evolve

分类: 商业与运营 | 上传者: keshav55 | 下载: 0 | 版本: v1.0（最新）
Self-improvement loop for agent-crm. Analyzes CRM data to find what's working, proposes one experiment, and tracks results via the facts table. Use when the user says evolve, improve, self-improve, optimize, what's working, experiment, or strategy.
更新日志: Source: GitHub https://github.com/keshav55/agent-crm
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📄 SKILL.md 10.1 KB
SKILL.md

---
name: evolve
description: Self-improvement loop for agent-crm. Analyzes CRM data to find what's working, proposes one experiment, and tracks results via the facts table. Use when the user says evolve, improve, self-improve, optimize, what's working, experiment, or strategy.
version: 1.0.0
tags:
  - crm
  - self-improvement
  - experiments
  - strategy
  - analytics
---

# Evolve

One improvement cycle per run. Read the CRM, find what's working, propose one change, measure it.

Inspired by Karpathy's autoresearch: instead of optimizing code, optimize your GTM by running experiments against real CRM data.

## When to activate

- "evolve" / "improve" / "self-improve"
- "optimize" / "what's working" / "experiment"
- "strategy" (in the context of outreach or GTM)
- "what should I try next?"
- "why aren't deals closing?"

## Prerequisites

The CRM database must exist. Default location: `crm.db` in the project root, or set via `CRM_DB` env var.

```python
from crm import CRM
crm = CRM("crm.db")
```

The `evolve.py` script and `strategy.md` file in the agent-crm project root provide additional context. Run `python evolve.py init` if they don't exist yet.

---

## Workflow: One Improvement Cycle

### Step 1: Read the CRM state

Pull the three data sources that tell you what's happening.

```python
from crm import CRM
crm = CRM("crm.db")

# Pipeline overview
pipeline = crm.pipeline()
# Returns: [{"status": "lead", "count": 12, "names": "Alice, Bob, ..."}]

# Full stats
stats = crm.stats()
# Returns: {"total_contacts": N, "contacted_last_7d": N, "stale_14d": N, "by_status": [...]}

# All activity (look at last 50 events across the CRM)
contacts = crm.list_contacts()

# Stale contacts — relationships that are fading
stale = crm.stale_contacts(days=14)

# Facts graph — experiment history and observed patterns
past_experiments = crm.search_graph("experiment")
past_learnings = crm.search_graph("learning")
```

### Step 2: Analyze what's working and what's not

Run these four analyses. Each one answers a specific question.

**Which outreach gets replies?**
```python
# Look at contacts who moved from 'lead' or 'contacted' to 'interested' or beyond
for c in contacts:
    if c["status"] in ("interested", "negotiating", "active_customer"):
        acts = crm.get_activity(c["email"] or c["name"], limit=20)
        timeline = crm.timeline(c["email"] or c["name"])
        # What type of first touch? How long between touches?
        # What channel? What did the outreach say?
```

**Which deals are stuck?**
```python
# Contacts in negotiating or interested that haven't moved
for c in contacts:
    if c["status"] in ("interested", "negotiating"):
        velocity = crm.velocity(c["email"] or c["name"])
        # velocity["trend"] tells you: "accelerating", "stable", "decaying", "cold"
        if velocity and velocity["trend"] in ("decaying", "cold"):
            # This deal is stuck. Note why.
            pass
```

**Which relationships are fading?**
```python
# stale_contacts already handles this
stale = crm.stale_contacts(days=14)
# Sort by deal_size to prioritize high-value fading relationships
stale_sorted = sorted(stale, key=lambda c: c.get("deal_size") or 0, reverse=True)
```

**What patterns correlate with closed deals?**
```python
customers = crm.list_contacts(status="active_customer")
for c in customers:
    acts = crm.get_activity(c["email"] or c["name"], limit=20)
    journey = " -> ".join(f"{a['type']}" for a in reversed(acts))
    # Track: source, number of touches, time to close, channel mix
```

### Step 3: Propose ONE specific change

Based on the analysis, propose exactly one change. Not two. Not a redesign. One variable.

The change must be one of:
- **Targeting**: Who you reach out to (industry, company size, role, signal)
- **Messaging**: What you say (subject line, opening line, CTA, proof point)
- **Timing**: When you send (day of week, time of day, cadence)
- **Channel**: How you reach them (email, Twitter DM, LinkedIn, warm intro)

Format the proposal clearly:

```
EXPERIMENT: [short name]
CHANGE: [exactly what's different from current strategy]
HYPOTHESIS: [what you expect to happen and why]
MEASURE: [how you'll know if it worked — specific metric + threshold]
BATCH SIZE: [how many contacts to test on — usually 5-10]
```

### Step 4: Record the experiment in the facts table

Use the facts table to track experiments across sessions. This is the persistent memory.

```python
import datetime
batch_id = f"experiment:{datetime.date.today().isoformat()}_{short_name}"

# Record the hypothesis
crm.observe(batch_id, "hypothesis", "Tuesday emails get 2x replies vs Thursday", source="evolve")
crm.observe(batch_id, "change_type", "timing", source="evolve")
crm.observe(batch_id, "batch_size", "8", source="evolve")
crm.observe(batch_id, "status", "active", source="evolve")
crm.observe(batch_id, "started_at", datetime.date.today().isoformat(), source="evolve")

# Tag each contact in the batch
for contact_email in batch_contacts:
    crm.observe(batch_id, "contact", contact_email, source="evolve")
```

Also record in `evolve.py`'s results file if it exists:
```bash
python evolve.py record --batch N --sent 8 --replies 0 --meetings 0 --notes "timing experiment: Tuesday send"
```

### Step 5: Record outcomes (when results come in)

After enough time has passed (typically 3-7 days for email), record what happened:

```python
# Update the experiment with results
crm.observe(batch_id, "result", "3/8 replied (37.5%), baseline was 12.5%", source="evolve")
crm.observe(batch_id, "status", "completed", source="evolve")
crm.observe(batch_id, "conclusion", "keep — Tuesday timing works", source="evolve")

# If the experiment won, record a learning
crm.observe("learning:timing", "insight", "Tuesday 9-11am gets 3x reply rate vs Thursday", source="evolve")
crm.observe("learning:timing", "confidence", "1 batch, needs replication", source="evolve")
crm.observe("learning:timing", "discovered_at", datetime.date.today().isoformat(), source="evolve")
```

If the experiment lost:
```python
crm.observe(batch_id, "conclusion", "revert — no improvement", source="evolve")
crm.observe("learning:timing", "insight", "Thursday is not worse than Tuesday — keep default", source="evolve")
```

### Step 6: Update strategy.md with learnings

If an experiment produced a clear win, update `strategy.md` in the agent-crm project root. Move proven learnings from "Open Experiments" to "What Converts (proven)" or "What Fails (proven)".

---

## Reviewing experiment history

To see all past experiments and learnings stored in the facts table:

```python
# All experiments
experiments = crm.search_graph("experiment:")
for e in experiments:
    print(f"{e['entity']} | {e['key']}: {e['value']}")

# All learnings
learnings = crm.search_graph("learning:")
for l in learnings:
    print(f"{l['entity']} | {l['key']}: {l['value']}")

# Specific experiment details
details = crm.search_graph("experiment:2026-03-17_tuesday_timing")
```

Or via the CLI:
```bash
python evolve.py history
python evolve.py status
```

---

## Connecting to Atris for cross-session memory

For learnings that should persist beyond the local CRM (across projects, across machines), push them to Atris.

### Get token

```bash
# From ~/.atris/credentials.json
TOKEN=$(python3 -c "import json,os; print(json.load(open(os.path.expanduser('~/.atris/credentials.json')))['token'])")
```

### Push a learning to Atris

```bash
curl -s -X POST "https://api.atris.ai/api/memory" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "CRM evolve: Tuesday 9-11am emails get 3x reply rate vs Thursday baseline. Tested on 8 contacts, 3 replied.",
    "tags": ["crm", "evolve", "timing", "experiment-result"],
    "source": "agent-crm/evolve"
  }'
```

### Retrieve past learnings from Atris

```bash
# Search for all CRM evolve learnings
curl -s "https://api.atris.ai/api/memory/search?q=crm+evolve&limit=20" \
  -H "Authorization: Bearer $TOKEN"
```

### When to use Atris vs local facts table

- **Local facts table**: Experiment tracking, batch details, contact-level data. This is the working memory.
- **Atris**: Proven learnings, cross-project patterns, insights you want available in every session. This is the long-term memory.

Rule of thumb: push to Atris only after an experiment has a clear conclusion. Don't push hypotheses or in-progress experiments.

---

## Example: Full cycle

User says: "/evolve"

1. Read CRM: 47 contacts, 12 leads, 8 contacted, 3 interested, 2 negotiating. 6 stale.
2. Analyze:
   - 2 of 3 interested contacts came from warm intros (already known)
   - Cold email reply rate: 2/16 (12.5%) — all sent Thursday afternoon
   - The 2 replies were both to emails that mentioned a specific pain point from their LinkedIn
   - 2 deals in negotiating haven't had activity in 11 days
3. Propose:
   ```
   EXPERIMENT: personalized_pain_point
   CHANGE: Every cold email must reference one specific pain point found on their LinkedIn or website (not generic industry pain)
   HYPOTHESIS: Specific pain references drove both replies so far. Doubling down should beat 12.5% baseline.
   MEASURE: Reply rate > 25% on next 8 cold emails
   BATCH SIZE: 8
   ```
4. Record: `crm.observe("experiment:2026-03-17_personalized_pain", "hypothesis", "...", source="evolve")`
5. Execute the batch, wait 5 days
6. Record: 3/8 replied (37.5%). Push learning to Atris.
7. Update strategy.md: move "specific pain point reference" from experiment to proven.

---

## Troubleshooting

### No experiment history found
The facts table may be empty. Run `crm.search_graph("experiment")` to check. If empty, this is the first cycle — skip the "review past experiments" substep and focus on establishing a baseline.

### Not enough data to analyze
If the CRM has fewer than 10 contacts or fewer than 5 activity entries, the analysis step won't yield meaningful patterns. In this case, propose a baseline measurement experiment: send 10 outreach messages using the current strategy and record reply rates.

### Experiment results are ambiguous
If the difference between experiment and baseline is less than 2x, the batch was probably too small. Record as "inconclusive" and either increase batch size or try a different variable. Don't declare a winner on thin data.
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