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Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

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agentCLAWHUBUnverified

Engineering manager 1-on-1 meeting brief generator

Generate a 1-on-1 brief from GitHub activity. Fully deterministic pipeline — 5 tool calls, zero sub-agent spawns. Skill: Engineering manager 1-on-1 meeting brief generator Owner: jacksync Summary: Generate a 1-on-1 brief from GitHub activity. Fully deterministic pipeline — 5 tool calls, zero sub-agent spawns. Tags: latest:1.0.3 Version history: v1.0.3 | 2026-08-24T00:02:35.036Z | user - Improved pipeline: 1-on-1 brief is now generated deterministically in a single agent session with exactly five tool calls (no sub-agents spawned

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.3release · observed Aug 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17797mbhv0wkbd3wj9d1mynr185px38:pullstar-1on1

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-jacksync-pullstar-1on1/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

60,542 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: skills
description: Generate a 1-on-1 brief from GitHub activity. Fully deterministic pipeline — 5 tool calls, zero sub-agent spawns.
license: MIT
---

## Overview

PullStar fetches GitHub activity for one engineer (PRs authored, reviews given), runs a **deterministic local scoring engine** across five dimensions, and prepares an LLM input payload. The **agent** then performs LLM inference and finalizes the brief.


**Quickstart:** 

    run_brief.py --login steipete --pr-insights --days 7
    Read llm_input_steipete.json, do the LLM inference, present the brief



**Data Flow Summary:**

| Step | What runs | External calls |
|------|-----------|----------------|
| Ingest | `run_brief.py` → `ingest.py` | GitHub API |
| Score | `run_brief.py` → `score.py` | None |
| Prepare | `run_brief.py` → `agent_prepare_1on1.py` | None |
| **Agent inference** | **Agent calls LLM** | **LLM provider** |
| Finalize | Agent runs `agent_finalize_1on1.py` | None |

**⚠️ Important:** Steps 1–3 run locally. Only the LLM inference step (step 4) sends data to your AI provider.

---

## Requirements

- Python 3.11+
- Install dependencies: `pip install PyGithub python-dotenv requests`
- A GitHub personal access token (see Security section below)

---

## Security & Privacy

### Token Scope

| Option | Where to create | Best for |
|--------|----------------|----------|
| Classic PAT (`repo` scope) | https://github.com/settings/tokens | Cross-user search, org-wide ingestion |
| Fine-grained PAT | https://github.com/settings/personal-access-tokens | Your own repos only |

> Fine-grained PATs cannot search across arbitrary users. Use a classic PAT for org-wide briefs.

Set `GITHUB_ORG` to narrow search to one organization.

### Token Resolution Order

Secrets are resolved using layered lookup — first match wins:

1. `--github-token` CLI flag (override/debug only — never logged)
2. `GITHUB_TOKEN` environment variable
3. `~/.pullstar/credentials` (key=value format)
4. `.env` in the skill directory

### Data Privacy by Mode

**Default (no `--pr-insights`):**
- Only aggregated statistics and scores sent to LLM
- No raw PR text, comments, or review bodies included

**PR Insights (`--pr-insights`):**
- Bounded raw PR discussion text packaged into the LLM prompt
- Bounded to 5 PRs, 3 reviews/comments each, 600 char limit per item
- Review `llm_input_{login}.json` before inference if you have privacy concerns

---

## Configuration

### `.env`

| Variable | Required | Description |
|----------|----------|-------------|
| `GITHUB_TOKEN` | Recommended | Classic PAT with `repo` scope. Omit for unauthenticated access (60 req/hr). |
| `GITHUB_ORG` | No | Scope ingestion to one org. |

---

## Usage

### Standard run

```bash
python run_brief.py --login jsmith
```

### With PR insights

```bash
python run_brief.py --login jsmith --pr-insights
```

### Common options

```bash
pyt

_meta.json

{
  "ownerId": "kn74sjz5m0z36x7fz1vcsh3xrs85phv2",
  "slug": "pullstar-1on1",
  "version": "1.0.3",
  "publishedAt": 1787529755036
}

skill-card.md

## Description:

Generate a 1-on-1 brief from GitHub activity using a deterministic five-step agent pipeline.

This skill is ready for commercial/non-commercial use.

## Publisher:

[jacksync](https://clawhub.ai/user/jacksync)

### License/Terms of Use:

MIT-0

## Use Case:

Engineering managers and team leads use this skill to turn an engineer's GitHub pull request and review activity into a concise preparation brief for a 1-on-1. It supports data-backed conversation preparation without presenting the result as a performance review.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill handles GitHub tokens and can read repositories visible to the configured token.

Mitigation: Use a fine-grained token where possible, or a narrowly scoped classic token with GITHUB_ORG set to limit ingestion scope.

Risk: PR insights mode may send private pull request discussion text to the configured LLM provider.

Mitigation: Leave PR insights disabled unless raw PR discussions are appropriate for the provider, and review llm_input_<login>.json before inference.

Risk: Local .pullstar artifacts may contain private engineering activity data.

Mitigation: Review and delete generated .pullstar artifacts when they may contain private data.

Risk: Runtime dependencies may change behavior if installed without version pinning.

Mitigation: Use an isolated Python environment with pinned dependencies where possible.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/jacksync/skills/pullstar-1on1)
- [Publisher profile](https://clawhub.ai/user/jacksync)
- [GitHub classic personal access tokens](https://github.com/settings/tokens)
- [GitHub fine-grained personal access tokens](https://github.com/settings/personal-access-tokens)

## Skill Output:

**Output Type(s):** [Markdown, JSON, Shell commands, Guidance]

**Output Format:** [Markdown brief embedded in JSON artifacts, with command guidance for the agent workflow]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Writes local .pullstar artifacts for ingest, scoring, LLM input, LLM output, and final output; PR insights mode may include bounded raw PR discussion excerpts.]

## Skill Version(s):

1.0.3 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

brief_v1.txt

You are an engineering leadership intelligence tool. Your output is read by an engineering manager immediately before a 1-on-1 with a direct report. The manager is technically competent and time-pressed.

Your job: convert GitHub activity scores and signals into a preparation brief the manager can scan in under 2 minutes. Every sentence must be usable — either as context the manager didn't have, a question worth asking, or a pattern worth noticing.

---

WHAT MAKES A BRIEF USEFUL:

1. Specificity. Reference actual numbers: "14 PRs merged" not "active contributor."

2. At least one non-obvious observation. Don't just restate the dimension scores. Look for combinations — a dimension that contradicts another, a signal that's unusual given the other signals, or a pattern that only appears when you look across the full picture. If the data doesn't support a non-obvious insight, skip it rather than inventing one.

3. Calibrated uncertainty. If confidence is low or data volume is small, say so clearly in one sentence and move on. Don't pad the brief to compensate for thin data.

4. No inferred context. You don't know: sprint goals, team norms, on-call rotations, personal circumstances, what the manager already knows, or why any number is what it is. Don't guess.

---

TONE:

- Practical, not corporate. Write like a sharp colleague briefing a peer, not a performance review.
- Neutral, not cheerful. "3 of 12 PRs had descriptions" is better than "shows room for growth in documentation."
- Direct, not hedging. One precise sentence beats three careful ones.
- Highlights must contain only positive or clearly constructive strengths.
- Reserve concerns, asymmetries, and coaching topics for Areas to Explore or Patterns Worth Noting.
- Do not over-index on a single metric unless it is clearly dominant and unusual.
- When identifying a possible concern, acknowledge plausible contextual explanations when appropriate.

---

ANTI-PATTERNS — never do these:

- Open any section with "In this period..." or "Over the past X days..."
- Write generic bullets: "Consistent contributor," "Active team member," "Good reviewer"
- Repeat the same data point across multiple sections
- Turn Areas to Explore into leading questions that imply a specific problem ("Have you considered improving your PR descriptions?")
- Expand the Score Summary beyond a compact reference table
- Tell the engineer what they "should" do, even indirectly
- Invent patterns the data doesn't support

---

OUTPUT — produce exactly these six sections in this order, in markdown. Do not add extra sections.

## Quick Summary
2-3 sentences. Lead with the most concrete number. Answer: what did this engineer ship and how engaged were they? If there is a non-obvious combination in the data worth flagging, include it here.

## Highlights
2-4 bullets. One specific data point per bullet. Draw signals from at least two different dimensions — don't cluster around one.

## Areas to Explore
2-3 open-ended questions. Derive

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