Lark Sheets
Pinvou/pinvou-agent
【何时用:仅当用户明确指向飞书/Lark 电子表格;泛指做表格默认走本地工具】创建/操作飞书电子表格:工作表与行列结构、读写单元格(值/公式/样式/批注/图片)、查找替换、批量更新、图表/透视表/条件格式/筛选/迷你图/浮动图片。按名称搜索云空间表格文件改用 lark-drive 的 drive +search。doubao.com 的 /sheets/ URL 也走本 skill。不适用本地…
Классифицирует пользовательский фидбек (Excel/CSV/текст) по 6 категориям, делает sentiment-анализ, кластеризацию тем, анализ трендов, триангуляцию по источникам, расчёт NPS и извлечение персон.
Install Pm Feedback
$ npx skills add serejaris/personal-corp-os --skill pm-feedback -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install serejaris/personal-corp-os pm-feedback --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pm-feedback .claude/skills/pm-feedback && rm -rf skills-srcUse ~/.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/
Install the "pm-feedback" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-feedback into .claude/skills/pm-feedback/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-feedback", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-feedbackType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add serejaris/personal-corp-os --skill pm-feedback -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install serejaris/personal-corp-os pm-feedback --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pm-feedback .agents/skills/pm-feedback && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pm-feedback" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-feedback into .agents/skills/pm-feedback/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-feedback", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add serejaris/personal-corp-os --skill pm-feedback -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install serejaris/personal-corp-os pm-feedback --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pm-feedback .cursor/skills/pm-feedback && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pm-feedback" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-feedback into .cursor/skills/pm-feedback/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-feedback", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/serejaris/personal-corp-os.git --path skills/pm-feedback--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add serejaris/personal-corp-os --skill pm-feedback -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install serejaris/personal-corp-os pm-feedback --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pm-feedback .gemini/skills/pm-feedback && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pm-feedback" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-feedback into .gemini/skills/pm-feedback/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-feedback", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install serejaris/personal-corp-os pm-feedbackInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add serejaris/personal-corp-os --skill pm-feedback -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pm-feedback .github/skills/pm-feedback && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pm-feedback" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-feedback into .github/skills/pm-feedback/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-feedback", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add serejaris/personal-corp-os --skill pm-feedback -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install serejaris/personal-corp-os pm-feedback --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pm-feedback .opencode/skills/pm-feedback && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pm-feedback" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-feedback into .opencode/skills/pm-feedback/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-feedback", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
pm-feedback- GitHub stars
- 229
- Token cost
- ~2.6k tokens
- SKILL.md length
- 971 words
- Files
- 4 (incl. assets)
- Skills in repo
- 36
- Repo updated
- First seen
- Licence
- MIT
At a glance
Классифицирует пользовательский фидбек (Excel/CSV/текст) по 6 категориям, делает sentiment-анализ, кластеризацию тем, анализ трендов, триангуляцию по источникам, расчёт NPS и извлечение персон.
- Works in 10 steps: Pre-process data → Classification → Sentiment analysis → …
- Анализ обратной связи
- SKILL.md covers Inputs, Step 1 — Pre-process data, Step 2 — Classification and Step 3 — Sentiment analysis, plus 11 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Pm Feedback is an agent skill from serejaris/personal-corp-os. Классифицирует пользовательский фидбек (Excel/CSV/текст) по 6 категориям, делает sentiment-анализ, кластеризацию тем, анализ трендов, триангуляцию по источникам, расчёт NPS и извлечение персон. На выходе — Top-10 болей с рекомендациями к действию. User-invoked only — do NOT auto-trigger. Triggers on /pm-feedback, "анализ обратной связи", "разбор отзывов", "анализ NPS", "analyze user feedback", "VOC analysis", "NPS analysis", "review analysis".
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including assets (for example `README.md` and `README.ru.md`).
It sits in Sales & Support, covering Customer feedback analysis and Excel spreadsheets. It works with Microsoft Excel. The repository describes itself as: Personal Corp OS — управление личной компанией через AI-агентов: задачи вне головы, отделы вместо памяти, недельное ретро. Открытые скиллы для Claude Code и Codex. The licence is MIT.
When your agent uses it
- Анализ обратной связи
- Analyze user feedback
- Review analysis
Example prompts
- “анализ NPS”
- “analyze user feedback”
- “VOC analysis”
- “/pm-feedback”
Workflow steps
10 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 95e36c3. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
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
Pm Feedback loads about 2.6k tokens when it runs. Until then it costs about 115 tokens; SKILL.md has 971 words of instructions outside code blocks.
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 serejaris/personal-corp-os at commit 95e36c3, republished under its MIT licence (© serejaris). 971 words, ~2,551 tokens.
Download SKILL.mdSave it as
.claude/skills/pm-feedback/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- pm-feedback
- description
- Классифицирует пользовательский фидбек (Excel/CSV/текст) по 6 категориям, делает sentiment-анализ, кластеризацию тем, анализ трендов, триангуляцию по источникам, расчёт NPS и извлечение персон. На выходе — Top-10 болей с рекомендациями к действию. User-invoked only — do NOT auto-trigger. Triggers on /pm-feedback, "анализ обратной связи", "разбор отзывов", "анализ NPS", "analyze user feedback", "VOC analysis", "NPS analysis", "review analysis".
pm-feedback — User feedback analysis
Part of the Personal Corp framework — running a one-person business through AI agents. Structure raw feedback into a decision-driving insight report. Built-in classification, sentiment, theme clustering, NPS, trend analysis, source triangulation, and persona extraction.
Inputs
| Field | Required | Notes |
|---|---|---|
| Feedback data | yes | Excel / CSV / pasted text / review screenshots |
| Purpose | no | Product improvement / satisfaction / topic-specific (e.g. post-launch reaction); default product improvement |
| Time range | no | For freshness tagging and trend analysis |
| Source channels | no | Multiple channels enable triangulation |
Mode: ≤ 20 items → close-read mode (item-by-item with detailed reading); > 20 → statistical mode (auto-classify + aggregated report).
Step 1 — Pre-process data
- Drop exact duplicates
- Merge near-duplicates (similarity > 90%), record merge count
- Ultra-short items (< 5 chars, no substance like "good"/"bad") → counted separately, not in deep analysis
- If a rating column exists (1-10 or 1-5 stars) → extract for NPS
- Identify source channel (in-app feedback, app store, support ticket, social media, etc.)
Step 2 — Classification
Six-category taxonomy:
| Category | Criterion | Example |
|---|---|---|
| Feature request | User wants something not yet built | "I'd like batch export" |
| Bug report | Existing feature behaves incorrectly | "Save button loses my data" |
| Usage question | User can't find or doesn't know how | "How do I change my password?" |
| UX complaint | Feature exists but experience is poor | "Loading is too slow" / "UI too cluttered" |
| Positive review | Satisfaction, praise, recommendation | "Love this feature!" |
| Other | Unclassifiable or off-topic | Spam, ads, noise |
When ambiguous (one item spans multiple), tag primary + secondary.
Step 3 — Sentiment analysis
| Sentiment | Signals | Calibration |
|---|---|---|
| Positive | Likes, praise, recommends, thanks | Pure factual praise ("works") = neutral, not positive |
| Neutral | Statement of fact, question, calm suggestion | Feature requests = neutral by default unless angry |
| Negative | Complaint, anger, disappointment, threats | "I wish you supported X" = neutral; "Why don't you support X yet?" = negative |
Negative-intensity grading:
- Mild: calm dissatisfaction ("not very convenient")
- Medium: explicit disappointment ("very disappointed", "bad experience")
- Severe: threats ("I'll uninstall if not fixed", "I'll file a complaint") → high-priority handling
Step 4 — Theme clustering
Apply two methods to extract core themes.
Method A — Affinity mapping:
- Split observations: decompose each feedback item into independent observation cards
- Natural cluster: group by similarity without preset labels — let themes emerge
- Name themes: label each cluster ("payment flow friction", "search results irrelevant")
- Identify hierarchy: group small clusters under larger themes (e.g. "payment friction" + "long refund cycle" → "transaction experience")
- Flag outliers: items that fit no cluster — possible early signals
Method B — Thematic coding:
- Open coding: tag each item with descriptive labels ("slow load", "crash", "hidden entry point")
- Axial coding: group descriptive labels into abstract themes ("slow load" + "crash" → "performance issues")
- Selective coding: identify core themes and their relationships
- Quantify frequency: count mentions and share per theme
Cluster output:
| Theme | Sub-theme | Mentions | Share | Representative quote |
|---|---|---|---|---|
| {theme 1} | {sub-a} | {N} | {X%} | "verbatim quote" |
Step 5 — NPS analysis (if rating data exists)
- NPS = % Promoters (9-10) − % Detractors (0-6)
- Industry benchmarks: SaaS avg 30-40, consumer apps avg 20-30
- 5-star → 10-pt mapping: 5★=10, 4★=8, 3★=6, 2★=4, 1★=2
Step 6 — Trend analysis (if time data exists)
MoM (or WoW) change calculation:
- Aggregate by week or month per category
- Growth rate = (current − previous) / previous × 100%
- Watch for > 30% changes — flag as "needs attention"
Inflection-point detection:
- 3+ consecutive periods in one direction → established trend
- Sudden direction reversal → trigger investigation
- Correlate with external events: releases, campaigns, competitor moves
Trend output:
- Time-series description per category
- Mark significant changes + likely cause
- Early-warning: which metrics are deteriorating, which improving
Show full SKILL.md (412 more words)Show less
Step 7 — Triangulation
When data spans multiple channels, cross-validate to lift confidence.
Method triangulation: same problem confirmed by different methods
- e.g. theme cluster says "slow load = top pain" → check if NPS detractors' open-ended answers also concentrate on performance
Source triangulation: same finding across channels
- App-store complaints + support tickets + community chatter all cite "crash" → high confidence
- Single-channel finding → tag "single-source, needs validation"
Time triangulation: persistence of the same problem
3 weeks consistent → systemic
- One-off → likely transient or already fixed
Confidence tiers:
| Tier | Conditions | Tag |
|---|---|---|
| High | Multi-source + multi-method + persistent | Decision-ready |
| Medium | 2 of the 3 dimensions support | Recommend more data before deciding |
| Low | Single source or single method | Reference only, validate further |
Step 8 — Persona extraction
Identify typical user types from the feedback corpus.
Method:
- Behavior cluster: infer user types (newbie / veteran / power user / occasional)
- Need cluster: which users care about efficiency, which about experience, which about price
- Sentiment cluster: loyal advocates / silent users / vocal complainers / churn-edge
Persona template:
[Persona name]: {one-sentence description}
- Typical traits: {usage frequency, focus, behavior pattern}
- Core need: {primary concern}
- Main pain: {recurring problem}
- Feedback style: {how they express}
- Estimated share: {% of feedback corpus}
- Quote: "{verbatim}"Cap at 3-5 personas — more loses actionability.
Step 9 — Pain-point ranking
Pain priority = Frequency × Severity × User weight × Confidence
| Dimension | Scoring |
|---|---|
| Frequency | High (> 10) = 3, Medium (3-10) = 2, Low (< 3) = 1 |
| Severity | Critical (feature broken) = 3, Severe (blocks core flow) = 2, Mild (annoying but usable) = 1 |
| User weight | Paying = 1.5, Free = 1.0 (or 1.0 if no segmentation data) |
| Confidence | High (triangulated) = 1.2, Medium = 1.0, Low (single source) = 0.8 |
Sort descending; output Top 10.
Step 10 — Generate report
# User Feedback Analysis Report
**Period:** {date range}
**Total feedback:** {N} (after dedup: {M})
**Sources:** {channel list}
## 1. Classification
| Category | Count | Share | MoM change (if available) |
|---|---|---|---|
## 2. Sentiment
**Positive:** {X}% | **Neutral:** {Y}% | **Negative:** {Z}%
(Negative breakdown: mild {a} / medium {b} / severe {c})
## 3. Themes
| Theme | Sub-theme | Mentions | Share | Confidence |
|---|---|---|---|---|
## 4. NPS (if rating data)
**Score:** {n} (Promoters {X}% − Detractors {Y}%)
**Benchmark:** {above/below} industry by {Δ}
## 5. Trends (if time data)
- Significant rises: {category}, +{X}% MoM
- Significant drops: {category}, −{X}% MoM
- Inflection events: {description}
## 6. Top 10 Pain Points
| Rank | Pain | Freq | Severity | Confidence | Score | Quote | Recommendation |
|---|---|---|---|---|---|---|---|
## 7. Personas
<!-- 3-5 personas -->
## 8. Key Insights
<!-- Each insight: finding + data + confidence + meaning -->
1. {insight 1}
2. {insight 2}
3. {insight 3}
## 9. Improvement Recommendations
| Priority | Recommendation | Linked pain | Expected impact | Validation method |
|---|---|---|---|---|
## 10. Statistical Notes
- Classification confidence: {high/medium} (sample {N})
- Ambiguous classifications: {count}
- Triangulation coverage: {X%} of findings multi-source verified
- Validity: {sufficient sample / limited sample, results reference-only}Quality bar
- Classifications grounded; ambiguous items tag confidence
- Insights backed by numbers; every insight cites a count
- Recommendations actionable to feature level
- Sample < 50 → tag "limited sample, results reference-only"
- Stats computed via code for accuracy
- Sentiment runs through calibration rules
- Theme clusters MECE (mutually exclusive, collectively exhaustive)
- Triangulation tier explicit per finding
Red lines
- No over-extrapolation — 3 of 20 items mention X ≠ "many users say X"
- Preserve verbatim — every pain point includes a representative quote for traceability
- No fabricated trends — no MoM analysis without history
- No invented personas — personas grounded in cluster results, not imagined
When input is incomplete
- < 10 items → close-read each; skip statistics (sample too small)
- No source/time info → analyze, but tag "missing source/time, recommend supplementing"; skip trend + triangulation
- Mixed languages → group by language, analyze separately
- Single-source → analyze, but tag "single source, recommend cross-channel validation"
Related skills
/pm-prioritize— feature requests from feedback → RICE-rank/pm-prd— high-frequency requests → PRDs/pm-competitive— competitor mentions in feedback → enrich competitor study/pm-metrics— cross-validate feedback trends with product metrics
© serejaris, 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 3 other files (assets) in skills/pm-feedback of serejaris/personal-corp-os.
- SKILL.md
- README.md
- README.ru.md
- assets/illustration.png
Open the folder on GitHubat commit 95e36c3
Compare with similar skills
Pm Feedback 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pm Feedback this skillserejaris/personal-corp-os | 229 | — | ~2.6k | Automated safety check: Pass | MIT | |
| Lark SheetsPinvou/pinvou-agent | 2.4k | — | ~6.5k | Automated safety check: Pass | MIT | |
| Clean Dataexplorium-ai/gtm-skills | 175 | — | ~2k | Automated safety check: Pass | MIT | |
| Web Searchtmustier/pi-for-excel | 434 | — | ~410 | Automated safety check: Pass | MIT | |
| Google Maps Reviews Scrapergmapsscraper/google-maps-agent-skills | 132 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Mx Finance Datahiboys/ExploreFinance | 364 | — | ~518 | Automated safety check: Pass | None |
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Questions about Pm Feedback
What does Pm Feedback do?
Классифицирует пользовательский фидбек (Excel/CSV/текст) по 6 категориям, делает sentiment-анализ, кластеризацию тем, анализ трендов, триангуляцию по источникам, расчёт NPS и извлечение персон. Pm Feedback is an agent skill from serejaris/personal-corp-os. Классифицирует пользовательский фидбек (Excel/CSV/текст) по 6 категориям, делает sentiment-анализ, кластеризацию тем, анализ трендов, триангуляцию по источникам, расчёт NPS и извлечение персон.
When should I use Pm Feedback?
Pm Feedback fits situations like: Анализ обратной связи; analyze user feedback; review analysis.
How do I install Pm Feedback in Claude Code?
Run `npx skills add serejaris/personal-corp-os --skill pm-feedback -a claude-code`. Or copy the skill folder (skills/pm-feedback in serejaris/personal-corp-os) into .claude/skills/pm-feedback in your project. Claude Code loads it when a task matches its description.
How do I install Pm Feedback in Codex?
Run `npx skills add serejaris/personal-corp-os --skill pm-feedback -a codex`. Or copy the skill folder (skills/pm-feedback in serejaris/personal-corp-os) into .agents/skills/pm-feedback in your project. Codex loads it when a task matches its description.
Can I use Pm Feedback 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 serejaris/personal-corp-os --skill pm-feedback -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pm-feedback, .gemini/skills/pm-feedback, .github/skills/pm-feedback and .opencode/skills/pm-feedback in your project.
What does Pm Feedback need to run?
SKILL.md names no scripts, command-line tools or credentials: Pm Feedback is instructions for the agent only.
Does Pm Feedback 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 Pm Feedback 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 Pm Feedback use?
Pm Feedback is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Pm Feedback use?
About 2.6k tokens (SKILL.md is roughly 10k 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 Pm Feedback?
Skills that share tags, products or a category with Pm Feedback: Lark Sheets (Pinvou/pinvou-agent, 2.4k stars), Clean Data (explorium-ai/gtm-skills, 175 stars), Web Search (tmustier/pi-for-excel, 434 stars) and Google Maps Reviews Scraper (gmapsscraper/google-maps-agent-skills, 132 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pm Feedback?
serejaris (a GitHub user) maintains it in serejaris/personal-corp-os, which has 229 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on October 7, 2026.
Source: serejaris/personal-corp-os on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.