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.
$ 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.
| 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.
macOS / Linux:
curl -fsSL https://raw.githubusercontent.com/micpet7514088/autogap/main/install.sh | bashWindows (PowerShell 7+):
Invoke-RestMethod https://raw.githubusercontent.com/micpet7514088/autogap/main/install.ps1 | Invoke-ExpressionRestart 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 # WindowsManual install options
Clone + symlink (updates propagate automatically on pull):
git clone https://github.com/micpet7514088/autogap.git ~/autogap
bash ~/autogap/install.shProject-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.mdFile copy: Copy skills/autogap/SKILL.md into any directory Claude Code scans for skills.
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 outputexamples/demo-02-docs/— CIKM 2026 paper, 50 days to deadline → expected outputexamples/demo-03-strategy/— Q3 product launch with top-3 LOI contingency → expected output
See examples/ for the full set and the run instructions.
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.
- Classify project type from file signals (folder tree, file types, naming, metadata). No software default.
- Infer the top-value objective the project is trying to reach. State confidence, name the strongest alternative hypothesis.
- Scan breadth-first. Deepen on the richest paths. Sample ≥1 artifact per distinct family (code / docs / config / media). Run a disconfirmation pass before closing.
- Rank gaps by blocker strength, not cosmetic polish. "Nice-to-have" is not a gap.
- Present a 3-gap / 3-macro-step menu. Stop. No auto-execute.
- 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.
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.
MIT — use freely, attribution appreciated.
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.