Agent skill

Meeting Analyzer

by borghei in borghei/Claude-Skills

Turn meeting notes into an accountable register — extract decisions, actions and open questions, flag ownerless items, track follow-through.

MITAuto-check passedProductivity & Automation

Install Meeting Analyzer

skills CLI
$ npx skills add borghei/Claude-Skills --skill meeting-analyzer -a claude-code

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

GitHub CLI
$ gh skill install borghei/Claude-Skills meeting-analyzer --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/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/project-management/meeting-analyzer .claude/skills/meeting-analyzer && 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
meeting-analyzer
GitHub stars
886
Token cost
~3.3k tokens
SKILL.md length
1,687 words
Files
11 (incl. scripts, references, assets)
Skills in repo
354
Repo updated
First seen
Licence
MIT

At a glance

Turn meeting notes into an accountable register — extract decisions, actions and open questions, flag ownerless items, track follow-through.

  • Works in 5 steps: Point the parser at the notes file. It… → Read the completeness rate first.… → Work the flagged gaps in the room or in… → …
  • Commitments get dropped
  • SKILL.md covers Scope note, When to use this skill, Inputs the skill expects and Clarify First, plus 4 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Meeting Analyzer is an agent skill from borghei/Claude-Skills. Turn meeting notes into an accountable register — extract decisions, actions and open questions, flag ownerless items, track follow-through. Use when commitments get dropped or a recurring meeting decides nothing.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts, reference files and assets (for example `assets/action_register_template.md`, `assets/sample_commitments.json` and `assets/sample_notes.md`).

It sits in Productivity & Automation, covering Meeting notes and agendas and Project management. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Commitments get dropped
  • A recurring meeting decides nothing

Example prompts

  • “/meeting-analyzer”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Point the parser at the notes file. It buckets every item into decisions,
  2. Read the completeness rate first. Anything under 60% means the meeting
  3. Work the flagged gaps in the room or in the thread the same day. Asking
  4. Never infer a missing owner or date. A flagged gap is the deliverable — it is
  5. Add --strict in CI over a notes directory to fail the build when a meeting

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. 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

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

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

  • Network

    No URLs in SKILL.md.

    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

Meeting Analyzer loads about 3.3k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 1,687 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 1,687 words, ~3,264 tokens.

Download SKILL.mdSave it as .claude/skills/meeting-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
meeting-analyzer
description
Turn meeting notes into an accountable register — extract decisions, actions and open questions, flag ownerless items, track follow-through. Use when commitments get dropped or a recurring meeting decides nothing.
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
project-management
metadata.domain
pm-execution
metadata.updated
2026-07-21
metadata.tags
meeting-notes, action-items, accountability, decision-log, meeting-health

Meeting Analyzer

Meetings fail at the seam between talking and tracking. The decision gets made and nobody records who approved it; the action gets stated and nobody's name lands on it; the same topic returns for the seventh week because discussing it feels productive and deciding it feels risky. This skill closes that seam with deterministic extraction — pattern rules, no model calls — and then follows the commitments across meetings to see which ones actually close.

Scope note

This is the accountability lens on meetings, not the summary lens. It extracts and tracks; it does not write the polished recap. Use it on notes that already exist, across a series, when the question is "did anything we said we would do actually happen?"

When to use this skill

  • Actions keep getting dropped — items agreed in one meeting quietly reappear three weeks later, or never do
  • A recurring meeting feels pointless — you need decision density and topic churn measured before proposing to cancel it
  • Notes exist but no register does — months of markdown notes with decisions buried in prose that nobody can find or count
  • The same topic returns every week — you need evidence of churn to force a decision owner and a deadline
  • Preparing a retro or a meeting audit — completion rates and ageing per meeting show which rituals produce work that gets done
  • Onboarding onto an existing project — extracting the decision log from past notes reconstructs why the system looks the way it does

Inputs the skill expects

  • Meeting notes or transcripts as markdown or plain text, one file per meeting
  • Ideally several meetings from the same series, so trends are visible
  • An action-item register with created date, due date, owner, status and carry-over count (the parser bootstraps this from notes)
  • Per-occurrence series data: attendance, duration, decisions, actions, whether an agenda was posted and notes published, and the topics covered
  • The reference date for ageing calculations, if not "the newest date in the data"
  • Which meeting types to exclude from decision-density scoring (retros, brainstorms, incident reviews legitimately decide little)

Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Whether owners and dates may be inferred — the answer is no by default; a fabricated due date enters the tracker looking legitimate and is far more damaging than a flagged gap
  • Meeting type — a retro or brainstorm scored on decision density will look broken when it is working exactly as intended
  • The reference date for ageing — "overdue" is meaningless without it, and pulling from the system clock makes yesterday's report irreproducible
  • Who sees the per-owner output — aggregate for the team, per-person only in a 1:1; low follow-through is usually over-assignment, not under-delivery

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflows

Workflow 1 — Extract an accountable register from raw notes
  1. Point the parser at the notes file. It buckets every item into decisions, actions and open questions, and pulls owner and due date from each action.
  2. Read the completeness rate first. Anything under 60% means the meeting produced intentions, not commitments.
  3. Work the flagged gaps in the room or in the thread the same day. Asking "who owns this?" a week later means reconstructing a conversation nobody remembers, and the answer becomes whoever feels most guilty.
  4. Never infer a missing owner or date. A flagged gap is the deliverable — it is the thing that gets fixed.
  5. Add --strict in CI over a notes directory to fail the build when a meeting lands actions with no owner or date.
bash
python3 project-management/meeting-analyzer/scripts/meeting_notes_parser.py \
  --input project-management/meeting-analyzer/assets/sample_notes.md \
  --format text --strict
Workflow 2 — Track follow-through across a series
  1. Append each meeting's extracted actions to a register in assets/sample_commitments.json shape, keeping created, due, owner, status and carried_over.
  2. Run the tracker with an explicit --as-of so the report is reproducible.
  3. Read in this order: completion rate, the ageing distribution, then per meeting. Per-owner comes last and never first.
  4. Act on the 30+ day band — close, reassign, or explicitly drop. An action auto-closed at 30 days that genuinely mattered gets re-opened within a week; one that nobody notices did not matter.
  5. Apply the three-strike rule to anything with carried_over >= 3: re-commit with a date the owner states out loud, or drop it. There is no fourth carry.
bash
python3 project-management/meeting-analyzer/scripts/commitment_tracker.py \
  --input project-management/meeting-analyzer/assets/sample_commitments.json \
  --as-of 2026-07-21 --format text
Workflow 3 — Diagnose a recurring meeting that decides nothing
  1. Assemble 8-12 occurrences with attendance, decisions, actions, agenda and notes compliance, and the topics covered.
  2. Run the diagnostic. It returns decision density, churning topics, attendance trend, and a KEEP / RESTRUCTURE / ASYNC / KILL verdict with a prescription.
  3. Check the exclusions before acting — retros and brainstorms score badly by design and should be removed from the input, not accommodated by lowering the floor.
  4. Take the churning topics to the room with the one diagnostic question: who can actually decide this? The answer picks the fix.
  5. Fix hygiene before cancelling. A series at 30% agenda compliance has not yet been tried as a well-run meeting.
bash
python3 project-management/meeting-analyzer/scripts/meeting_series_diagnostic.py \
  --input project-management/meeting-analyzer/assets/sample_series.json \
  --churn-threshold 3 --format text

Decision frameworks

The action-item completeness gate [PROVEN]
Has ownerHas dateVerdict
YesYes (ISO)Tracked
YesVague ("soon", "next sprint")Gap — the owner believes they committed; nobody else does
YesNoneGap — reads as "someday"
NoEitherGap — an action owned by "the team" is owned by nobody

we, the team, someone, everyone and TBD are never owners, even when they occupy the grammatical slot. Accepting them launders the gap instead of surfacing it.

MetricHealthyWarningBroken
Completion rate75%+50-75%under 50%
Owner and date present90%+70-90%under 70%
On-time closure70%+50-70%under 50%
Open beyond 30 days0-12-45+
Average carry-overunder 11-23+

Carry-over predicts abandonment better than age. An item carried three times has usually been silently deprioritised by its owner but never formally dropped.

Show full SKILL.md (705 more words)Show less
Decision density verdicts [PROVEN]

Density = decisions produced / person-hours consumed.

DensityVerdictAction
0.35+KEEPHealthy: a 60-min meeting of 5 produces 2+ decisions
0.15-0.35RESTRUCTURECut duration 25%; trim to the people who hold the decision
under 0.15ASYNCMove standing content to a written update
under 0.15 and 40%+ empty occurrencesKILLCancel; replace with a written update and a comment window

Exclude retros, brainstorms, incident reviews and onboarding — they decide little by design. Exclude them explicitly rather than lowering the floor, or the floor stops catching the status meetings it exists to catch.

Reading low follow-through

Low follow-through has four common causes and only one of them is the person: over-assignment (one owner holding 40%+ of open actions), deadlines set by someone other than the owner, an unclearable blocking dependency, or a meeting generating more actions than the team has capacity for. When every owner looks bad, the meeting is the problem — see the diagnostic table and per-owner interpretation guidance in references/accountability-and-series-health.md.

Anti-Patterns

Inferring the Missing Owner

Mistake: Filling in a plausible owner or date for an action the notes left blank, so the register looks complete. Why it happens: An incomplete register feels like a failure of the extraction, and a model will happily supply a name. Completeness is mistaken for quality. Instead: Leave it flagged. The gap is the finding — an unowned action is genuinely unowned, and surfacing it is the entire value. Rules under-extract and models over-extract; a fabricated due date enters the tracker looking legitimate and nobody ever questions it again.

The Decorative Register

Mistake: Maintaining a meticulous action list that is reviewed by reading every row aloud at the next meeting, and never acted on. Why it happens: The register becomes a performance of diligence. Reading it all feels thorough, and cutting the review feels like letting standards slip. Instead: Review overdue items and third carry-overs only — under five minutes. Everything on track needs no airtime. A register that consumes twenty minutes a week to change nothing is more expensive than having no register.

Weaponising Per-Owner Data

Mistake: Opening a team meeting with the per-owner follow-through table and asking the bottom name to explain themselves. Why it happens: The data looks like performance data, and it is right there, ranked. Instead: Share follow-through in aggregate with the team and per-person only in a 1:1, as a question rather than a verdict. When every owner looks bad, the meeting is over-generating actions — treating that as several simultaneous performance problems is both wrong and expensive, and it teaches people to accept fewer actions rather than to close more.

Scoring Every Meeting on Decision Density

Mistake: Running the series diagnostic across the whole calendar and proposing to cancel the retro because it produced two decisions in ten weeks. Why it happens: The metric is clean and comparable, which makes it tempting to apply universally. Instead: Exclude retros, brainstorms, incident reviews and onboarding before scoring. Their output is shared understanding, not convergence. Excluding them keeps the floor sharp enough to catch the status meeting it was built for.

Cancelling Before Fixing Hygiene

Mistake: Killing a low-density meeting that never had an agenda or published notes. Why it happens: The density number is damning and cancellation is a satisfying, visible action. Instead: Fix hygiene first — agenda-or-cancel plus published notes — and re-measure over four occurrences. A series at 30% agenda compliance has not been tried as a well-run meeting yet, and cancelling it moves the same unresolved topics somewhere less visible.

Files

FilePurpose
scripts/meeting_notes_parser.pyExtracts decisions, actions and questions from notes; detects owners and dates; flags gaps; --strict exits 1
scripts/commitment_tracker.pyAgeing, completion and on-time rates, carry-over, per-owner and per-meeting breakdowns, deterministic --as-of
scripts/meeting_series_diagnostic.pyDecision density, churning topics, attendance decay, hygiene compliance, KEEP/RESTRUCTURE/ASYNC/KILL verdict
references/extraction-rules-and-note-conventions.mdCue catalogue, owner/date detection, reflow, source-format handling, deduplication, note conventions, failure modes
references/accountability-and-series-health.mdCommitment lifecycle, benchmarks, per-owner interpretation, decision density, topic churn, action caps, escalation ladder
assets/action_register_template.mdRegister: open actions, third-carry-over gate, decision log, open questions, health snapshot
assets/sample_notes.mdWrapped markdown notes, mixed-quality actions (extracts at 33% completeness)
assets/sample_notes_transcript.txtLabelled transcript (extracts at 100%) — the contrast shows what note conventions buy
assets/sample_commitments.json18-action register across 5 meetings and 7 weeks
assets/sample_series.json10 occurrences of a weekly alignment meeting

© borghei, MIT. 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 10 other files (scripts, references, assets) in project-management/meeting-analyzer of borghei/Claude-Skills.

  • SKILL.md
  • assets/action_register_template.md
  • assets/sample_commitments.json
  • assets/sample_notes.md
  • assets/sample_notes_transcript.txt
  • assets/sample_series.json
  • references/accountability-and-series-health.md
  • references/extraction-rules-and-note-conventions.md
  • scripts/commitment_tracker.py
  • scripts/meeting_notes_parser.py
  • scripts/meeting_series_diagnostic.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

Meeting Analyzer 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.

Meeting Analyzer compared with similar skills
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Meeting Analyzer this skillborghei/Claude-Skills886—~3.3kAutomated safety check: PassMIT
Client Onboardingindranilbanerjee/digital-marketing-pro8591 repos~1.8kAutomated safety check: PassMIT
Zohosundial-org/awesome-openclaw-skills663—~2.7kAutomated safety check: NotesNone
Obsidian Research Logbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~378Automated safety check: PassCustom licence
Twinmind Core Workflow Bjeremylongshore/tons-of-skills-marketplace2.8k—~922Automated safety check: PassMIT
Sop Builderericrisco/rsc-harness174—~3.1kAutomated safety check: PassMIT

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Questions about Meeting Analyzer

What does Meeting Analyzer do?

Turn meeting notes into an accountable register — extract decisions, actions and open questions, flag ownerless items, track follow-through. Meeting Analyzer is an agent skill from borghei/Claude-Skills. Turn meeting notes into an accountable register — extract decisions, actions and open questions, flag ownerless items, track follow-through.

When should I use Meeting Analyzer?

Meeting Analyzer fits situations like: commitments get dropped; A recurring meeting decides nothing.

How do I install Meeting Analyzer in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill meeting-analyzer -a claude-code`. Or copy the skill folder (project-management/meeting-analyzer in borghei/Claude-Skills) into .claude/skills/meeting-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Meeting Analyzer in Codex?

Run `npx skills add borghei/Claude-Skills --skill meeting-analyzer -a codex`. Or copy the skill folder (project-management/meeting-analyzer in borghei/Claude-Skills) into .agents/skills/meeting-analyzer in your project. Codex loads it when a task matches its description.

Can I use Meeting Analyzer 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 borghei/Claude-Skills --skill meeting-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meeting-analyzer, .gemini/skills/meeting-analyzer, .github/skills/meeting-analyzer and .opencode/skills/meeting-analyzer in your project.

What does Meeting Analyzer need to run?

Going by SKILL.md and its folder, Meeting Analyzer needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Meeting Analyzer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Meeting Analyzer 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Meeting Analyzer use?

Meeting Analyzer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Meeting Analyzer use?

About 3.3k 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. Its references folder adds about 6.9k tokens, read only when the agent opens those files.

What are the alternatives to Meeting Analyzer?

Skills that share tags, products or a category with Meeting Analyzer: Client Onboarding (indranilbanerjee/digital-marketing-pro, 859 stars), Zoho (sundial-org/awesome-openclaw-skills, 663 stars), Obsidian Research Log (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars) and Twinmind Core Workflow B (jeremylongshore/tons-of-skills-marketplace, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meeting Analyzer?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 886 GitHub stars. The repository holds 354 skills in this directory. The repository was last updated on October 7, 2026.

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