autogap

The first Claude Code skill that diagnoses the top 3 gaps in any project — code or not.

Give autogap a project directory. It classifies the project type from file signals (not assumptions), infers the top-value objective the project is reaching for, and reports the 3 biggest gaps blocking that objective plus 3 macro-steps to close them. Then it hands you a numbered menu and waits. If you pick, it plans and executes autonomously.

It works on:

• source code — single package or monorepo
• documentation sets — research notes, spec folders, knowledge bases
• strategy / business — roadmaps, OKRs, decks, briefs
• creative / content — drafts, outlines, edit lists
• ops / infra — runbooks, playbooks, inventories
• hybrid — whatever you've actually got

Most agent skills assume software. autogap doesn't.

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30-second example

$ cd research-project/
$ claude
> /autogap

# Project Objective Hypothesis
- Publish a peer-reviewed paper on X by Q3 2026
- Project type: research
- Confidence: High
- Signals: 3 draft sections, referee feedback letter, 47-entry BibTeX,
  methods/ folder with 2 Jupyter notebooks, reproducibility/ empty

# Top 3 Gaps
## Gap 1 — Methods section incomplete
  Why it matters: editors reject without reproducible methodology.
  Evidence: methods.md is 40% length of Background + has 3 TODO markers.
  Consequence: guaranteed R&R loop, likely 3-month delay.

## Gap 2 — No reproducibility kit
  Why it matters: target journal requires data + code on acceptance.
  Evidence: reproducibility/ directory empty; notebooks have no seeds.
  Consequence: acceptance delayed until kit is produced.

## Gap 3 — Discussion skirts disconfirmation
  Why it matters: weakest reviewer target; editor likely flags.
  Evidence: 0 mentions of limitations; no alternative-explanation section.
  Consequence: weakest part of the paper, most reviewer ammunition.

# 3 Macro-Steps
## Macro-Step 1 — Complete methods from lab notes (Low / High / High)
## Macro-Step 2 — Produce reproducibility kit  (Medium / High / Medium)
## Macro-Step 3 — Add disconfirmation to discussion (Low / Medium / High)

Choose what to implement:
1. Macro-Step 1 only
2. Macro-Step 2 only
3. Macro-Step 3 only
4. Macro-Steps 1 + 2
...

Pick a number. autogap plans, executes, verifies, and reports exactly what changed.

No user confirmation on every step. No fake completion. Stops cleanly on ambiguous irreversible moves.

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Why it's different

| Most skills | autogap | |---|---| | Assume your project is code | Classifies project type from signals | | Suggest a long list of improvements | Ranks exactly 3 gaps, by blocker strength | | Lecture best practices | Infers what the project is trying to become | | Ask you 20 questions upfront | Deep-inspects first, asks ≤2 questions only if blocked | | Execute everything or nothing | User picks a subset via menu, then autonomous exec | | Silent on uncertainty | Confidence + strongest alt hypothesis, explicit |

Full thinking in docs/design-rationale.md.

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Install

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/micpet7514088/autogap/main/install.sh | bash

Windows (PowerShell 7+):

Invoke-RestMethod https://raw.githubusercontent.com/micpet7514088/autogap/main/install.ps1 | Invoke-Expression

Restart Claude Code. From any project directory: claude → /autogap.

To update after a new release, clone the repo once and use the --update / -Update flag:

git clone https://github.com/micpet7514088/autogap.git ~/autogap
bash ~/autogap/install.sh --update     # macOS / Linux
# .\autogap\install.ps1 -Update        # Windows

Clone + symlink (updates propagate automatically on pull):

git clone https://github.com/micpet7514088/autogap.git ~/autogap
bash ~/autogap/install.sh

Project-level skill (per-project, no clone required):

mkdir -p .claude/skills/autogap
curl -sSL https://raw.githubusercontent.com/micpet7514088/autogap/main/skills/autogap/SKILL.md \
  -o .claude/skills/autogap/SKILL.md

File copy: Copy skills/autogap/SKILL.md into any directory Claude Code scans for skills.

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Examples you can run

Three demo projects, one per major project type. Each one has an expected-output file so you can compare what autogap produces against what it should produce.

• examples/demo-01-code/ — Python CLI blocked on PyPI release → expected output
• examples/demo-02-docs/ — CIKM 2026 paper, 50 days to deadline → expected output
• examples/demo-03-strategy/ — Q3 product launch with top-3 LOI contingency → expected output

See examples/ for the full set and the run instructions.

Case studies

Real diagnoses on real projects. Some are anonymized; one is a fully public post-mortem.

• Accessibility audit on two production sites — 88/95 → 100/100 in one day Diagnosis, fix, deploy, certification. Includes a real drift incident + recovery. Fully public, real commit SHAs.
• NIS2 incident response audit on a European insurer Independent audit, 3 parallel workstreams, DR failover error caught in tabletop. Anonymized.
• AI Coach fine-tuning for a healthcare vertical — retention +17% Response-variance + semantic fix + within-session latency bug. Anonymized.

See docs/case-studies/ for the full set.

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How it works, briefly

1. Classify project type from file signals (folder tree, file types, naming, metadata). No software default.
2. Infer the top-value objective the project is trying to reach. State confidence, name the strongest alternative hypothesis.
3. Scan breadth-first. Deepen on the richest paths. Sample ≥1 artifact per distinct family (code / docs / config / media). Run a disconfirmation pass before closing.
4. Rank gaps by blocker strength, not cosmetic polish. "Nice-to-have" is not a gap.
5. Present a 3-gap / 3-macro-step menu. Stop. No auto-execute.
6. On pick: plan tight, execute autonomously, verify proportional, report compactly with 3 next-steps.

Architecture choices, trade-offs, and the "why 3 (not 5, not 7)" question: docs/design-rationale.md.

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Contributing

See CONTRIBUTING.md. Short version:

• Real use cases welcome, theoretical improvements discouraged.
• One change per PR, closing one concrete gap.
• No SaaS wrappers. No scope creep "just in case".

Issues and PRs: https://github.com/micpet7514088/autogap.

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License

MIT — use freely, attribution appreciated.

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Authors

Built by the team behind:

• Synergix — fractional IT direction and AI governance for European finance and insurtech mid-caps. 20+ years of enterprise delivery applied to AI programs.
• Arenia — AI coaching avatars for corporate training. Realistic, scalable practice with measurable KPI frameworks.

autogap was battle-tested on our own client work before we open-sourced it.