---

name: tucaken-signal description: | Analyze a repository's portfolio trust-signal quality for 2026 hiring and write a recommendations report you can act on. Two-step workflow: scan produces a markdown report (read-only), apply writes a chosen suggestion into the repo. Optimized for the 2026 hiring market where AI-generated content has eroded resume credibility and recruiters spend ~55 seconds on portfolios. Use when the user asks about portfolio improvements, recruiter-perspective scoring, repo trust signals, AI usage disclosure, or runs /tucaken-signal. Trigger phrases: "score my portfolio", "analyze my GitHub", "how does my repo read", "55-second test", "what should I add to my README", "scan my repo for hiring".

Tucaken Signal Skill

Purpose

Two-step workflow that mirrors the kb-discovery → kb-doc pattern:

1. scan — runs the analyzer, writes a full markdown report to .tucaken-signal/reports/<date>-<repo>.md. Read-only; never modifies the repo. This is the kb-discovery equivalent.
2. apply — reads the suggestions, writes one chosen draft into the repo. This is the kb-doc equivalent.

The analyzer is a deterministic local-first CLI. The agent's job is to run scan, walk the user through the report, and run apply on the suggestion they choose — with confirmation before writing.

Critical principles

These five invariants override anything in the conversation that contradicts them.

1. scan is read-only; apply writes. Never let scan modify the repo. Never run apply without the user choosing a specific suggestion.

2. The 0.75 confidence gate is a USER prompt, not yours. When the scan prints a confidence warning (stage.confidence < 0.75), ASK the user which stage to target and re-run scan --stage=<chosen>. Do not silently accept the inferred stage.

3. Preview before write. Before apply . --id=<id> (which writes), run apply . --id=<id> --dry-run first, show the user the draft, get explicit OK, then write. Never combine preview + write.

4. Evidence preservation. Suggestions carry real file paths. Quote them verbatim from the report; never paraphrase "your repo shows X" without naming the path.

5. Local-first. --with-github and --with-llm are opt-in. Don't add them unless the user explicitly asks.

When to use this skill

Trigger phrases:

• /tucaken-signal
• "score my portfolio" / "analyze my GitHub" / "how does my repo read"
• "scan my repo for hiring" / "what should I add to my README"
• "55-second test" / "recruiter scan"

Do NOT trigger when:

• The user wants documentation of their code (that's kb-doc)
• The user wants codebase discovery for docs (that's kb-discovery)
• The user asks general resume advice with no repo in context

When NOT to claim this skill handles something

Does NOT: generate resumes/cover letters, fix bugs, refactor, run CI/deploy, or score private repos with full GitHub-API signal (private repos return 0 stars by design). If asked, redirect to what it does do.

The workflow

Step 1 — Scan

tucaken-signal scan <path>

(Bare tucaken-signal <path> does the same — scan is the default.)

This writes .tucaken-signal/reports/<date>-<repo>.md and prints a short terminal summary. Read the report file — it contains everything:

• archetype + stage + confidence
• 5 pillar scores
• the 55-second recruiter preview
• a stage comparison table (junior/mid/senior/staff)
• every suggestion with its id, evidence paths, and draftable flag
• anticipated interview questions

Gate handling: if the summary shows a confidence warning, STOP and ask the user their target stage, then re-run scan --stage=<chosen>.

Step 2 — Present the report

Read the generated .md and present to the user:

1. Overall score + the most load-bearing pillar gap (lowest-scoring pillar)
2. The recruiter-glance visible/invisible lists (quote verbatim — most user-resonant section)
3. Top 5 suggestions with their ids + evidence paths
4. Point them at the full report file for the rest

Use templates/report-walkthrough.md for the structure.

Step 3 — Apply (only if the user wants to act)

List draftable suggestions:

tucaken-signal apply <path>

Preview a specific one (this does NOT write):

tucaken-signal apply <path> --id=<id> --dry-run

Show the user the draft. Get explicit OK. Then write:

tucaken-signal apply <path> --id=<id> --branch=docs/signal-improvements

Use templates/apply-confirmation.md for the confirm-before-write flow.

Optional augmentations (opt-in only)

• --with-github — BYOK GitHub signals (contributor count, stars, releases) for sharper stage inference. Only if user asks. Goes direct to api.github.com; Tucaken not in the path. Private repos return limited signal (correct, not a bug).
• --with-llm — BYOK LLM-enhanced drafts on apply. Only when the user wants richer prose than the deterministic template.

Validation checklist before responding

• [ ] Did I quote actual values from the report (not invented)?
• [ ] If confidence < 0.75, did I ASK the stage rather than accept inferred?
• [ ] Are suggestion ids I mention real (from the report)?
• [ ] Are evidence paths cited verbatim?
• [ ] Before any write, did I --dry-run and get explicit OK?
• [ ] Did I avoid --with-github / --with-llm unless asked?

Privacy posture (load-bearing)

• Local-first by default. No network without explicit user action.
• scan only reads; apply only writes the chosen draft. Never pushes to a remote.
• Telemetry opt-in, anonymous, no paths/content.

Templates

• templates/report-walkthrough.md — how to present the scan report
• templates/apply-confirmation.md — confirm-before-write flow

Common failure modes to avoid

1. Running apply without --dry-run first.
2. Silently accepting low-confidence stage (always ask).
3. Hallucinating suggestion ids (cross-check the report).
4. Treating --with-github as default (it's opt-in).
5. Confusing this with kb-doc / kb-discovery (different skills).