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atlas-optimize
The efficiency counterpart to `draco-analyze`. That skill asks *did the research score well*; this one asks *where did the time and money go, and which function should change*.
Overview
The efficiency counterpart to `draco-analyze`. That skill asks *did the research score well*; this one asks *where did the time and money go, and which function should change*.
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atlas-optimize
The efficiency counterpart to draco-analyze. That skill asks did the research
score well; this one asks where did the time and money go, and which function
should change.
It reads the trace --trace captures for a run: per-call / per-agent / per-phase
timing spans (with a compute-vs-gate-wait split), an automatic bottleneck
digest (critical path, phase + per-agent rollups, concurrency peaks, ranked
anomalies), and the byte-exact model transcript — every step's system prompt,
input thread, thinking, and tool calls. Crucially the digest carries an
attribution map (site → src/file.ts:fn), so every hot span points straight at
the code that owns it. That map is the whole reason this is a skill and not an
SDK feature: a finding maps to a file you can open and read.
Producing a trace
A run only emits a trace when started with --trace. Traces land under
eval-runs/traces/<commit>/ (git-ignored), one pair of files per run:
<runId>.digest.json (small) and <runId>.trace.json (spans + transcript).
tsx examples/cli.ts "<question>" --trace full --effort balanced
--trace full= spans + verbatim transcript.--trace spans= the lighter tier (timing + digest, no verbatim I/O) — enough for bottleneck work, cheaper to keep.- If the user names a question to optimize and no trace exists, offer to run this and proceed only on confirmation — it spends provider + Steel credits.
- If they say "analyze this commit's traces", read what's already on disk; don't re-run.
Reading traces — one keyless CLI
All reads go through tsx examples/trace.ts. The data is large, so the rule is
progressive disclosure: start at the digest, descend only as far as the
question needs. Never dump a full transcript blind — slice it.
tsx examples/trace.ts commits # commit dirs that have traces
tsx examples/trace.ts list [--commit SHA] # runs for a commit (default HEAD): wall/cost/waitRatio/peak/top anomaly
tsx examples/trace.ts digest <runId> # the bottleneck digest — START HERE
tsx examples/trace.ts spans <runId> [--kind model|tool|io|agent] [--grep RE]
tsx examples/trace.ts transcript <runId> [--role R] [--seq A-B] [--step N] [--grep RE] [--head N] [--messages]
Output is JSON (transcript renders text); parse it, don't echo it at the user.
Step 0 — Resolve the commit and the runs
If the user gave a runId, use it. Otherwise default to the current commit
(git rev-parse --short HEAD) and list its runs; say which you used. If the
commit has no traces, say so and offer to produce one. With several runs of the
same question, treat them as repeated samples — timing varies run to run.
Step 1 — Read the digest (the headline)
tsx examples/trace.ts digest <runId>
Read off, in order:
criticalPath+criticalPathMs— the chain of spans that actually fills the wall clock. This is where time went. Each entry carries itssite.waitVsCompute—{ computeMs, waitMs, ratio }. ratio ≫ 1 means most model time was spent queued for the gate, not computing.concurrency—peakModelInFlightvsgateLimitModel. Peak pinned at the limit and high wait ⇒ gate-starved.anomalies— pre-ranked by severity; each has asiteand adetail.phaseBreakdown(keyed by site) andbyAgent— where wall / compute / cost / tokens concentrate.byAgentsplitsselfMs(own model time) fromsubtreeMs(incl. children).topByWait/topByLatency/topByCost— the worst individual model calls.attribution—site → src/file.ts:fn. This is your jump table to code.
Step 2 — Symptom → cause → source
| digest signal | likely cause | drill | fix lives in |
|---|---|---|---|
waitVsCompute.ratio high + peakModelInFlight == gateLimitModel | model-gate starvation — work oversubscribes the concurrency cap | spans --kind model, topByWait | src/config.ts (maxConcurrentModelCalls) + the fan-out flooding it |
high-wait anomalies clustered at one site | that phase queues behind everything else | spans --grep <site> | attribution[site] |
slow-step anomaly, low wait | model-bound: prompt too large or maxTokens too high | transcript --seq <n> --messages | attribution[site] |
redundant-call anomaly | identical fresh call issued >1× — duplicated spend | spans --grep <callKey8> | attribution[site] (missing memoization) |
tail-agent anomaly | one leaf agent is the long pole in a Promise.all | byAgent, transcript --role <agentId> | src/agent.ts / the spawn fan-out |
retry-storm anomaly | backoff from rate limits / 429s, not gate wait | spans --grep <site> | provider concurrency / src/config.ts |
large idleMs | serialization gap — an await that could overlap | criticalPath (look for idle jumps) | the awaiting call site |
one site dominates phaseBreakdown / topByCost | that phase is the spend driver | phaseBreakdown, transcript --role | attribution[site] |
Always resolve the file from the digest's own attribution map — don't guess the
filename from the site name.
Step 3 — Drill only where a hypothesis needs it
tsx examples/trace.ts spans <runId> --kind model --grep verify # one phase's calls: waitMs, computeMs, cost
tsx examples/trace.ts transcript <runId> # summary: steps per role
tsx examples/trace.ts transcript <runId> --role lead # the lead's reasoning + tool calls
tsx examples/trace.ts transcript <runId> --seq 8-10 --messages # byte-exact input for a step range (large)
Use the transcript to answer what the aggregates can't: why is this call's prompt so big? what is this agent re-reading every step? did synthesis get handed redundant context? Then open the attributed source and read it before proposing anything.
Step 4 — Synthesize: bottlenecks → fixes
Produce, for the user:
- Ranked bottlenecks — each tied to a digest signal (quote the number:
waitMs, share ofcriticalPathMs,costUSD), the root cause, and thesrc/file.ts:fnit lives in (fromattribution). - A concrete fix per bottleneck — read the file first, then cite
file:lineand the change. Separate config fixes (a knob inconfig.ts, low-risk) from structural fixes (a pipeline change — overlap awaits, memoize a call, trim a prompt, rebalance fan-out). - Cross-run patterns — if several runs on this commit show the same hot site, that's the highest-leverage fix; a site that's hot in one run but not others is variance, not a bug.
Step 5 — Close the loop (verify a fix)
After a fix is applied (by the user, or in a normal edit turn — not under this skill), confirm it moved the needle:
tsx examples/cli.ts "<same question>" --trace full --effort <same>
tsx examples/trace.ts digest <newRunId>
Diff the new digest against the old: did criticalPathMs / modelWaitMs /
costUSD drop, did the anomaly clear, and where did the bottleneck move next
(it usually moves — name the new long pole)? If src/ changed, confirm
npm run test:run is green. Report before/after numbers, not just "fixed".
Boundaries
- Read-only. This skill diagnoses and proposes; it does not edit
src/. Hand off each fix asfile:line+ the change. If asked to apply it, do so in a normal turn, not under this skill. - Progressive disclosure. Start at
digest; slicespans/transcript. Never dump a whole transcript to reach a conclusion. - Replayed work isn't a bottleneck. Replayed calls have zeroed timing and
status:"replayed"; the digest already excludes them from latency math — don't re-introduce them as if they cost wall-clock. - Honor variance. Timing shifts with network and provider load. One run is a hypothesis; if numbers swing across runs of the same question, say so rather than over-fitting a story to a single run.
- Ground every claim. Quote the digest field, the span's
waitMs/computeMs, the transcript line, or thefile:lineyou cite. If the trace can't decide, say what's missing (e.g. "this run was--trace spans; needfullto see the prompt"). - Mind cost. Producing a trace runs real research. Re-run to verify only when it earns the spend; prefer reusing the commit's existing traces.
File metadata
name: atlas-optimize description: Diagnose an atlas run's efficiency — find where wall-clock, model wait, and cost actually went, tie each bottleneck back to the src/*.ts function that owns it, and propose concrete performance fixes. Reads the bottleneck digest, timing spans, and byte-exact transcript captured by `--trace`. Use when the user asks to "analyze this commit's run traces", find why a run is slow or expensive, investigate a latency or cost regression, or optimize a research query. Read-only: it diagnoses and proposes file:line fixes; it does not edit code. user-invocable: true allowed-tools: Bash, Read, Grep, Glob
View original text
---
name: atlas-optimize
description:
Diagnose an atlas run's efficiency — find where wall-clock, model wait, and
cost actually went, tie each bottleneck back to the src/*.ts function that owns
it, and propose concrete performance fixes. Reads the bottleneck digest, timing
spans, and byte-exact transcript captured by `--trace`. Use when the user asks
to "analyze this commit's run traces", find why a run is slow or expensive,
investigate a latency or cost regression, or optimize a research query.
Read-only: it diagnoses and proposes file:line fixes; it does not edit code.
user-invocable: true
allowed-tools: Bash, Read, Grep, Glob
---
# atlas-optimize
The efficiency counterpart to `draco-analyze`. That skill asks *did the research
score well*; this one asks *where did the time and money go, and which function
should change*.
It reads the trace `--trace` captures for a run: per-call / per-agent / per-phase
**timing spans** (with a compute-vs-gate-wait split), an automatic **bottleneck
digest** (critical path, phase + per-agent rollups, concurrency peaks, ranked
anomalies), and the **byte-exact model transcript** — every step's system prompt,
input thread, thinking, and tool calls. Crucially the digest carries an
`attribution` map (`site → src/file.ts:fn`), so every hot span points straight at
the code that owns it. That map is the whole reason this is a skill and not an
SDK feature: a finding maps to a file you can open and read.
## Producing a trace
A run only emits a trace when started with `--trace`. Traces land under
`eval-runs/traces/<commit>/` (git-ignored), one pair of files per run:
`<runId>.digest.json` (small) and `<runId>.trace.json` (spans + transcript).
```bash
tsx examples/cli.ts "<question>" --trace full --effort balanced
```
- `--trace full` = spans + verbatim transcript. `--trace spans` = the lighter
tier (timing + digest, no verbatim I/O) — enough for bottleneck work, cheaper
to keep.
- If the user names a question to optimize and no trace exists, **offer** to run
this and proceed only on confirmation — it spends provider + Steel credits.
- If they say "analyze this commit's traces", read what's already on disk; don't
re-run.
## Reading traces — one keyless CLI
All reads go through `tsx examples/trace.ts`. The data is large, so the rule is
**progressive disclosure**: start at the digest, descend only as far as the
question needs. Never dump a full transcript blind — slice it.
```bash
tsx examples/trace.ts commits # commit dirs that have traces
tsx examples/trace.ts list [--commit SHA] # runs for a commit (default HEAD): wall/cost/waitRatio/peak/top anomaly
tsx examples/trace.ts digest <runId> # the bottleneck digest — START HERE
tsx examples/trace.ts spans <runId> [--kind model|tool|io|agent] [--grep RE]
tsx examples/trace.ts transcript <runId> [--role R] [--seq A-B] [--step N] [--grep RE] [--head N] [--messages]
```
Output is JSON (transcript renders text); parse it, don't echo it at the user.
## Step 0 — Resolve the commit and the runs
If the user gave a runId, use it. Otherwise default to the current commit
(`git rev-parse --short HEAD`) and `list` its runs; say which you used. If the
commit has no traces, say so and offer to produce one. With several runs of the
same question, treat them as repeated samples — timing varies run to run.
## Step 1 — Read the digest (the headline)
```bash
tsx examples/trace.ts digest <runId>
```
Read off, in order:
- `criticalPath` + `criticalPathMs` — the chain of spans that actually fills the
wall clock. **This is where time went.** Each entry carries its `site`.
- `waitVsCompute` — `{ computeMs, waitMs, ratio }`. ratio ≫ 1 means most model
time was spent **queued for the gate**, not computing.
- `concurrency` — `peakModelInFlight` vs `gateLimitModel`. Peak pinned at the
limit *and* high wait ⇒ gate-starved.
- `anomalies` — pre-ranked by severity; each has a `site` and a `detail`.
- `phaseBreakdown` (keyed by site) and `byAgent` — where wall / compute / cost /
tokens concentrate. `byAgent` splits `selfMs` (own model time) from
`subtreeMs` (incl. children).
- `topByWait` / `topByLatency` / `topByCost` — the worst individual model calls.
- `attribution` — `site → src/file.ts:fn`. **This is your jump table to code.**
## Step 2 — Symptom → cause → source
| digest signal | likely cause | drill | fix lives in |
| --- | --- | --- | --- |
| `waitVsCompute.ratio` high + `peakModelInFlight == gateLimitModel` | model-gate starvation — work oversubscribes the concurrency cap | `spans --kind model`, `topByWait` | `src/config.ts` (maxConcurrentModelCalls) + the fan-out flooding it |
| `high-wait` anomalies clustered at one `site` | that phase queues behind everything else | `spans --grep <site>` | `attribution[site]` |
| `slow-step` anomaly, low wait | model-bound: prompt too large or maxTokens too high | `transcript --seq <n> --messages` | `attribution[site]` |
| `redundant-call` anomaly | identical fresh call issued >1× — duplicated spend | `spans --grep <callKey8>` | `attribution[site]` (missing memoization) |
| `tail-agent` anomaly | one leaf agent is the long pole in a `Promise.all` | `byAgent`, `transcript --role <agentId>` | `src/agent.ts` / the spawn fan-out |
| `retry-storm` anomaly | backoff from rate limits / 429s, not gate wait | `spans --grep <site>` | provider concurrency / `src/config.ts` |
| large `idleMs` | serialization gap — an await that could overlap | `criticalPath` (look for idle jumps) | the awaiting call site |
| one `site` dominates `phaseBreakdown` / `topByCost` | that phase is the spend driver | `phaseBreakdown`, `transcript --role` | `attribution[site]` |
Always resolve the file from the digest's own `attribution` map — don't guess the
filename from the site name.
## Step 3 — Drill only where a hypothesis needs it
```bash
tsx examples/trace.ts spans <runId> --kind model --grep verify # one phase's calls: waitMs, computeMs, cost
tsx examples/trace.ts transcript <runId> # summary: steps per role
tsx examples/trace.ts transcript <runId> --role lead # the lead's reasoning + tool calls
tsx examples/trace.ts transcript <runId> --seq 8-10 --messages # byte-exact input for a step range (large)
```
Use the transcript to answer what the aggregates can't: *why is this call's
prompt so big? what is this agent re-reading every step? did synthesis get handed
redundant context?* Then open the attributed source and read it before proposing
anything.
## Step 4 — Synthesize: bottlenecks → fixes
Produce, for the user:
1. **Ranked bottlenecks** — each tied to a digest signal (quote the number:
`waitMs`, share of `criticalPathMs`, `costUSD`), the root cause, and the
`src/file.ts:fn` it lives in (from `attribution`).
2. **A concrete fix per bottleneck** — read the file first, then cite `file:line`
and the change. Separate *config* fixes (a knob in `config.ts`, low-risk) from
*structural* fixes (a pipeline change — overlap awaits, memoize a call, trim a
prompt, rebalance fan-out).
3. **Cross-run patterns** — if several runs on this commit show the same hot
site, that's the highest-leverage fix; a site that's hot in one run but not
others is variance, not a bug.
## Step 5 — Close the loop (verify a fix)
After a fix is applied (by the user, or in a normal edit turn — **not** under this
skill), confirm it moved the needle:
```bash
tsx examples/cli.ts "<same question>" --trace full --effort <same>
tsx examples/trace.ts digest <newRunId>
```
Diff the new digest against the old: did `criticalPathMs` / `modelWaitMs` /
`costUSD` drop, did the anomaly clear, and where did the bottleneck move next
(it usually moves — name the new long pole)? If `src/` changed, confirm
`npm run test:run` is green. Report before/after numbers, not just "fixed".
## Boundaries
- **Read-only.** This skill diagnoses and proposes; it does not edit `src/`. Hand
off each fix as `file:line` + the change. If asked to apply it, do so in a
normal turn, not under this skill.
- **Progressive disclosure.** Start at `digest`; slice `spans` / `transcript`.
Never dump a whole transcript to reach a conclusion.
- **Replayed work isn't a bottleneck.** Replayed calls have zeroed timing and
`status:"replayed"`; the digest already excludes them from latency math — don't
re-introduce them as if they cost wall-clock.
- **Honor variance.** Timing shifts with network and provider load. One run is a
hypothesis; if numbers swing across runs of the same question, say so rather
than over-fitting a story to a single run.
- **Ground every claim.** Quote the digest field, the span's `waitMs`/`computeMs`,
the transcript line, or the `file:line` you cite. If the trace can't decide, say
what's missing (e.g. "this run was `--trace spans`; need `full` to see the
prompt").
- **Mind cost.** Producing a trace runs real research. Re-run to verify only when
it earns the spend; prefer reusing the commit's existing traces.
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- MIT
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 20 GitHub stars
- Stars/forks activity: 20 stars, 1 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- steel-dev/atlas
- License
- MIT
- Version
- Unknown
- Last GitHub push
- Sep 17, 2026
- Registry updated
- Sep 21, 2026
- Instruction path
- .claude/skills/atlas-optimize/SKILL.md @ 7aea679b740a
Version reported in registry metadata; check source releases before relying on it.
Quality
54/100
Needs review
Trust
59/100
Do not auto-install
Audit
71/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 20 GitHub stars
- Stars/forks activity: 20 stars, 1 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"trust": {
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"label": "Manual review",
"version": "trust-score-v4",
"install_policy": "block",
"evidence": {
"stars": "20 GitHub stars",
"repoActivity": "20 stars, 1 forks",
"lastPushed": "18d since push",
"license": "MIT",
"repository": "https://github.com/steel-dev/atlas/tree/main/.claude/skills/atlas-optimize",
"install": "npx skills add steel-dev/atlas --skill atlas-optimize",
"installSafety": "standard package or runtime install path",
"permissionSurface": "secrets or environment access, shell or command execution",
"documentation": "Strong README/SKILL.md context",
"agentOutcomes": "No agent outcome data yet"
},
"outcome_evidence": {
"total": 0,
"successes": 0,
"failures": 0,
"not_relevant": 0,
"success_rate": null,
"recent_success_rate": null,
"recent_failure_rate": null,
"install_attempts": 0,
"install_success_rate": null,
"risk_blocked": 0,
"setup_required": 0,
"avg_output_quality": null,
"production_outcomes": 0,
"last_outcome_at": null,
"label": "No agent outcome data yet"
},
"auto_install": {
"allowed": false,
"sandbox_required": true,
"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"research",
"agent-skill"
],
"known_risks": [
"AI review approval is missing",
"Low GitHub adoption signal",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"GitHub adoption: 20 GitHub stars",
"Stars/forks activity: 20 stars, 1 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 71,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Low GitHub adoption signal",
"AI review approval is missing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"GitHub adoption: 20 GitHub stars",
"Stars/forks activity: 20 stars, 1 forks; issue activity unavailable in current metadata"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 54,
"label": "Needs review"
},
"supply": {
"track": "Research and knowledge work",
"scenario": "Research agents",
"maintenance": "18d since push",
"risk": "Needs review"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"Low GitHub adoption signal",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"AI review approval is missing",
"Quality score needs review"
],
"agent_contract": {
"task_input": "Use atlas-optimize in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 67/100 Manual review",
"Audit: 71/100 Needs review",
"Safety: 27/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "steel-dev-atlas-optimize (atlas-optimize)",
"install_command": "npx skills add steel-dev/atlas --skill atlas-optimize",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "steel-dev-atlas-optimize",
"task": "Use atlas-optimize in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/steel-dev-atlas-optimize",
"api": "https://www.openagentskill.com/api/agent/skills/steel-dev-atlas-optimize",
"audit": "https://www.openagentskill.com/skills/steel-dev-atlas-optimize/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=steel-dev-atlas-optimize&task=Use%20atlas-optimize%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20atlas-optimize%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20atlas-optimize%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/steel-dev-atlas-optimize/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/steel-dev-atlas-optimize"
}
}For the creator
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- steel-dev/atlas
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