Agent skill

Instruction Dashboard Tuning

by adam-s in adam-s/intercept

Use sub-agents to iteratively improve dashboard-building instructions.

MITAuto-check passedFrontend & Design

Install Instruction Dashboard Tuning

skills CLI
$ npx skills add adam-s/intercept --skill instruction-dashboard-tuning -a claude-code

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

GitHub CLI
$ gh skill install adam-s/intercept instruction-dashboard-tuning --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/adam-s/intercept.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/instruction-dashboard-tuning .claude/skills/instruction-dashboard-tuning && 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
instruction-dashboard-tuning
GitHub stars
189
Token cost
~4.3k tokens
SKILL.md length
1,056 words
Files
3 (incl. scripts)
Skills in repo
10
Repo updated
First seen
Licence
MIT

At a glance

Use sub-agents to iteratively improve dashboard-building instructions.

  • Works in 3 steps: Discovery → Build → Review
  • Tasks that involve UI design
  • SKILL.md covers ⚠️💣 MANDATORY CONSENT CHECK…, How This Works, Before Starting — Ask the User and The Three-Phase Pipeline, plus 12 more sections
  • Runs Shell scripts from its folder; calls pnpm, curl and python3

What it does

Instruction Dashboard Tuning is an agent skill from adam-s/intercept. Use sub-agents to iteratively improve dashboard-building instructions. Three-phase pipeline — discover APIs, build dashboard matching a wireframe, review code + UI with a SOTA reviewer agent. The dashboards are throwaway; instruction improvements and framework code fixes are the product.

Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/run-iteration.sh` and `scripts/write-handoff.sh`).

It sits in Frontend & Design, covering UI design, Subagents and Code review. The repository describes itself as: Turn any website into a typed JSON API using self improving agents. The licence is MIT.

When your agent uses it

  • Tasks that involve UI design
  • Tasks that involve Subagents
  • Tasks that involve Code review

Example prompts

  • “/instruction-dashboard-tuning”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

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

  1. Discovery
  2. Build
  3. Review

What it can do on your machine

Read from SKILL.md and the folder at commit 6451b89. 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

    Ships 2 files in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • pnpm
    • curl
    • python3

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

  • Network

    No URLs in SKILL.md. Its commands use pnpm and curl, 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

Instruction Dashboard Tuning loads about 4.3k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 1,056 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from adam-s/intercept at commit 6451b89, republished under its MIT licence (© adam-s). 1,056 words, ~4,276 tokens.

Download SKILL.mdSave it as .claude/skills/instruction-dashboard-tuning/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
instruction-dashboard-tuning
description
Use sub-agents to iteratively improve dashboard-building instructions. Three-phase pipeline — discover APIs, build dashboard matching a wireframe, review code + UI with a SOTA reviewer agent. The dashboards are throwaway; instruction improvements and framework code fixes are the product.

DO NOT write memory files. All learnings go into .claude/skills/, .claude/agents/, .claude/rules/, or framework code — NOT into memory.

Instruction Dashboard Tuning via Sub-Agent Testing

Check if .claude/user-consent.md exists with ACCEPTED: true. If yes, display: ✅ Prior consent on file (DATE). Proceeding. and skip to "Before Starting."

If not, present the 3 warnings from .claude/skills/instruction-tuning/SKILL.md (ToS, autonomous agents, resource consumption). All 3 must be accepted. Write .claude/user-consent.md on acceptance. This file is shared across both tuning skills.

How This Works

You are not building dashboards. You are writing instructions that make other agents build correct dashboards.

  1. Capture a wireframe — screenshot a real website (any data-rich site with lists, tables, or dashboards). This is the design target.
  2. Launch a discovery agent — discovers ALL transports via the already-tuned discovery protocol.
  3. Launch a builder agent — builds a dashboard matching the wireframe using the discovered API routes.
  4. Launch a reviewer agent — a SOTA frontier LLM that reviews the code + screenshots and produces structured findings.
  5. Apply findings — instruction improvements go to .claude/, framework code fixes go to packages/, apps/, etc.
  6. Discard the worktree — the dashboard is throwaway. The instruction and code improvements are the product.

Before Starting — Ask the User

Turn 1: Ask how many discovery passes — 1 or 2? Default to 1.

  • 1 pass: Full breadth discovery, then build dashboard.
  • 2 passes: Pass 1 = breadth. Pass 2 = deep dive on missed transports. Then build dashboard with the combined routes.

Turn 2: Ask which websites to use as wireframes. The user picks the sites.

Turn 3: Ask what to do when agents finish. Pick one:

  • A) Full cleanup (default) — kill processes, delete worktrees, revert shared files.
  • B) Keep worktrees — kill processes but preserve worktree directories for reuse.
  • C) Keep agents alive — don't stop running agents, allow continuation or redirection.
  • D) Keep both — preserve worktrees AND keep agents alive.

Do NOT launch agents until the user answers all questions.

The Three-Phase Pipeline

SCREENSHOT of real website = the "wireframe"
                │
    ┌───────────┼───────────┐
    ▼           ▼           ▼
 Phase 1     Phase 2     Phase 3
 DISCOVERY   BUILD        REVIEW
 (worktree)  (same wt)   (read-only)
    │           │           │
    ▼           ▼           ▼
 Domain      Dashboard   Findings
 plugin      matching    report
 w/ routes   wireframe     │
                      ┌────┴────┐
                      ▼         ▼
                  .claude/   packages/
                  skills/    apps/ etc.
                      │
                      ▼
               WORKTREE DELETED
               IMPROVEMENTS KEPT

The Loop

1.  Clean: bash .claude/hooks/cleanup-agents.sh
    Also: for port in $(seq 3031 3049); do lsof -ti:"$port" | xargs kill -9 2>/dev/null; done
    Also: rm -rf /tmp/dashboard-tuning/
2.  Verify commit: ensure worktrees branch from latest committed instructions
3.  Capture wireframe:
    mkdir -p /tmp/dashboard-tuning
    Screenshot target website at 1280x800 → /tmp/dashboard-tuning/wireframe-desktop.png
    Screenshot at 375x800 → /tmp/dashboard-tuning/wireframe-mobile.png
4.  Launch Phase 1 (Discovery) in worktree (run_in_background: true)
5.  LIVE MONITOR every 60s until discovery completes
6.  Verify: elimination table filled, routes return data via curl
7.  Launch Phase 2 (Build) in SAME worktree (run_in_background: true)
8.  LIVE MONITOR every 60s until build completes
8b. VERIFY PROXY — before screenshots, confirm the web proxy reaches the API:
    curl -s http://localhost:$WEB_PORT/api/$DOMAIN/ROUTE | head -c 200
    If this fails (500, empty), the web server was started without API_PORT=$API_PORT.
    Kill, restart with API_PORT=$API_PORT PORT=$WEB_PORT, re-verify.
8c. VERIFY PLACEMENT — check page is in (dashboard)/ group:
    ls apps/web/src/app/\(dashboard\)/PAGE_NAME/page.tsx
    If the page is at apps/web/src/app/PAGE_NAME/ instead, that's a finding.
9.  Capture dashboard screenshots at 4 viewports (375, 768, 1280, 1920):
    mkdir -p /tmp/dashboard-tuning/screenshots
    ./scripts/screenshot-dashboard.sh --path /PAGE --width W --port $WEB_PORT --output /tmp/dashboard-tuning/screenshots/WxH.png
10. Launch Phase 3 (Review) — reviewer reads worktree + screenshots (run_in_background: true)
11. MONITOR until reviewer produces findings report
12. Process findings:
    a. Apply GENERALIZED=yes instruction improvements to .claude/
    b. Apply framework code fixes to packages/, apps/, services/, scripts/, tests/
    c. CONSISTENCY CHECK — grep all .claude/ for the concept you changed
13. PRUNE .claude/ — run `wc -l .claude/skills/dashboard-builder/SKILL.md .claude/agents/dashboard-agent.md`.
    If any file exceeds 300 lines, extract the bottom third to a `reference/` subdirectory.
    Keep the main file focused on: architecture, build steps, states, wireframe fidelity, responsive, errors.
    Niche patterns (comment trees, video, sparklines, CRUD) go in reference files.
14. Process cleanup: kill servers, remove worktree
15. Commit fixes to main
16. Write handoff (.claude/dashboard-tuning-handoff.md, gitignored)
17. Start fresh Claude Code session, repeat

Phase 1: Discovery

Reuses the already-tuned discovery protocol. No new instructions needed.

Agent: discovery-agent (.claude/agents/discovery-agent.md)

Prompt template:

Discover ALL transport types that [site] uses. Build a route for EVERY transport found.
Target: [url]
Follow .claude/rules/discovery.md — GATHER→SCAN→CLASSIFY→BUILD.
In GATHER: connect to HOMEPAGE first and browse naturally (scroll, click) to warm up cookies before navigating to target pages. Intercept pagination traffic. If you see an API endpoint with pagination params in traffic, test it directly via /browser/mcp/fetch. For cross-origin APIs, credentials are forwarded automatically.
In CLASSIFY: name the site's core data and verify your transports cover it.
In BUILD: auth-gated endpoints (Gap=Y) go directly to session harvest. Read the session harvest reference file BEFORE writing any harvest code.
Fill ALL 8 elimination rows before writing code.
After building routes, register your domain and test EVERY route through the API server proxy.
Before finishing: run `pnpm biome check --write --unsafe .` and fix any remaining lint or type errors. CI must be clean.
Budget: ~150 tool calls. Plan: ~30 GATHER, ~10 SCAN/CLASSIFY, ~80 BUILD, ~30 testing.
Your port is XXXX.

Port: 3031+N (API only — discovery doesn't need a web server)

Phase 2: Build

Agent: dashboard-agent (.claude/agents/dashboard-agent.md)

Prompt template:

Build a dashboard that matches the wireframe screenshot at /tmp/dashboard-tuning/wireframe-desktop.png.

API routes are already working in this worktree:
[paste output of curl -s http://localhost:API_PORT/api]

Read these skill files before starting:
1. .claude/skills/dashboard-builder/SKILL.md — the build process
2. .claude/skills/visual-dev/SKILL.md — screenshot + judge loop
3. .claude/skills/debug-logs/SKILL.md — when data doesn't flow
4. .claude/skills/systematic-testing/SKILL.md — verify API routes first

Your wireframe: Read /tmp/dashboard-tuning/wireframe-desktop.png
This is a real website screenshot. Match its:
- Layout structure (grid, sidebar, header)
- Information density (items per row, spacing)
- Typography hierarchy (title vs metadata sizing)
- Component patterns (cards, badges, thumbnails)

The gap between your dashboard screenshot and the wireframe IS the bug.

Structural requirements:
- Place page in (dashboard)/ group: apps/web/src/app/(dashboard)/<page-name>/page.tsx
- If the wireframe has its own header/footer/nav, add a layout.tsx opt-out:
  export default function Layout({ children }: { children: React.ReactNode }) { return <>{children}</>; }
- Use shadcn/ui Button (not raw <button>), Alert (not raw <div>), for ALL interactive/status elements
- Override visual tokens (className + style), not component choice
- If API returns HTML fragments, sanitize with DOMPurify before dangerouslySetInnerHTML

When starting servers, set ports explicitly:
  PORT=$API_PORT pnpm --filter @interceptor/api dev
  API_PORT=$API_PORT PORT=$WEB_PORT pnpm --filter @interceptor/web dev

Before finishing: run `pnpm biome check --write --unsafe .` and fix any remaining lint or type errors. CI must be clean.
API port: XXXX. Web port: YYYY.
Budget: 80 tool calls.

Ports: API 3031+N, Web 3041+N

Before launching: Kill the discovery agent's API server, then restart with both API and Web servers in the same worktree.

Phase 3: Review

Agent: reviewer-agent (.claude/agents/reviewer-agent.md)

Prompt template:

Review the dashboard built by another agent.

Worktree code: [WORKTREE_PATH]
Dashboard screenshots: /tmp/dashboard-tuning/screenshots/
Wireframe: /tmp/dashboard-tuning/wireframe-desktop.png

Read ALL component files in the dashboard directory.
Read ALL screenshots (4 viewports) and the wireframe.
Read the .claude/ instruction files the builder was supposed to follow.

Score on the 12-point review. Compare wireframe to dashboard — name SPECIFIC differences.
Produce Section A (instruction improvements) and Section B (framework code fixes).
Only include GENERALIZED=yes findings.

Budget: 40 tool calls.

Runs from main repo (not a worktree). Reads the worktree path but writes nothing.

Live Monitoring

Parse the agent output file every 60 seconds:

bash
FILE="$AGENT_OUTPUT_FILE"
cat "$FILE" | python3 -c "
import sys, json
tool_calls = 0; screenshots = 0; skill_reads = 0; writes = 0; edits = 0
files_written = []; last_texts = []
for line in sys.stdin:
    try:
        d = json.loads(line)
        if d.get('type') == 'assistant':
            for c in d.get('message',{}).get('content',[]):
                if isinstance(c,dict):
                    if c.get('type')=='text' and len(c.get('text',''))>20:
                        last_texts.append(c['text'][:200])
                        if len(last_texts) > 3: last_texts.pop(0)
                    elif c.get('type')=='tool_use':
                        tool_calls += 1
                        n=c.get('name','');inp=c.get('input',{})
                        if n=='Bash':
                            cmd = inp.get('command','')
                            if 'screenshot' in cmd.lower(): screenshots += 1
                        elif n=='Write':
                            writes += 1; files_written.append(inp.get('file_path','').split('/')[-1])
                        elif n=='Edit': edits += 1
                        elif n=='Read':
                            fp = inp.get('file_path','')
                            if 'SKILL.md' in fp or 'visual-dev' in fp: skill_reads += 1
        if d.get('type') == 'result': print('*** AGENT COMPLETE ***')
    except: pass
print(f'Calls: {tool_calls}/80 | Screenshots: {screenshots} | Skills read: {skill_reads} | Writes: {writes} | Edits: {edits}')
print(f'Files: {files_written}')
for t in last_texts: print(t[:150]); print('---')
"

At each check, update the monitoring table:

AgentCallsScreenshotsSkillsFilesNotes

Phase 1 watch for:

  • Elimination table progress
  • Route count
  • Infrastructure waste (pnpm retries, sleep loops)

Phase 2 watch for:

  • Screenshot frequency — 20+ calls without a screenshot = building blind
  • State enumeration — should happen in first 5-10 calls
  • Skill file reads — should read dashboard-builder + visual-dev early
  • Component granularity — one 500-line file vs multiple small files
  • Route group placement — page should be under (dashboard)/, not top-level /app/
  • shadcn/ui coverage — check for raw <button> or raw <div> for errors
  • API_PORT set correctly when web server started

Phase 3 watch for:

  • Did reviewer read all component files?
  • Did reviewer read all 4 viewport screenshots + wireframe?
  • Are findings GENERALIZED or site-specific?

Budget overrun: If an agent exceeds its budget, let it finish its current output but note the overrun. In findings, check whether the overrun was caused by a monolith rewrite (instruction gap) or unnecessary retries (agent error).

Scorecards

Discovery Scorecard

Same as .claude/skills/instruction-tuning/SKILL.md — the existing 18-check table.

Show full SKILL.md (474 more words)Show less
Builder Scorecard
CheckPass/Fail
Read all 4 skill files before starting
API routes verified via curl before UI work
States enumerated BEFORE writing component code
Built component-by-component, not whole page blind
Screenshot taken after EVERY visual change
7 judgment criteria applied to each screenshot
Wireframe compared against dashboard screenshot
Fixed issues one at a time between screenshots
All states rendered: idle, loading, populated, empty, error, detail
Mobile viewport (375px) screenshot taken and judged
Data flows through /api/ proxy — no hardcoded URLs
Used shadcn/ui components — no reinvented primitives
Component files under 200 lines each
No @interceptor/shared imports in client components
Biome auto-fix run before manual lint cleanup
Stayed under 80 tool calls
Reviewer Scorecard
CheckPass/Fail
Read all worktree source files (components, routes, types)
Read dashboard screenshots at all 4 viewports
Read the wireframe screenshot
Compared wireframe to dashboard (named specific differences)
Produced Section A with >= 3 instruction improvement findings
All Section A findings are GENERALIZED=yes
Produced Section B with >= 1 framework code fix
Did NOT suggest changes to the worktree code
Each finding includes specific file path and exact text change
Scored the dashboard on the 12-point review

Findings Flow

REVIEWER produces findings report
         │
    ┌────┴────┐
    ▼         ▼
Section A:   Section B:
Instructions Code Fixes
    │         │
    ▼         ▼
.claude/     packages/
skills/      apps/ etc.
    │         │
    └────┬────┘
         ▼
CONSISTENCY CHECK
(grep .claude/ for contradictions)
         │
         ▼
   COMMIT + HANDOFF

The orchestrator is the ONLY entity that writes to .claude/. The reviewer produces a report. The builder writes to the worktree. No agent ever writes to .claude/ directly.

Consistency Check

When you change an instruction, the same concept appears in multiple files. A fix in dashboard-builder/SKILL.md means nothing if dashboard-agent.md still uses the old language.

Before committing any instruction change:

bash
grep -rn "CONCEPT" .claude/rules/ .claude/agents/ .claude/skills/

Every hit must be consistent with your change.

Port Allocation

WorktreeAPI PortWeb Port
dash-130313041
dash-230323042
dash-330333043

Avoids collision with discovery agents (3011-3021) and user dev (3000-3001).

Session Handoff

After committing all fixes, write .claude/dashboard-tuning-handoff.md (gitignored) with:

  1. Iteration number
  2. Wireframe used — what website was the target
  3. Phase 1 results — routes discovered, transports found, tool calls used
  4. Phase 2 results — components built, screenshots taken, tool calls used
  5. Phase 3 results — reviewer score (out of 24), findings count
  6. Findings applied — which instruction changes were committed
  7. Findings deferred — which findings need more data
  8. What's next — specific items for the next iteration

Generalization Rule

Every instruction change must work for ANY website. If a fix only helps for a specific site, it's overfitting. The reviewer must mark GENERALIZED=yes on every finding. The orchestrator drops GENERALIZED=no findings.

Convergence

The dashboard tuning loop converges when fresh builder agents (clean session, no hints):

  1. Read all skill files and follow prescribed phases in order
  2. Enumerate all visual states before writing code
  3. Screenshot after every visual change and apply 7 judgment criteria
  4. Compare against wireframe, identify and fix layout gaps
  5. Build component by component, keep files under 200 lines
  6. Score 20+ on the reviewer's 12-point review
  7. Stay near 80 tool calls

© adam-s, 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 2 other files (scripts) in .claude/skills/instruction-dashboard-tuning of adam-s/intercept.

  • SKILL.md
  • scripts/run-iteration.sh
  • scripts/write-handoff.sh

Open the folder on GitHubat commit 6451b89

Compare with similar skills

Instruction Dashboard Tuning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Instruction Dashboard Tuning compared with similar skills
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Tidymblode/agent-skills144—~4.2kAutomated safety check: PassMIT
Spec App Consistency Auditleo-kuang-ai/spec-first107—~4.6kAutomated safety check: PassMIT
UI StylingOhh-889/skyroc79513 repos~2.5kAutomated safety check: PassMIT
LobeHub Interactive Prototypelobehub/lobehub83k—~1.6kAutomated safety check: PassCustom licence

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Questions about Instruction Dashboard Tuning

What does Instruction Dashboard Tuning do?

Use sub-agents to iteratively improve dashboard-building instructions. Instruction Dashboard Tuning is an agent skill from adam-s/intercept. Use sub-agents to iteratively improve dashboard-building instructions.

When should I use Instruction Dashboard Tuning?

Instruction Dashboard Tuning fits situations like: tasks that involve UI design; tasks that involve Subagents; tasks that involve Code review.

How do I install Instruction Dashboard Tuning in Claude Code?

Run `npx skills add adam-s/intercept --skill instruction-dashboard-tuning -a claude-code`. Or copy the skill folder (.claude/skills/instruction-dashboard-tuning in adam-s/intercept) into .claude/skills/instruction-dashboard-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Instruction Dashboard Tuning in Codex?

Run `npx skills add adam-s/intercept --skill instruction-dashboard-tuning -a codex`. Or copy the skill folder (.claude/skills/instruction-dashboard-tuning in adam-s/intercept) into .agents/skills/instruction-dashboard-tuning in your project. Codex loads it when a task matches its description.

Can I use Instruction Dashboard Tuning 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 adam-s/intercept --skill instruction-dashboard-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/instruction-dashboard-tuning, .gemini/skills/instruction-dashboard-tuning, .github/skills/instruction-dashboard-tuning and .opencode/skills/instruction-dashboard-tuning in your project.

What does Instruction Dashboard Tuning need to run?

Going by SKILL.md and its folder, Instruction Dashboard Tuning needs a shell for the scripts in its folder and the command-line tools its instructions call (pnpm, curl and python3). Our summary lists: Python 3; A Bash shell.

Does Instruction Dashboard Tuning access the network?

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

Is Instruction Dashboard Tuning 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Instruction Dashboard Tuning use?

Instruction Dashboard Tuning 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 Instruction Dashboard Tuning use?

About 4.3k tokens (SKILL.md is roughly 17k 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 Instruction Dashboard Tuning?

Skills that share tags, products or a category with Instruction Dashboard Tuning: Review UI (aaddrick/claude-pipeline, 130 stars), Tidy (mblode/agent-skills, 144 stars), Spec App Consistency Audit (leo-kuang-ai/spec-first, 107 stars) and UI Styling (Ohh-889/skyroc, 795 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Instruction Dashboard Tuning?

adam-s (a GitHub user) maintains it in adam-s/intercept, which has 189 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on July 31, 2026.

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