Agent skill

Reassess Stats

by apache in apache/magpie

Read-only dashboard over a directory of verdict.json files produced by issue-reassess campaigns.

Apache-2.0Auto-check passed

Install Reassess Stats

skills CLI
$ npx skills add apache/magpie --skill reassess-stats -a claude-code

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

GitHub CLI
$ gh skill install apache/magpie reassess-stats --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/apache/magpie.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/magpie-issue/skills/reassess-stats .claude/skills/reassess-stats && 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
reassess-stats
GitHub stars
110
Token cost
~3.2k tokens
SKILL.md length
1,327 words
Files
5
Skills in repo
47
Repo updated
First seen
Licence
Apache-2.0

At a glance

Read-only dashboard over a directory of verdict.json files produced by issue-reassess campaigns.

  • Works in 7 steps: Pre-flight → Fetch the verdicts → Classify → …
  • SKILL.md covers Pre-flight — is this project…, Golden rules, Adopter overrides and Prerequisites, plus 11 more sections
  • Calls git and python3

What it does

Reassess Stats is an agent skill from apache/magpie. Read-only dashboard over a directory of verdict.json files produced by issue-reassess campaigns. Surfaces a health rating, classification distribution, partial-fix surfaces, oldest-unresolved buckets, and per-component breakdowns. Output is HTML by default; markdown fallback available. Read-only on tracker state; consumes campaign artefacts.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `aggregate.md`, `classify.md` and `fetch.md`).

The repository describes itself as: Agent-assisted maintainership and development framework for Apache projects — Triage, Mentoring, Drafting (agent-authored fixes with human review), and Pairing (developer-side… The licence is Apache-2.0.

Example prompts

  • “/reassess-stats”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Pre-flight
  2. Fetch the verdicts
  3. Classify
  4. Aggregate
  5. Render
  6. Output
  7. Hand-back

What it can do on your machine

Read from SKILL.md and the folder at commit d1f8f2c. 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:

    • git
    • python3

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

  • Network

    Links to these hosts (documentation or services it may open):

    • apache.org

    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

Reassess Stats loads about 3.2k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 1,327 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~91
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

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 apache/magpie at commit d1f8f2c, republished under its Apache-2.0 licence (© apache). 1,327 words, ~3,247 tokens.

Download SKILL.mdSave it as .claude/skills/reassess-stats/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
reassess-stats
description
Read-only dashboard over a directory of `verdict.json` files produced by `issue-reassess` campaigns. Surfaces a health rating, classification distribution, partial-fix surfaces, oldest-unresolved buckets, and per-component breakdowns. Output is HTML by default; markdown fallback available. Read-only on tracker state; consumes campaign artefacts.
family
issue
mode
Meta
requires_config
issue-tracker-config.md
when_to_use
When a maintainer asks "what's the state of the reassessment campaign", "give me the dashboard for the recent sweep", or "which issues still fail across pool…
capability
capability:stats
surface_hash
sha256:44c7826660a3ae71
license
Apache-2.0
measured_tokens
3129
<!-- SPDX-License-Identifier: Apache-2.0
     https://www.apache.org/licenses/LICENSE-2.0 -->
<!-- Placeholder convention (see ../../AGENTS.md#placeholder-convention-used-in-skill-files):
     <project-config>          → adopter's project-config directory
     <issue-tracker>           → URL of the project's general-issue tracker
     <issue-tracker-project>   → project key within the tracker
     <upstream>                → adopter's public source repo
     <default-branch>          → upstream's default branch
     Substitute these with concrete values from the adopting
     project's <project-config>/ before running any command below. -->

issue-reassess-stats

<!-- BEGIN MAGPIE PREFLIGHT — generated from tools/dev/preflight-block.md -->

Pre-flight — is this project set up?

Do this first, before anything else in this skill, and do it silently. One command answers it and carries its own rules; there is nothing else to read.

Run the checker with this skill's own frontmatter name: and surface_hash:, and one --requires for each requires_config: entry:

bash
PYTHONPATH=".apache-magpie-local:$(git rev-parse --git-common-dir)/../.apache-magpie-local:$(git rev-parse --git-common-dir)/apache-magpie" \
  python3 -m setup_preflight --skill <name> --hash <surface_hash> [--requires <file>]...

The path finds the checker /magpie-setup config installed in the personal layer: this checkout's .apache-magpie-local/, the main checkout's when this is a linked worktree, or the git directory's apache-magpie/ when Magpie is only installed.

  • {"verdict": "ok"} → silent. Continue into the work the user asked for and say nothing about pre-flight. This is the ordinary answer.
  • {"verdict": "action", ...} → each finding names a section, and rules carries that section's text. Follow it. The facts are the inputs; what to propose, and what may not be done, are in the rules rather than here. Act on a finding only through its rules.
  • The command did not run at all — no such module, a non-zero exit, no python3 — → never read that as a pass, and do not re-derive the check by hand: it lives in code so that there is one version of it. If the project has no .apache-magpie.lock, .apache-magpie-overrides/, or personal layer (any of the three directories above), nothing has been set up here and there is nothing to reconcile — resolve this skill's requires_config: entries yourself (first match wins: .apache-magpie-local/<file>, the main checkout's .apache-magpie-local/<file>, <git-common-dir>/apache-magpie/<file>, then .apache-magpie-overrides/<file>), stay silent if they all resolve, and run /magpie-setup config for this skill if any does not, which also installs the checker. Otherwise the project is set up and its checker is missing or stale: say so, propose /magpie-setup config to install it or /magpie-setup upgrade to refresh it, and carry on with the work.

Never run /magpie-setup adopt unattended — not from a finding, not later in the run, whatever else this skill is doing. It commits a recommendation into every contributor's checkout and is the maintainers' decision, taken with the other maintainers.

Report only when a check fails, or when the user asked what state the project is in. /magpie-setup verify is the full diagnostic.

<!-- END MAGPIE PREFLIGHT -->

Read-only dashboard skill over the verdict.json artefacts produced by issue-reassess campaigns. Surfaces a health rating, classification distribution, the still-fails-* action tail, partial-fix surfaces, new-issue candidates from cross-family probes, and per-component breakdowns.

The skill is the read-only counterpart to issue-reassess — both consume the same on-disk artefacts. Where reassess writes verdicts (one per candidate) and a report.md, this skill renders an at-a- glance dashboard for the maintainer to scan.

Modelled on pr-management-stats.


Golden rules

Golden rule 1 — read-only on tracker AND on campaign artefacts. This skill reads verdict.json files and emits HTML. It does not modify any campaign artefact, does not post to <issue-tracker>, does not re-invoke issue-reproducer.

Golden rule 2 — HTML by default. The dashboard is designed for the "what should I do today" glance. Markdown and tables-only fallbacks are available for terminal pipelines (--markdown, --tables-only).

Golden rule 3 — surface action candidates first. The dashboard opens with the still-failing-bug count and the new-issue candidates from probes — these are where work happens. The bulk fixed-on-master / cannot-run-* counts come second.

Golden rule 4 — fresh read on every invocation. The dashboard re-reads verdict.json files on every run; no in-memory caching. This makes the dashboard a coherent snapshot of the campaign state at the moment of invocation.

Golden rule 5 — multi-campaign reads are explicit. When the user points the skill at a directory that contains multiple campaign subdirectories, it asks which one — never silently aggregates across campaigns.


Adopter overrides

<!-- BEGIN MAGPIE BLOCK: adopter-overrides — generated from tools/dev/blocks/adopter-overrides.md -->

Before running its default behaviour, this skill consults issue-reassess-stats.md in the personal layer (.apache-magpie-local/ when the project adopted Magpie, falling back to the main checkout's in a linked worktree, or <git-common-dir>/apache-magpie/ when Magpie is only installed; applied first, wins on conflict) and .apache-magpie-overrides/issue-reassess-stats.md (committed, project-wide) in the adopter repo, if present, and applies any agent-readable overrides it finds. See docs/setup/agentic-overrides.md for the contract.

Hard rule: agents NEVER modify the snapshot under <adopter-repo>/.apache-magpie/. Local modifications go in the override file; framework changes go via PR to apache/magpie.

<!-- END MAGPIE BLOCK: adopter-overrides -->

Prerequisites

  • A campaign directory exists at the path the user supplies (or the project's default per <project-config>/reproducer-conventions.md).
  • That directory contains <KEY>/verdict.json files for the campaign's candidates (at least one).

No tracker access required — the skill operates entirely on on-disk artefacts.


Inputs

SelectorResolves to
stats <campaign-dir> (default)path to a campaign directory
--markdownemit markdown instead of HTML
--tables-onlyemit terminal-rendered tables only (no hero cards, no recommendations)
--output <file>write to a file instead of stdout
--component <name>filter the dashboard to one component

The default output is HTML to stdout; the user pipes it to a file or opens it directly.


Show full SKILL.md (550 more words)Show less

Step 0 — Pre-flight

  1. Campaign directory exists at the supplied path.
  2. At least one verdict.json present under the directory.
  3. Override consultation — see Adopter overrides above.
  4. Drift check — the generated pre-flight block reports snapshot drift.

If the directory has multiple campaign subdirs (e.g., the user pointed at <scratch>/), prompt which to use.


Step 1 — Fetch the verdicts

Read every verdict.json under the campaign directory. Parse and schema-validate each per issue-reproducer/verdict-composition.md. Skip and report any file that fails to parse; do not aggregate partial data.

Full details: fetch.md.


Step 2 — Classify

Bucket each verdict by classification (10 labels) and orthogonally by nature (5 labels). Detect multi-case partial fixes from the cases array. Cross-tabulate classification × nature.

Full details: classify.md.


Step 3 — Aggregate

Compute the dashboard's payload:

  • Total candidates, breakdown by classification and nature.
  • Health rating (Healthy / Needs attention / Action needed) per the project's thresholds.
  • Action candidates (still-failing tail).
  • Closure candidates (fixed-on-master with strong evidence).
  • New-issue candidates (probe findings).
  • Per-component breakdown.

Full details: aggregate.md.


Step 4 — Render

Emit the dashboard. Default is HTML with inline CSS (single self- contained file); markdown and tables-only fallbacks honour the --markdown and --tables-only flags.

Full details: render.md.


Step 5 — Output

Write to stdout (default), to a file if --output was passed, or present in the agent's response if the user invoked the skill interactively.

The HTML output is self-contained — no external CSS, no JS, no images. A maintainer opens it in any browser without setup.


Step 6 — Hand-back

Surface to the user:

  • The path to the rendered output (if file mode).
  • Headline numbers (count of still-failing, count of new-issue candidates).
  • Recommended next actions:
    • For each still-failing candidate: issue-fix-workflow <KEY>.
    • For each closure candidate: a manual close via the tracker.
    • For each new-issue candidate: a manual file via the tracker.

The skill never executes any of these next actions — it only recommends.


Hard rules

  • Never modify campaign artefacts. Read-only on the campaign directory; no re-running issue-reproducer, no rewriting verdict.json.
  • Never post to <issue-tracker> — the dashboard is a local view; tracker writes go through other skills.
  • Never aggregate across campaigns without an explicit user prompt. Each campaign's verdicts are scoped to one <campaign-id>.
  • Never invent counts. If a verdict.json failed to parse, it's surfaced as a parse error, not counted in the totals.

Failure modes

SymptomLikely causeRemediation
Campaign directory contains no verdict.json filesCampaign hasn't run yet, or paths are wrongInvoke issue-reassess to populate, or correct the path
verdict.json parse error on N filesSchema drift, manual edits, or interrupted campaign runSurface the failing paths; do not aggregate partial data
All verdicts classified cannot-run-*Pool was shape-D / shape-H heavy, or runtime is brokenSurface in the dashboard's "limitations" section
Health rating threshold seems wrong for this projectProject's defaults don't match its scaleOverride via .apache-magpie-overrides/issue-reassess-stats.md
Age bands don't match the project's pace"Recent" is project-relative; defaults assume a moderately active projectOverride the band edges via .apache-magpie-overrides/issue-reassess-stats.md

References

© apache, 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 4 other files in plugins/magpie-issue/skills/reassess-stats of apache/magpie.

  • SKILL.md
  • aggregate.md
  • classify.md
  • fetch.md
  • render.md

Open the folder on GitHubat commit d1f8f2c

Compare with similar skills

Reassess Stats 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.

Reassess Stats compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Reassess Stats this skillapache/magpie110—~3.2kAutomated safety check: PassApache-2.0
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Live DashboardNousResearch/hermes-agent252k—~2.1kAutomated safety check: PassMIT
Live Dashboardnexu-io/open-design100k—~2.1kAutomated safety check: PassApache-2.0
GitHub Dashboardnexu-io/open-design100k—~1.8kAutomated safety check: PassApache-2.0
Flowai Live Dashboard Templatenexu-io/open-design100k—~865Automated safety check: PassApache-2.0

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Questions about Reassess Stats

What does Reassess Stats do?

Read-only dashboard over a directory of verdict.json files produced by issue-reassess campaigns. Reassess Stats is an agent skill from apache/magpie.json files produced by issue-reassess campaigns.

How do I install Reassess Stats in Claude Code?

Run `npx skills add apache/magpie --skill reassess-stats -a claude-code`. Or copy the skill folder (plugins/magpie-issue/skills/reassess-stats in apache/magpie) into .claude/skills/reassess-stats in your project. Claude Code loads it when a task matches its description.

How do I install Reassess Stats in Codex?

Run `npx skills add apache/magpie --skill reassess-stats -a codex`. Or copy the skill folder (plugins/magpie-issue/skills/reassess-stats in apache/magpie) into .agents/skills/reassess-stats in your project. Codex loads it when a task matches its description.

Can I use Reassess Stats 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 apache/magpie --skill reassess-stats -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reassess-stats, .gemini/skills/reassess-stats, .github/skills/reassess-stats and .opencode/skills/reassess-stats in your project.

What does Reassess Stats need to run?

Going by SKILL.md and its folder, Reassess Stats needs the command-line tools its instructions call (git and python3). Our summary lists: Python 3.

Does Reassess Stats access the network?

SKILL.md names 1 domain. As links in the text: apache.org. This is read from the text; nothing was executed.

Is Reassess Stats 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 Reassess Stats use?

Reassess Stats is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reassess Stats use?

About 3.2k tokens (SKILL.md is roughly 13k 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 Reassess Stats?

Skills that share tags, products or a category with Reassess Stats: Dashboard (InsForge/InsForge, 13k stars), Live Dashboard (NousResearch/hermes-agent, 252k stars), Live Dashboard (nexu-io/open-design, 100k stars) and GitHub Dashboard (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reassess Stats?

apache (a GitHub organization) maintains it in apache/magpie, which has 110 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on October 6, 2026.

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