How do I install the Skill Cockpit skill?

Run npx skillmds@latest add getedgehq/skill-cockpit in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Skill Cockpit safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Skill Cockpit?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Skill Cockpit free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Skill Cockpit?

getedgehq (@getedgehq) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Skill Cockpit by getedgehq — Agent Skill

Name: Skill Cockpit
Rating: 4.5 (4 reviews)
Author: getedgehq

Skill Cockpit

Open a local browser cockpit for the agent self-improvement loop - see every installed skill, the new candidate skills, and the blind eval results and adopt/probation/reject decisions for all of them, and start mining, dry runs, full loops, evals and probation rechecks from the page. Use when asked to "show my skills", "open the cockpit", "what did the loop adopt", "show eval results", "manage my skills", or to watch skill-miner / skill-eval-loop runs. Local only, stdlib Python, no cloud.

getedgehq Updated Sep 22, 2026

File contents

skill-cockpit

The UX layer over skill-miner and skill-eval-loop. One local page, three questions: what skills do I have, which new ones are being considered, and did they prove themselves on my own tasks.

Start

lsof -iTCP:7788 -sTCP:LISTEN                 # check the port first
python3 scripts/cockpit.py                    # http://127.0.0.1:7788/
python3 scripts/cockpit.py --port 7790 --forge-root ~/skill-forge \
  --projects ~/.claude/projects --skills-dir ~/.claude/skills --skills-dir ~/.agents/skills \
  --memory ~/.claude/projects/-Users-me/memory --adopt-dir ~/skill-forge/adopted
python3 scripts/cockpit.py --dump             # state JSON, no server (for agents/tests)

On a remote box, tunnel it: ssh -L 7788:127.0.0.1:7788 host, then open locally.

What the page shows

Evals - every run in $FORGE_ROOT/runs: winner (skill / baseline / tie / invalid) with the sample tally (3–0 of 3), verify passes per arm (3/3) and tool errors, gate decision. Sampled runs (runs/<id>/sN/) show each sample's winner, timings and runner error; a run still being written by forge.sh or a timer reads as running, an abandoned one as incomplete. Expand for the exact task prompt, the blind judge's reasons, per-criterion winners, the production failure it targets, and the probation recheck date.
Candidates - drafted skills (drafts/) with their forge status, and scored matches from local, getedge, Floom and skills.sh with P(improvement). Any draft can be evaluated on an unused brief from the page (forge.sh --brief --skill-dir --samples N, default 3, optional probation on a tie).
Fleet - installed skills (symlinks de-duplicated) with their latest ledger decision, filterable.
Failures - correction themes from mined/corrections.json (knowledge gap vs generic, with the user's own rule), then mined error clusters (task vs infra), frustration quotes, and the heaviest sessions by output tokens. Task clusters can be looped directly. "Mine corrections" runs corrections.py (one model call; --memory maps themes to the user's rule files).
Jobs - live log tail of anything the cockpit started.

Rules

Binds 127.0.0.1 only, rejects foreign Host headers, and every action needs the per-process token embedded in the page (no CSRF against localhost).
Actions are a fixed whitelist that call the sibling skills' scripts. One job at a time, so eval spend stays visible. Evals never overwrite an existing run.
The cockpit never adopts or deletes skills by itself - gate.py and recheck.py own those decisions, and the ledger records them.
Scripts are found as siblings (../../skill-miner/scripts), then in ~/.agents/skills / ~/.claude/skills; override with MINER_SCRIPTS / EVAL_SCRIPTS.

---name: skill-cockpitdescription: Open a local browser cockpit for the agent self-improvement loop - see every installed skill, the new candidate skills, and the blind eval results and adopt/probation/reject decisions for all of them, and start mining, dry runs, full loops, evals and probation rechecks from the page. Use when asked to "show my skills", "open the cockpit", "what did the loop adopt", "show eval results", "manage my skills", or to watch skill-miner / skill-eval-loop runs. Local only, stdlib Python, no cloud.---# skill-cockpitThe UX layer over skill-miner and skill-eval-loop. One local page, three questions:what skills do I have, which new ones are being considered, and did they provethemselves on my own tasks.## Start```bashlsof -iTCP:7788 -sTCP:LISTEN # check the port firstpython3 scripts/cockpit.py # http://127.0.0.1:7788/python3 scripts/cockpit.py --port 7790 --forge-root ~/skill-forge \ --projects ~/.claude/projects --skills-dir ~/.claude/skills --skills-dir ~/.agents/skills \ --memory ~/.claude/projects/-Users-me/memory --adopt-dir ~/skill-forge/adoptedpython3 scripts/cockpit.py --dump # state JSON, no server (for agents/tests)```On a remote box, tunnel it: `ssh -L 7788:127.0.0.1:7788 host`, then open locally.## What the page shows- **Evals** - every run in `$FORGE_ROOT/runs`: winner (skill / baseline / tie / invalid) with the sample tally (`3–0 of 3`), verify passes per arm (`3/3`) and tool errors, gate decision. Sampled runs (`runs/<id>/sN/`) show each sample's winner, timings and runner error; a run still being written by forge.sh or a timer reads as running, an abandoned one as incomplete. Expand for the exact task prompt, the blind judge's reasons, per-criterion winners, the production failure it targets, and the probation recheck date.- **Candidates** - drafted skills (`drafts/`) with their forge status, and scored matches from local, getedge, Floom and skills.sh with P(improvement). Any draft can be evaluated on an unused brief from the page (`forge.sh --brief --skill-dir --samples N`, default 3, optional probation on a tie).- **Fleet** - installed skills (symlinks de-duplicated) with their latest ledger decision, filterable.- **Failures** - correction themes from `mined/corrections.json` (knowledge gap vs generic, with the user's own rule), then mined error clusters (task vs infra), frustration quotes, and the heaviest sessions by output tokens. Task clusters can be looped directly. "Mine corrections" runs `corrections.py` (one model call; `--memory` maps themes to the user's rule files).- **Jobs** - live log tail of anything the cockpit started.## Rules- Binds 127.0.0.1 only, rejects foreign Host headers, and every action needs the per-process token embedded in the page (no CSRF against localhost).- Actions are a fixed whitelist that call the sibling skills' scripts. One job at a time, so eval spend stays visible. Evals never overwrite an existing run.- The cockpit never adopts or deletes skills by itself - `gate.py` and `recheck.py` own those decisions, and the ledger records them.- Scripts are found as siblings (`../../skill-miner/scripts`), then in `~/.agents/skills` / `~/.claude/skills`; override with `MINER_SCRIPTS` / `EVAL_SCRIPTS`.

Frequently asked questions