7.1: Update Git History

Commit with descriptive message that future searches will find:

bash
git add <changed-files>
git commit -m "improve(sanctum): <component> - <specific fix>

Addresses recurring issue: <pattern from Step 0>
Reduces <metric> by <percentage>

Evidence: stability_gap reduced from 0.35 to 0.12"

No AI-attribution trailer. plugins/imbue/hooks/vow_no_ai_attribution.py blocks co-authorship and generated-by trailers naming an AI model at PreToolUse, so a template carrying one hands the agent a commit its own toolchain refuses.

7.2: Post Tooling Learnings to Discussions (Preferred)

Observations about night-market tooling (skill behavior, agent coordination, hook timing, command UX) belong in https://github.com/athola/claude-night-market/discussions, not local memory. Always target the night-market repo regardless of which repo you are currently working in.

Use the GraphQL pattern in plugins/sanctum/commands/fix-pr-modules/steps/6-complete.md Sub-Step 6.7. It resolves the night-market repository and the Learnings category by ID and hardcodes athola/claude-night-market as the target, so it works from any repo.

Do not reach for plugins/abstract/scripts/post_learnings_to_discussions.py here. It takes no arguments: it parses LEARNINGS.md and posts the aggregated daily digest, which is a different artifact from one observation captured mid-workflow.

Repo-specific learnings stay in the current repo. Tooling learnings always go to https://github.com/athola/claude-night-market/discussions so the framework can improve.

7.3: Capture Lesson in Memory Palace (Optional, Local Only)

If the improvement addresses a repo-specific pattern (not tooling), store it locally:

bash
# Store in review-chamber lessons
/review-room capture --room lessons --title "Workflow: <pattern name>"
7.4: Update Improvement Metrics

Track the improvement's impact:

bash
# Check post-improvement stability
pensive:skill-review --skill sanctum:<component> --recommendations

This creates a feedback loop where future /fix-workflow and /update-plugins runs will reference this lesson.

Record Lessons Learned (decision journal)

If this work involved rework, a failed approach, or a blocker, record it to docs/lessons-learned.md so the insight survives past the session (draft and confirm):

  • If leyline is installed, invoke Skill(leyline:decision-journal) and append a lesson entry (what_happened, what_didnt_work, root_cause, action; set phase to review). Show the draft; append on confirmation.
  • Fallback (leyline absent): append to docs/lessons-learned.md using the in-file ENTRY TEMPLATE; assign the next LL-NNN id.

Supporting Modules

Exit Criteria

  • The session slice is captured with a stated boundary, trigger, goal, and artifacts touched
  • At least 3 distinct improvement approaches were generated and scored
  • One approach was chosen with acceptance criteria and a bounded file list (<= 5 files unless justified)
  • Validation records before/after metrics (step count, tool calls, or failure points), not prose
  • If the slice involved rework, a failed approach, or a blocker, the lesson is recorded to docs/lessons-learned.md via the decision journal

Troubleshooting

Common Issues

If a command is not found, confirm that all dependencies are installed and accessible in your PATH. For permission errors, check file system permissions and run the command with appropriate privileges. If you encounter unexpected behavior, enable verbose logging using the --verbose flag to capture more detailed execution data.

© athola, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in plugins/sanctum/skills/workflow-improvement of athola/claude-night-market.

  • SKILL.md
  • modules/auto-issue-creation.md

Open the folder on GitHubat commit 9f3eb00

Compare with similar skills

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Categories

Questions about Workflow Improvement

What does Workflow Improvement do?

Evaluates and improves skills, agents, commands, and hooks after a workflow slice. Workflow Improvement is an agent skill from athola/claude-night-market. Evaluates and improves skills, agents, commands, and hooks after a workflow slice.

When should I use Workflow Improvement?

Workflow Improvement fits situations like: execution felt slow.

How do I install Workflow Improvement in Claude Code?

Run `npx skills add athola/claude-night-market --skill workflow-improvement -a claude-code`. Or copy the skill folder (plugins/sanctum/skills/workflow-improvement in athola/claude-night-market) into .claude/skills/workflow-improvement in your project. Claude Code loads it when a task matches its description.

How do I install Workflow Improvement in Codex?

Run `npx skills add athola/claude-night-market --skill workflow-improvement -a codex`. Or copy the skill folder (plugins/sanctum/skills/workflow-improvement in athola/claude-night-market) into .agents/skills/workflow-improvement in your project. Codex loads it when a task matches its description.

Can I use Workflow Improvement in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add athola/claude-night-market --skill workflow-improvement -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/workflow-improvement, .gemini/skills/workflow-improvement, .github/skills/workflow-improvement and .opencode/skills/workflow-improvement in your project.

What does Workflow Improvement need to run?

Going by SKILL.md and its folder, Workflow Improvement needs the command-line tools its instructions call (git). Our summary lists: Python 3.

Does Workflow Improvement access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Workflow Improvement safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Workflow Improvement use?

Workflow Improvement is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Workflow Improvement use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Workflow Improvement?

Skills that share tags, products or a category with Workflow Improvement: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Workflow Improvement?

athola (a GitHub user) maintains it in athola/claude-night-market, which has 342 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on October 6, 2026.

Source: athola/claude-night-market on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.