bradygaster/squad is a tool that creates a human-directed team of AI development agents inside a project repository through GitHub Copilot. Developers use it to delegate work among persistent specialists such as frontend, backend, testing, and lead agents while retaining responsibility for decisions and review. The catalogue entries are the skills, agents, and instructions that define and coordinate those team members.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/bradygaster/squad/session-recoverynpx skills add bradygaster/squad --skill session-recoverygit clone --depth 1 https://github.com/bradygaster/squadWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bradygaster/squad/session-recovery)<a href="https://agentmods.dev/skills/bradygaster/squad/session-recovery"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/session-recovery.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.01215 |
| Opus 5 | $0.00008 | $0.00607 |
| Sonnet 5 | $0.00003 | $0.00243 |
| Haiku 4.5 | $0.00002 | $0.00121 |
Grade A, and why
session-recovery scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to session-recovery — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Squad agents run in Copilot CLI sessions that can be interrupted — terminal crashes, network drops, machine restarts, or accidental window closes. When this happens, in-progress work may be left in a partially-completed state: branches with uncommitted changes, issues marked in-progress with no active agent, or checkpoints that were never finalized.
Copilot CLI stores session history in a SQLite database called session_store (read-only, accessed via the sql tool with database: "session_store"). This skill teaches agents how to query that store to detect interrupted sessions and resume work.
Patterns
1. Find Recent Sessions
Query the sessions table filtered by time window. Include the last checkpoint to understand where the session stopped:
SELECT
s.id,
s.summary,
s.cwd,
s.branch,
s.updated_at,
(SELECT title FROM checkpoints
WHERE session_id = s.id
ORDER BY checkpoint_number DESC LIMIT 1) AS last_checkpoint
FROM sessions s
WHERE s.updated_at >= datetime('now', '-24 hours')
ORDER BY s.updated_at DESC;
2. Filter Out Automated Sessions
Automated agents (monitors, keep-alive, heartbeat) create high-volume sessions that obscure human-initiated work. Exclude them:
SELECT s.id, s.summary, s.cwd, s.updated_at,
(SELECT title FROM checkpoints
WHERE session_id = s.id
ORDER BY checkpoint_number DESC LIMIT 1) AS last_checkpoint
FROM sessions s
WHERE s.updated_at >= datetime('now', '-24 hours')
AND s.id NOT IN (
SELECT DISTINCT t.session_id FROM turns t
WHERE t.turn_index = 0
AND (LOWER(t.user_message) LIKE '%keep-alive%'
OR LOWER(t.user_message) LIKE '%heartbeat%')
)
ORDER BY s.updated_at DESC;
3. Search by Topic (FTS5)
Use the search_index FTS5 table for keyword search. Expand queries with synonyms since this is keyword-based, not semantic:
SELECT DISTINCT s.id, s.summary, s.cwd, s.updated_at
FROM search_index si
JOIN sessions s ON si.session_id = s.id
WHERE search_index MATCH 'auth OR login OR token OR JWT'
AND s.updated_at >= datetime('now', '-48 hours')
ORDER BY s.updated_at DESC
LIMIT 10;
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 156 lines · 16 tokens per session scan A 54a7c1feba61
session-recovery is a skill published in the GitHub repository bradygaster/squad (3,159 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 1,215 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to session-recovery, differing in 0 lines, and is treated as a copy.
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