agentleFS
hamzzaqureshihamzzaqureshi/gravidex/.claude/skills/rigor/SKILL.md

rigor

Skill1 starsChanged 9 days agoMITView on GitHub

Engineering discipline for AI coding agents: grounding against current docs, verifiable task ledgers, cross-model review, and strict completion gates. Use before claiming work is complete, when planning complex multi-step implementations, or when verifying unfamiliar APIs.

5 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 7 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Grounding Protocol: Verify external libraries, APIs, and CLI flags against primary sources before coding.

    line 11
  2. Distinctive:

    Hypothesis vs Fact: Treat what you remember about an API, CLI flag, version, or platform constraint as a hypothesis.

    line 33
  3. Distinctive:

    Keep tasks between 2 to 5 minutes of work: exact file paths, interfaces consumed/produced, and tests written first.

    line 49
  4. Distinctive:

    Before uttering any variant of:

    line 83
  5. Distinctive:

    Before declaring any task or milestone finished.

    line 107
  6. Distinctive:

    When committing, merging, or releasing production code.

    line 108
  7. Distinctive:

    Before writing code against third-party APIs or unfamiliar CLI tools.

    line 109
  8. Very specific:

    When a user asks for /rigor, "prove it", "check your work", or "be thorough".

    line 110

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Rigor: Engineering Discipline for AI Agents

"Model memory is a hypothesis. A fact needs evidence. If you did not run it just now, you do not know it."

Rigor synthesizes high-reliability engineering discipline into 4 non-negotiable verification gates:

  1. Grounding Protocol: Verify external libraries, APIs, and CLI flags against primary sources before coding.
  2. Task Ledger: Break work into atomic, verifiable tasks with persistent disk-backed state surviving context loss.
  3. Cross-Model Review: Dispatch critical diffs to an independent model family or subagent to eliminate blindspots.
  4. Completion Gate: Disallow any claim of "done", "fixed", or "passing" without fresh execution output in that exact message.

The 4 Gates of Rigor

flowchart LR
    G1["1. Grounding\nVerify APIs & Docs"] --> G2["2. Task Ledger\nAtomic 2-5 min tasks"]
    G2 --> G3["3. Review\nCross-model audit"]
    G3 --> G4["4. Completion Gate\nFresh terminal proof"]

Gate 1: Grounding Protocol

The Rule: No fact that can change enters code or an answer unverified.

  • Hypothesis vs Fact: Treat what you remember about an API, CLI flag, version, or platform constraint as a hypothesis.
  • Climb the Ladder:
    1. Primary source 1: Installed tool (tool --version, tool --help, one-line probe).
    2. Primary source 2: Local project types, lockfile, source code (package.json, Cargo.lock, poetry.lock).
    3. Primary source 3: Official docs, GitHub repository releases, changelogs.
  • Helper Command:
    python3 -B /path/to/rigor/scripts/rigor_engine.py ground <tool_or_package>

Gate 2: Task Ledger & Atomic Briefs

The Rule: No complex implementation proceeds without atomic tasks and disk-backed state.

  • Keep tasks between 2 to 5 minutes of work: exact file paths, interfaces consumed/produced, and tests written first.
  • Maintain disk-backed progress under <repo>/.rigor/runs/<slug>/ so progress survives context window compaction and subagent isolation.
  • Helper Commands:
    # Initialize plan workspace
    ws=$(python3 -B /path/to/rigor/scripts/rigor_engine.py workspace docs/plans/feature.md)
    
    # Extract brief for Task 1
    brief=$(python3 -B /path/to/rigor/scripts/rigor_engine.py brief docs/plans/feature.md 1 "$ws")
    
    # Log progress and check status
    python3 -B /path/to/rigor/scripts/rigor_engine.py ledger "$ws" "Task 1: complete (tests green)"
    python3 -B /path/to/rigor/scripts/rigor_engine.py status "$ws"

Gate 3: Cross-Model Review

The Rule: Before landing critical code, get an outside read from another model family or independent subagent.

  • Models of the same family share identical blindspots and common failure modes.
  • Dispatch a read-only review with 3–5 checkable, falsifiable questions (see cross-model-brief.md).
  • Helper Command:
    python3 -B /path/to/rigor/scripts/rigor_engine.py review-package main HEAD "$ws"

Gate 4: Completion Gate

The Rule: NO CLAIM OF DONE, FIXED, OR PASSING WITHOUT FRESH VERIFICATION OUTPUT IN THIS MESSAGE.

Before uttering any variant of:

  • "I have fixed the bug",
  • "All tests are passing",
  • "The feature is complete",
  • "Everything is working as expected",

You must run the command in the terminal and display the raw exit code and output in the response alongside an Evidence Table:

ClaimCommand or CheckResult (Observed in This Message)Status
Tests passpytest tests/24 passed, 0 failed in 0.35s, exit 0Verified
Types cleanmypy src/Success: no issues found in 12 source filesVerified
Bug reproduced & resolvedpython test_repro.pyFails before; exits 0 with fix appliedVerified
  • Validator Command:
    python3 -B /path/to/rigor/scripts/rigor_engine.py verify-gate "<response_text_or_file>"

When to Use

  • Required:
    • Before declaring any task or milestone finished.
    • When committing, merging, or releasing production code.
    • Before writing code against third-party APIs or unfamiliar CLI tools.
    • When a user asks for /rigor, "prove it", "check your work", or "be thorough".
  • Skip:
    • Fast exploratory brainstorming and throwaway scratchpad spikes.
    • Simple 1-line conversational queries that do not modify files or make verifiable claims.

References

  • Completion Gate & Evidence Table
  • The 5-Rung Grounding Ladder
  • Cross-Model Review Brief Template
  • Task Ledger Specification

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.