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affaan-m/context-budget

affaan-m

context-budget

Routes live context checks to the native Codex or Claude Code meter without scanning files, and performs a bounded static component audit only when explicitly requested. Use for context-budget requests about remaining capacity or installed context overhead.

NewUpdated Oct 12, 2026

Context Budget

Use the provider's native meter for live session usage. Keep the separate, estimated installation audit behind an explicit opt-in.

Default: Live Context

Treat an invocation with no audit request as a live-context request. This includes requests for the current window, remaining capacity, headroom, token usage, or a context breakdown.

For this default path:

  • Do not call or use tools.
  • Do not scan or read files.
  • Do not run shell commands, provider CLIs, debug logging, or MCP discovery.
  • Reply with only the native command and one short explanation.

Route by the active harness:

Harness Native command Guidance
Codex CLI /status Shows current token usage and remaining context capacity. Use /statusline and enable Context stats plus Token counters for a persistent footer meter.
Codex app /status Shows current thread context usage and rate limits.
Claude Code /context Shows the live context breakdown. Use /context all for expanded items.
Other or unknown None verified State that ECC cannot read live provider state and ask which harness is active.

These commands are documented in the Codex command reference and Claude Code command reference.

A skill cannot invoke a TUI slash command on the user's behalf. Never replace the native command with an estimated scan and never infer an audit request from phrases such as “context is heavy” or “how much is left.”

Explicit Static Audit

Run an installation/configuration audit only when the user explicitly says --audit, “audit installed components,” or equivalent unambiguous wording. --verbose changes report detail; it does not opt into an audit by itself.

If the audit scope is ambiguous, ask for the harness and installation root before reading anything. Keep every audit bounded and read-only:

  1. Inspect only the named plugin/install root and the named harness's known configuration files.
  2. Never recursively enumerate the workspace root, home directory, provider cache, transcripts, debug logs, or unrelated worktrees.
  3. Never launch a provider CLI, MCP server, or debug session to count tools.
  4. Cap inventory output at 50 entries per component class. Summarize the rest.
  5. Do not write temporary logs or copy configuration content into the report.

Audit only the component metadata relevant to eager discovery:

  • agent and skill descriptions;
  • command descriptions when the harness discovers them eagerly;
  • always-loaded instruction files in the explicitly named scope;
  • configured MCP tool schemas when an existing bounded manifest exposes them.

Full skill, agent, command, and rule bodies are generally on-demand content. Report their disk size separately from estimated eager overhead instead of adding every body to the session baseline.

Audit Output

Return a compact report containing:

  • harness and exact paths inspected;
  • estimated eager/discovery overhead by component class;
  • installed counts and the heaviest metadata entries;
  • assumptions, unavailable measurements, and up to three optimizations.

Every calculated token number is an estimate of static configuration overhead, not live session context. Do not claim that an estimate is provider-reported, current, exact, or evidence of realized savings. Direct the user back to the native live-context command for current usage.

Optional Selected-Record Diagnostic

Inspect a persisted record only when the user explicitly selects that file for this diagnostic. This is separate from the default live-context request and the installation audit.

For a local file diagnostic, explicitly select a stable, regular JSONL file or a snapshot you intend to inspect. Replace the example path below; this does not find or reconnect a session. The snippet prints aggregate byte counts only. It reads one line at a time, so memory use depends on the largest record; avoid very large records and actively growing files.

python3 - "/path/to/selected-session.jsonl" <<'EOF'
import json
import sys

total = attachment = other = unclassified = 0
try:
    with open(sys.argv[1], "rb") as source:
        for raw in source:
            size = len(raw)
            total += size
            try:
                record = json.loads(raw.decode("utf-8"))
            except (UnicodeDecodeError, json.JSONDecodeError):
                unclassified += size
                continue
            record_type = record.get("type") if isinstance(record, dict) else None
            if not isinstance(record_type, str):
                unclassified += size
            elif record_type == "attachment":
                attachment += size
            else:
                other += size
except OSError:
    print("Cannot read selected JSONL file.", file=sys.stderr)
    raise SystemExit(1)

pct = attachment * 100.0 / total if total else 0.0
print(f"persisted {total}B | attachment records {attachment}B ({pct:.1f}%) | "
      f"other records {other}B | unclassified {unclassified}B")
EOF

The three categories add up to the original file bytes, including line endings and blank lines. attachment is an exact record-type filter, not a guarantee about a harness's current internal schema. Other records have a different string type; malformed JSON, invalid UTF-8, nonobject values, missing or non-string types, and blank lines are unclassified. Neither category means "conversation," and the percentage is only a share of persisted bytes.

These counts do not establish active context, remaining room, token usage, billing, or what a reconnect loads. For current harness-reported context and usage, use the version-appropriate /context and /usage commands (/cost is an alias).

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Overall Score

82/100

Grade

B

Good

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

85

Quality

82

Clarity

86

Completeness

75

Summary

This skill routes context-budget queries to native provider meters (Codex `/status` or Claude Code `/context`) without file scanning, and optionally performs bounded static audits of installed components only when explicitly requested via `--audit`. It prevents accidental file enumeration and directs users to authoritative provider commands for live context reporting.

Detected Capabilities

read-only file inspectionbounded configuration auditjson parsing and analysisprovider command routingrecord byte counting

Trigger Keywords

Phrases that agents use to match this skill to user intent.

check context capacityview token usagecontext budget remainingaudit installed overheadcodex statusclaude code contextinspect session records

Risk Signals

INFO

Python script for JSONL parsing uses sys.argv directly without path validation

SKILL.md:Optional Selected-Record Diagnostic
WARNING

User-supplied file path passed to Python script without validation or sandboxing

SKILL.md:Optional Selected-Record Diagnostic, /path/to/selected-session.jsonl
WARNING

Python script reads until EOF without size/line limits on unvalidated file paths

SKILL.md:Optional Selected-Record Diagnostic, EOF block

Referenced Domains

External domains referenced in skill content, detected by static analysis.

code.claude.comdevelopers.openai.com

Use Cases

  • Check remaining context capacity in live session
  • View current token usage and context breakdown
  • Audit installation overhead of plugins and agents
  • Diagnose installed component metadata size
  • Inspect persisted session records for byte counts
  • Compare estimated configuration overhead vs. live usage

Quality Notes

  • Excellent explicit scope boundaries: audit is strictly opt-in behind `--audit` flag; default behavior is read-only routing only
  • Well-documented limitations: clearly states what NOT to do (no file enumeration, no CLI invocation, no recursive scanning)
  • Clear decision table mapping harness types to native commands, with citations to official documentation
  • Bounded audit design: explicit caps (50 entries per component class), read-only operations, no temp file writes
  • Python diagnostic script is minimal and memory-safe for reasonable JSONL files but lacks path sanitization
  • Instructions appropriately disclaim token estimates as static overhead, not live usage or billing
  • Helpful optional diagnostic acknowledges active file growth risk and advises against very large files
  • Edge case handling: gracefully handles ambiguous audit requests by asking clarifying questions first
Model: claude-haiku-4-5-20251001Analyzed: Oct 12, 2026

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Version History

  1. v3.0

    Contract changed: description

    ✦ AIRoutes live context requests to native provider commands without scanning, moves file audit behind explicit --audit flag

    triggering2026-10-12

    LATEST
  2. v2.1

    Content updated

    ✦ AIAdds diagnostic snippet to measure persisted-record byte composition in JSONL session files.

    2026-09-30

    View This Version
  3. v2.0

    Contract changed: description

    ✦ AIExpands activation guidance to clarify use when context window fills too fast.

    triggering2026-09-09

    View This Version
  4. v1.2

    Content updated

    ✦ AISafety grade changed from A to B.

    2026-07-14

    View This Version
  5. v1.1

    Content updated

    ✦ AIAdds LICENSE file; content otherwise unchanged.

    2026-04-20

    View This Version
  6. v1.0

    2026-04-12

    View This VersionInitial version

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