Agent skill

Roam Evidence Hardening

by Cranot in Cranot/roam-code

Investigate and harden Roam detectors, CLI/MCP result contracts, and evidence consumers when dogfooding or correcting incomplete, misleading, or inconsistent analysis.

Apache-2.0Auto-check passedDevelopment

Install Roam Evidence Hardening

skills CLI
$ npx skills add Cranot/roam-code --skill roam-evidence-hardening -a claude-code

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

GitHub CLI
$ gh skill install Cranot/roam-code roam-evidence-hardening --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/Cranot/roam-code.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/roam-evidence-hardening .claude/skills/roam-evidence-hardening && 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
roam-evidence-hardening
GitHub stars
517
Token cost
~1.5k tokens
SKILL.md length
766 words
Files
2
Skills in repo
9
Repo updated
First seen
Licence
Apache-2.0

At a glance

Investigate and harden Roam detectors, CLI/MCP result contracts, and evidence consumers when dogfooding or correcting incomplete, misleading, or inconsistent analysis.

  • A requested hardening pass
  • SKILL.md covers Establish the question and…, Interpret before repairing and Qualify the smallest correction
  • Calls uv
  • A suspected evidence defect

What it does

Roam Evidence Hardening is an agent skill from Cranot/roam-code. Investigate and harden Roam detectors, CLI/MCP result contracts, and evidence consumers when dogfooding or correcting incomplete, misleading, or inconsistent analysis. Use for a requested hardening pass, a suspected evidence defect, or unresolved Roam result/delivery state, not routine feature edits, product copy, or release approval.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Development, covering Copywriting and MCP servers. It works with Model Context Protocol. The repository describes itself as: Local codebase intelligence CLI + MCP server for AI coding agents: SQLite code graph, 28 languages, 287 commands, 246 MCP tools, change-safety gates, audit evidence, zero API keys. The licence is Apache-2.0.

When your agent uses it

  • A requested hardening pass
  • A suspected evidence defect
  • Unresolved Roam result/delivery state
  • Not routine feature edits

Example prompts

  • “/roam-evidence-hardening”

What it can do on your machine

Read from SKILL.md and the folder at commit f0bdb63. 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

    Shell commands in SKILL.md call:

    • uv

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

  • Network

    No URLs in SKILL.md. Its commands use uv, 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

Roam Evidence Hardening loads about 1.5k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 766 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from Cranot/roam-code at commit f0bdb63, republished under its Apache-2.0 licence (© Cranot). 766 words, ~1,483 tokens.

Download SKILL.mdSave it as .claude/skills/roam-evidence-hardening/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
roam-evidence-hardening
description
Investigate and harden Roam detectors, CLI/MCP result contracts, and evidence consumers when dogfooding or correcting incomplete, misleading, or inconsistent analysis. Use for a requested hardening pass, a suspected evidence defect, or unresolved Roam result/delivery state, not routine feature edits, product copy, or release approval.

Roam evidence hardening

Turn one concrete uncertainty into a reproduced defect, a supported correction, or a justified no-change decision. More findings and more refusals are not the objective. The consumer must distinguish a measured result from an unavailable measurement without losing useful observations.

Establish the question and boundary

Resolve the checkout and read its current AGENTS.md and docs/concepts/verification-evidence.md. For detector claims also read docs/concepts/detector-evidence.md; for environment/index trouble use docs/repository-maintenance.md. These are maintained authorities, not rules to copy permanently into this skill. Read the relevant implementation when a source/doc disagreement affects the decision.

Separate a request to diagnose from authorization to change code. In a broad hardening request, choose a bounded seam with a plausible consumer consequence; name what observation would disprove the concern. Follow the actual producer, serialization/dispatch, wrapper and acting consumer. Do not stop at a helper whose output never reaches that consumer.

Keep the correction's job explicit. A prose-only instruction error needs checking against the real contract, not an invented runtime failure. A relevant red/green regression qualifies a repair, not a comparative speed or workflow-value claim; qualify that comparison separately without holding a concrete repair for a study.

Capture the selected files/diff, including relevant untracked content, before analysis. Check the diff producer succeeded; empty input is not permission to review a different commit. Use the checkout's .venv/Scripts/roam.exe on Windows or its verified venv equivalent (such as uv run --no-sync roam), not bare PATH roam. Preserve an explicitly requested launcher. Inspect current help for flag placement and command names. Use full JSON for evidence acted on; save exact argv, cwd, stdout, stderr, exit, revision/diff identity and index state under ignored internal/. A help call is not an analysis run.

Interpret before repairing

Check execution, input scope, computation completeness and delivery separately. Exit 0, an empty findings list or a valid schema alone proves none of these. Absent, malformed or contradictory evidence stays unknown; preserve legitimate zeros and valid findings from completed portions. A zero-scan result cannot establish absence in the requested population. Read command-specific status, denominators, metric definitions and bounds, not invented universal fields.

Distinguish intentional detail_mode elision from token-budget truncation, computation caps and unavailable inputs. A completed, current zero-finding scan can be valid within its stated bounds. Do not call intentional presentation elision a failed computation. Conversely, removing --budget limits does not repair a failed scanner or broaden the indexed population. Fetch an MCP handle before consuming its stored analysis; a preview is not the full artifact.

A valid gate-negative result is different from transport failure. Stderr may contain diagnostics alongside valid stdout JSON. A response-storage failure may occur after the operation took effect: inspect its outcome and recover delivery before deciding whether a retry is safe, especially for mutating tools.

For a detector finding, inspect the matched source and its assumptions: receiver identity, loop placement, mutation/order, language/framework and graph resolution. A method name or heuristic score is a lead, not a demonstrated defect. Preserve metric meaning when comparing commands. A source hash proves identity, not the truth or completeness of the reported result.

Show full SKILL.md (260 more words)Show less

Qualify the smallest correction

Freeze a defect-specific test and observe its relevant failure on the unfixed implementation in an isolated setup. Run the same test on the repair with valid controls; for a false-positive fix retain a genuine positive. If expected behavior changes, repeat both sides. Import errors or empty collection do not establish the regression. Then exercise the real CLI or serialized boundary and the consumer decision, including incomplete and valid inputs.

Keep tests independent of accidental clock, network, shared index and module- cache state. Real filesystem/subprocess integration can be necessary: use a controlled fixture and bounded lifecycle, and explain what it verifies. Do not replace the very boundary under test with a mock. Measure proposed optimizations against a simpler baseline while preserving semantics and evidence limits.

Run affected tests and the project's required gates in proportion to the actual diff. A static test mapping is not execution; skipped tests remain skipped. Use bounded workers and isolate index writers. Do not execute write/network commands merely to claim complete dogfood coverage; record the authority gap. Within the selected dogfood scope, run safe read-only checks rather than judging them inapplicable without trying them. Verify prerequisites and side effects; a completed zero is an observation, while zero scanned cannot establish absence.

Finish with the defect/no-change decision, exact evidence, repair scope, surviving uncertainties and next useful action. Update handwritten behavior docs or their owning generators as appropriate; regenerate generated output rather than hand-editing it. Preserve historical records, and keep new dated operational measurements private. Separate local repair qualification from whole-release review and publication.

© Cranot, Apache-2.0. 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 1 other file in .agents/skills/roam-evidence-hardening of Cranot/roam-code.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit f0bdb63

Compare with similar skills

Roam Evidence Hardening 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.

Roam Evidence Hardening compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Roam Evidence Hardening this skillCranot/roam-code517—~1.5kAutomated safety check: PassApache-2.0
Lemlist Campaign From IcpOthmane-Khadri/YALC-the-GTM-operating-system318—~6.5kAutomated safety check: PassMIT
Analyze Logsactivepieces/activepieces25k1 repos~1.6kAutomated safety check: PassMIT
ReleasePrefectHQ/fastmcp28k—~2.9kAutomated safety check: PassApache-2.0
Review PRPrefectHQ/fastmcp28k—~3.1kAutomated safety check: PassApache-2.0
ObservalObserval/Observal4.3k—~2.2kAutomated safety check: PassApache-2.0

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Questions about Roam Evidence Hardening

What does Roam Evidence Hardening do?

Investigate and harden Roam detectors, CLI/MCP result contracts, and evidence consumers when dogfooding or correcting incomplete, misleading, or inconsistent analysis. Roam Evidence Hardening is an agent skill from Cranot/roam-code. Investigate and harden Roam detectors, CLI/MCP result contracts, and evidence consumers when dogfooding or correcting incomplete, misleading, or inconsistent analysis.

When should I use Roam Evidence Hardening?

Roam Evidence Hardening fits situations like: A requested hardening pass; A suspected evidence defect; unresolved Roam result/delivery state; not routine feature edits.

How do I install Roam Evidence Hardening in Claude Code?

Run `npx skills add Cranot/roam-code --skill roam-evidence-hardening -a claude-code`. Or copy the skill folder (.agents/skills/roam-evidence-hardening in Cranot/roam-code) into .claude/skills/roam-evidence-hardening in your project. Claude Code loads it when a task matches its description.

How do I install Roam Evidence Hardening in Codex?

Run `npx skills add Cranot/roam-code --skill roam-evidence-hardening -a codex`. Or copy the skill folder (.agents/skills/roam-evidence-hardening in Cranot/roam-code) into .agents/skills/roam-evidence-hardening in your project. Codex loads it when a task matches its description.

Can I use Roam Evidence Hardening 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 Cranot/roam-code --skill roam-evidence-hardening -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/roam-evidence-hardening, .gemini/skills/roam-evidence-hardening, .github/skills/roam-evidence-hardening and .opencode/skills/roam-evidence-hardening in your project.

What does Roam Evidence Hardening need to run?

Going by SKILL.md and its folder, Roam Evidence Hardening needs the command-line tools its instructions call (uv).

Does Roam Evidence Hardening access the network?

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

Is Roam Evidence Hardening 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. Review the folder before installing.

What licence does Roam Evidence Hardening use?

Roam Evidence Hardening is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Roam Evidence Hardening use?

About 1.5k tokens (SKILL.md is roughly 5.9k 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 Roam Evidence Hardening?

Skills that share tags, products or a category with Roam Evidence Hardening: Lemlist Campaign From Icp (Othmane-Khadri/YALC-the-GTM-operating-system, 318 stars), Analyze Logs (activepieces/activepieces, 25k stars), Release (PrefectHQ/fastmcp, 28k stars) and Review PR (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Roam Evidence Hardening?

Cranot (a GitHub user) maintains it in Cranot/roam-code, which has 517 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 10, 2026.

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